



from typing import List

from packaging import version
from sklearn.metrics import fa_score

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse("""3.8"""):
      import importlib_metadata
else:
      import importlib.metadata as importlib_metadata


# TODO(Patrick/Anton)
_lowerCamelCase         =	"""\
"""

_lowerCamelCase         =	"""\
    XTREME-S is a benchmark to evaluate universal cross-lingual speech representations in many languages.
    XTREME-S covers four task families: speech recognition, classification, speech-to-text translation and retrieval.
"""

_lowerCamelCase         =	"""
Compute XTREME-S evaluation metric associated to each XTREME-S dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    bleu_kwargs: optional dict of keywords to be passed when computing 'bleu'.
        Keywords include Dict can be one of 'smooth_method', 'smooth_value', 'force', 'lowercase',
        'tokenize', 'use_effective_order'.
    wer_kwargs: optional dict of keywords to be passed when computing 'wer' and 'cer'.
        Keywords include 'concatenate_texts'.
Returns: depending on the XTREME-S task, one or several of:
    \"accuracy\": Accuracy - for 'fleurs-lang_id', 'minds14'
    \"f1\": F1 score - for 'minds14'
    \"wer\": Word error rate - for 'mls', 'fleurs-asr', 'voxpopuli', 'babel'
    \"cer\": Character error rate - for 'mls', 'fleurs-asr', 'voxpopuli', 'babel'
    \"bleu\": BLEU score according to the `sacrebleu` metric - for 'covost2'
Examples:

    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'mls')  # 'mls', 'voxpopuli', 'fleurs-asr' or 'babel'
    >>> references = [\"it is sunny here\", \"paper and pen are essentials\"]
    >>> predictions = [\"it's sunny\", \"paper pen are essential\"]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {'wer': 0.56, 'cer': 0.27}

    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'covost2')
    >>> references = [\"bonjour paris\", \"il est necessaire de faire du sport de temps en temp\"]
    >>> predictions = [\"bonjour paris\", \"il est important de faire du sport souvent\"]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {'bleu': 31.65}

    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'fleurs-lang_id')
    >>> references = [0, 1, 0, 0, 1]
    >>> predictions = [0, 1, 1, 0, 0]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {'accuracy': 0.6}

    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'minds14')
    >>> references = [0, 1, 0, 0, 1]
    >>> predictions = [0, 1, 1, 0, 0]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {'f1': 0.58, 'accuracy': 0.6}
"""

_lowerCamelCase         =	["""fleurs-asr""", """mls""", """voxpopuli""", """babel""", """covost2""", """fleurs-lang_id""", """minds14"""]
_lowerCamelCase         =	""""""

try:
      from jiwer import transforms as tr

      _lowerCamelCase         =	True
except ImportError:
      _lowerCamelCase         =	False

if _jiwer_available and version.parse(importlib_metadata.version("""jiwer""")) < version.parse("""2.3.0"""):

      class    _SCREAMING_SNAKE_CASE						(tr.AbstractTransform			):





           def __init__(							self			:				Tuple  ,	UpperCamelCase			:				str = " "					)->List[str]:
            __SCREAMING_SNAKE_CASE							:     List[Any]							=						sentence_delimiter





           def 						__snake_case     (							self			:				Union[str, Any]  ,	UpperCamelCase			:				str					)->Any:
            return list(UpperCamelCase					)






           def 						__snake_case     (							self			:				str  ,	UpperCamelCase			:				List[str]					)->Tuple:
            __SCREAMING_SNAKE_CASE							:     int							=						[]
            for sent_idx, sentence in enumerate(UpperCamelCase					):
             chars.extend(self.process_string(UpperCamelCase					)					)
             if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(UpperCamelCase					) - 1:
              chars.append(self.sentence_delimiter					)
            return chars






      _lowerCamelCase         =	tr.Compose(
          [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
      )
elif _jiwer_available:
      _lowerCamelCase         =	tr.Compose(
          [
              tr.RemoveMultipleSpaces(),
              tr.Strip(),
              tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
              tr.ReduceToListOfListOfChars(),
          ]
      )
else:
      _lowerCamelCase         =	None






def 						_lowerCAmelCase    (					__lowerCamelCase :  Optional[Any]		,						__lowerCamelCase :  Union[str, Any]  ):



 """simple docstring"""





 return float((preds == labels).mean()  )






def 						_lowerCAmelCase    (					__lowerCamelCase :  Dict		,						__lowerCamelCase :  str  ):



 """simple docstring"""





 return {
     "f1": float(fa_score(y_true=__lowerCamelCase		,						y_pred=__lowerCamelCase		,						average="macro"  )  ),
     "accuracy": simple_accuracy(__lowerCamelCase		,						__lowerCamelCase  ),
 }






def 						_lowerCAmelCase    (					__lowerCamelCase :  str		,						__lowerCamelCase :  List[str]		,						__lowerCamelCase :  Optional[int]="exp"		,						__lowerCamelCase :  Optional[Any]=None		,						__lowerCamelCase :  Tuple=False		,						__lowerCamelCase :  Dict=False		,						__lowerCamelCase :  Dict=None		,						__lowerCamelCase :  Optional[Any]=False		,						):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Tuple							=						[[label] for label in labels]
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						list(__lowerCamelCase  )
 try:
  import sacrebleu as scb
 except ImportError:
  raise ValueError(
      "sacrebleu has to be installed in order to apply the bleu metric for covost2."
      "You can install it via `pip install sacrebleu`."  )

 if version.parse(scb.__version__  ) < version.parse("1.4.12"  ):
  raise ImportWarning(
      "To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"
      "You can install it with `pip install \"sacrebleu>=1.4.12\"`."  )

 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						len(labels[0]  )
 if any(len(__lowerCamelCase  ) != references_per_prediction for refs in labels  ):
  raise ValueError("Sacrebleu requires the same number of references for each prediction"  )
 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						[[refs[i] for refs in labels] for i in range(__lowerCamelCase  )]
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						scb.corpus_bleu(
     __lowerCamelCase		,						__lowerCamelCase		,						smooth_method=__lowerCamelCase		,						smooth_value=__lowerCamelCase		,						force=__lowerCamelCase		,						lowercase=__lowerCamelCase		,						use_effective_order=__lowerCamelCase		,						**({"tokenize": tokenize} if tokenize else {})		,						)
 return {"bleu": output.score}






def 						_lowerCAmelCase    (					__lowerCamelCase :  List[str]		,						__lowerCamelCase :  List[str]		,						__lowerCamelCase :  Union[str, Any]		,						__lowerCamelCase :  int  ):



 """simple docstring"""





 try:
  from jiwer import compute_measures
 except ImportError:
  raise ValueError(
      F"""jiwer has to be installed in order to apply the wer metric for {config_name}."""
      "You can install it via `pip install jiwer`."  )

 if concatenate_texts:
  __SCREAMING_SNAKE_CASE							:     Dict							=						compute_measures(__lowerCamelCase		,						__lowerCamelCase  )["wer"]

  __SCREAMING_SNAKE_CASE							:     List[Any]							=						compute_measures(__lowerCamelCase		,						__lowerCamelCase		,						truth_transform=__lowerCamelCase		,						hypothesis_transform=__lowerCamelCase  )["wer"]
  return {"wer": wer, "cer": cer}
 else:

  def compute_score(__lowerCamelCase :  Any		,						__lowerCamelCase :  Optional[int]		,						__lowerCamelCase :  List[Any]="wer"  ):
   __SCREAMING_SNAKE_CASE							:     List[Any]							=						0
   __SCREAMING_SNAKE_CASE							:     Optional[int]							=						0
   for prediction, reference in zip(__lowerCamelCase		,						__lowerCamelCase  ):
    if score_type == "wer":
     __SCREAMING_SNAKE_CASE							:     List[str]							=						compute_measures(__lowerCamelCase		,						__lowerCamelCase  )
    elif score_type == "cer":
     __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						compute_measures(
         __lowerCamelCase		,						__lowerCamelCase		,						truth_transform=__lowerCamelCase		,						hypothesis_transform=__lowerCamelCase  )
    incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
    total += measures["substitutions"] + measures["deletions"] + measures["hits"]
   return incorrect / total

  return {"wer": compute_score(__lowerCamelCase		,						__lowerCamelCase		,						"wer"  ), "cer": compute_score(__lowerCamelCase		,						__lowerCamelCase		,						"cer"  )}






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,     _KWARGS_DESCRIPTION			)
class    _SCREAMING_SNAKE_CASE						(datasets.Metric			):





     def 						__snake_case     (							self			:				Optional[Any]					)->Tuple:
      if self.config_name not in _CONFIG_NAMES:
       raise KeyError(F"""You should supply a configuration name selected in {_CONFIG_NAMES}"""					)

      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						"int64" if self.config_name in ["fleurs-lang_id", "minds14"] else "string"

      return datasets.MetricInfo(
          description=_DESCRIPTION  ,	citation=_CITATION  ,	inputs_description=_KWARGS_DESCRIPTION  ,	features=datasets.Features(
              {"predictions": datasets.Value(UpperCamelCase					), "references": datasets.Value(UpperCamelCase					)}					)  ,	codebase_urls=[]  ,	reference_urls=[]  ,	format="numpy"  ,	)






     def 						__snake_case     (							self			:				List[Any]  ,	UpperCamelCase			:				Union[str, Any]  ,	UpperCamelCase			:				List[str]  ,	UpperCamelCase			:				Union[str, Any]=None  ,	UpperCamelCase			:				Dict=None					)->Optional[int]:
      __SCREAMING_SNAKE_CASE							:     str							=						bleu_kwargs if bleu_kwargs is not None else {}
      __SCREAMING_SNAKE_CASE							:     Any							=						wer_kwargs if wer_kwargs is not None else {}

      if self.config_name == "fleurs-lang_id":
       return {"accuracy": simple_accuracy(UpperCamelCase  ,	UpperCamelCase					)}
      elif self.config_name == "minds14":
       return fa_and_simple_accuracy(UpperCamelCase  ,	UpperCamelCase					)
      elif self.config_name == "covost2":
       __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						bleu_kwargs.pop("smooth_method"  ,	"exp"					)
       __SCREAMING_SNAKE_CASE							:     List[str]							=						bleu_kwargs.pop("smooth_value"  ,	UpperCamelCase					)
       __SCREAMING_SNAKE_CASE							:     str							=						bleu_kwargs.pop("force"  ,	UpperCamelCase					)
       __SCREAMING_SNAKE_CASE							:     Tuple							=						bleu_kwargs.pop("lowercase"  ,	UpperCamelCase					)
       __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						bleu_kwargs.pop("tokenize"  ,	UpperCamelCase					)
       __SCREAMING_SNAKE_CASE							:     Dict							=						bleu_kwargs.pop("use_effective_order"  ,	UpperCamelCase					)
       return bleu(
           preds=UpperCamelCase  ,	labels=UpperCamelCase  ,	smooth_method=UpperCamelCase  ,	smooth_value=UpperCamelCase  ,	force=UpperCamelCase  ,	lowercase=UpperCamelCase  ,	tokenize=UpperCamelCase  ,	use_effective_order=UpperCamelCase  ,	)
      elif self.config_name in ["fleurs-asr", "mls", "voxpopuli", "babel"]:
       __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						wer_kwargs.pop("concatenate_texts"  ,	UpperCamelCase					)
       return wer_and_cer(UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	self.config_name					)
      else:
       raise KeyError(F"""You should supply a configuration name selected in {_CONFIG_NAMES}"""					)











import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_lowerCamelCase         =	logging.get_logger(__name__)

_lowerCamelCase         =	{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """mask_emb""": """masked_spec_embed""",
}






def 						_lowerCAmelCase    (					__lowerCamelCase :  Union[str, Any]		,						__lowerCamelCase :  Optional[int]		,						__lowerCamelCase :  List[Any]		,						__lowerCamelCase :  str		,						__lowerCamelCase :  Tuple  ):



 """simple docstring"""





 for attribute in key.split("."  ):
  __SCREAMING_SNAKE_CASE							:     Any							=						getattr(__lowerCamelCase		,						__lowerCamelCase  )

 if weight_type is not None:
  __SCREAMING_SNAKE_CASE							:     List[Any]							=						getattr(__lowerCamelCase		,						__lowerCamelCase  ).shape
 else:
  __SCREAMING_SNAKE_CASE							:     Dict							=						hf_pointer.shape

 assert hf_shape == value.shape, (
     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":
  __SCREAMING_SNAKE_CASE							:     str							=						value
 elif weight_type == "weight_g":
  __SCREAMING_SNAKE_CASE							:     str							=						value
 elif weight_type == "weight_v":
  __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						value
 elif weight_type == "bias":
  __SCREAMING_SNAKE_CASE							:     int							=						value
 else:
  __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						value

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






def 						_lowerCAmelCase    (					__lowerCamelCase :  int		,						__lowerCamelCase :  Tuple  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Dict							=						[]
 __SCREAMING_SNAKE_CASE							:     str							=						fairseq_model.state_dict()

 __SCREAMING_SNAKE_CASE							:     List[Any]							=						hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  __SCREAMING_SNAKE_CASE							:     str							=						False
  if "conv_layers" in name:
   load_conv_layer(
       __lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase		,						hf_model.config.feat_extract_norm == "group"		,						)
   __SCREAMING_SNAKE_CASE							:     Optional[int]							=						True
  else:
   for key, mapped_key in MAPPING.items():
    __SCREAMING_SNAKE_CASE							:     List[str]							=						mapped_key

    if key in name:
     __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						True
     if "*" in mapped_key:
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						name.split(__lowerCamelCase  )[0].split("."  )[-2]
      __SCREAMING_SNAKE_CASE							:     int							=						mapped_key.replace("*"		,						__lowerCamelCase  )
     if "weight_g" in name:
      __SCREAMING_SNAKE_CASE							:     int							=						"weight_g"
     elif "weight_v" in name:
      __SCREAMING_SNAKE_CASE							:     str							=						"weight_v"
     elif "weight" in name:
      __SCREAMING_SNAKE_CASE							:     Tuple							=						"weight"
     elif "bias" in name:
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						"bias"
     else:
      __SCREAMING_SNAKE_CASE							:     int							=						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 						_lowerCAmelCase    (					__lowerCamelCase :  List[Any]		,						__lowerCamelCase :  Optional[Any]		,						__lowerCamelCase :  str		,						__lowerCamelCase :  str		,						__lowerCamelCase :  Union[str, Any]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     int							=						full_name.split("conv_layers."  )[-1]
 __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						name.split("."  )
 __SCREAMING_SNAKE_CASE							:     Dict							=						int(items[0]  )
 __SCREAMING_SNAKE_CASE							:     Dict							=						int(items[1]  )

 if type_id == 0:
  if "bias" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
       F"""{full_name} has size {value.shape}, but"""
       F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
   )
   __SCREAMING_SNAKE_CASE							:     Dict							=						value
   logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""  )
  elif "weight" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
       F"""{full_name} has size {value.shape}, but"""
       F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
   )
   __SCREAMING_SNAKE_CASE							:     int							=						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:
   assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
       F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
       " found."
   )
   __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						value
   logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""  )
  elif "weight" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
       F"""{full_name} has size {value.shape}, but"""
       F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
   )
   __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						value
   logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""  )
 else:
  unused_weights.append(__lowerCamelCase  )






def 						_lowerCAmelCase    (					__lowerCamelCase :  List[Any]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						HubertConfig()
 __SCREAMING_SNAKE_CASE							:     List[str]							=						model.config

 __SCREAMING_SNAKE_CASE							:     str							=						fs_config.activation_dropout
 __SCREAMING_SNAKE_CASE							:     Any							=						False
 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						fs_config.attention_dropout
 __SCREAMING_SNAKE_CASE							:     List[str]							=						False
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						eval(fs_config.extractor_conv_feature_layers  )
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						[x[0] for x in conv_layers]
 __SCREAMING_SNAKE_CASE							:     Any							=						[x[1] for x in conv_layers]
 __SCREAMING_SNAKE_CASE							:     Dict							=						[x[2] for x in conv_layers]
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						"gelu"
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						"layer" if fs_config.extractor_mode == "layer_norm" else "group"
 __SCREAMING_SNAKE_CASE							:     Any							=						False
 __SCREAMING_SNAKE_CASE							:     Tuple							=						0.0
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						0.0
 __SCREAMING_SNAKE_CASE							:     Any							=						fs_config.activation_fn
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						fs_config.dropout
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						fs_config.encoder_embed_dim
 __SCREAMING_SNAKE_CASE							:     int							=						0.02
 __SCREAMING_SNAKE_CASE							:     Tuple							=						fs_config.encoder_ffn_embed_dim
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						1E-5
 __SCREAMING_SNAKE_CASE							:     Any							=						0.0
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						fs_config.encoder_attention_heads
 __SCREAMING_SNAKE_CASE							:     Dict							=						fs_config.conv_pos_groups
 __SCREAMING_SNAKE_CASE							:     Tuple							=						fs_config.conv_pos
 __SCREAMING_SNAKE_CASE							:     Tuple							=						len(__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						fs_config.encoder_layers

 return config






@torch.no_grad()
def 						_lowerCAmelCase    (					__lowerCamelCase :  Any		,						__lowerCamelCase :  Optional[Any]=None  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Dict							=						distilhubert().model.model

 if config_path is not None:
  __SCREAMING_SNAKE_CASE							:     List[Any]							=						HubertConfig.from_pretrained(__lowerCamelCase  )
 else:
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						convert_config(__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						model.eval()

 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						WavaVecaFeatureExtractor(
     feature_size=1		,						sampling_rate=16000		,						padding_value=0		,						do_normalize=__lowerCamelCase		,						return_attention_mask=__lowerCamelCase		,						)
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						HubertModel(__lowerCamelCase  )

 recursively_load_weights(__lowerCamelCase		,						__lowerCamelCase  )

 feature_extractor.save_pretrained(__lowerCamelCase  )
 hf_model.save_pretrained(__lowerCamelCase  )


if __name__ == "__main__":
      _lowerCamelCase         =	argparse.ArgumentParser()
      parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
      parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
      _lowerCamelCase         =	parser.parse_args()
      convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)






