code: 
  
  
  
  
  """simple docstring"""
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_sentencepiece_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  SCREAMING_SNAKE_CASE       = {"configuration_t5": ["T5_PRETRAINED_CONFIG_ARCHIVE_MAP", "T5Config", "T5OnnxConfig"]}
  
  try:
        if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        SCREAMING_SNAKE_CASE       = ["T5Tokenizer"]
  
  try:
        if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        SCREAMING_SNAKE_CASE       = ["T5TokenizerFast"]
  
  try:
        if not is_torch_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        SCREAMING_SNAKE_CASE       = [
            "T5_PRETRAINED_MODEL_ARCHIVE_LIST",
            "T5EncoderModel",
            "T5ForConditionalGeneration",
            "T5Model",
            "T5PreTrainedModel",
            "load_tf_weights_in_t5",
            "T5ForQuestionAnswering",
        ]
  
  try:
        if not is_tf_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        SCREAMING_SNAKE_CASE       = [
            "TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST",
            "TFT5EncoderModel",
            "TFT5ForConditionalGeneration",
            "TFT5Model",
            "TFT5PreTrainedModel",
        ]
  
  try:
        if not is_flax_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        SCREAMING_SNAKE_CASE       = [
            "FlaxT5EncoderModel",
            "FlaxT5ForConditionalGeneration",
            "FlaxT5Model",
            "FlaxT5PreTrainedModel",
        ]
  
  
  if TYPE_CHECKING:
        from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig
  
        try:
              if not is_sentencepiece_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .tokenization_ta import TaTokenizer
  
        try:
              if not is_tokenizers_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .tokenization_ta_fast import TaTokenizerFast
  
        try:
              if not is_torch_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .modeling_ta import (
                  T5_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TaEncoderModel,
                  TaForConditionalGeneration,
                  TaForQuestionAnswering,
                  TaModel,
                  TaPreTrainedModel,
                  load_tf_weights_in_ta,
              )
  
        try:
              if not is_tf_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .modeling_tf_ta import (
                  TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFTaEncoderModel,
                  TFTaForConditionalGeneration,
                  TFTaModel,
                  TFTaPreTrainedModel,
              )
  
        try:
              if not is_flax_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .modeling_flax_ta import (
                  FlaxTaEncoderModel,
                  FlaxTaForConditionalGeneration,
                  FlaxTaModel,
                  FlaxTaPreTrainedModel,
              )
  
  
  else:
        import sys
  
        SCREAMING_SNAKE_CASE       = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  import argparse
  
  import torch
  
  from transformers import (
      WavaVecaFeatureExtractor,
      WavLMConfig,
      WavLMForAudioFrameClassification,
      WavLMForSequenceClassification,
      WavLMForXVector,
      logging,
  )
  
  
  logging.set_verbosity_info()
  SCREAMING_SNAKE_CASE       = logging.get_logger(__name__)
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	int:
   A__ =       WavLMForSequenceClassification.from_pretrained(lowercase_    ,				config=lowercase_		)
   A__ =       downstream_dict["projector.weight"]
   A__ =       downstream_dict["projector.bias"]
   A__ =       downstream_dict["model.post_net.linear.weight"]
   A__ =       downstream_dict["model.post_net.linear.bias"]
   return model
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	Optional[Any]:
   A__ =       WavLMForAudioFrameClassification.from_pretrained(lowercase_    ,				config=lowercase_		)
   A__ =       downstream_dict["model.linear.weight"]
   A__ =       downstream_dict["model.linear.bias"]
   return model
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	int:
   A__ =       WavLMForXVector.from_pretrained(lowercase_    ,				config=lowercase_		)
   A__ =       downstream_dict["connector.weight"]
   A__ =       downstream_dict["connector.bias"]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel		):
    A__ =       downstream_dict[
        f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
    ]
    A__ =       downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]
  
   A__ =       downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
   A__ =       downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
   A__ =       downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
   A__ =       downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
   A__ =       downstream_dict["objective.W"]
   return model
  
  
  
  
  
  
  
  @torch.no_grad()
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_		)		->	Union[str, Any]:
   A__ =       torch.load(lowercase_    ,				map_location="cpu"		)
  
   A__ =       checkpoint["Downstream"]
  
   A__ =       WavLMConfig.from_pretrained(lowercase_		)
   A__ =       WavaVecaFeatureExtractor.from_pretrained(
       lowercase_    ,				return_attention_mask=lowercase_    ,				do_normalize=lowercase_		)
  
   A__ =       hf_config.architectures[0]
   if arch.endswith("ForSequenceClassification"		):
    A__ =       convert_classification(lowercase_    ,				lowercase_    ,				lowercase_		)
   elif arch.endswith("ForAudioFrameClassification"		):
    A__ =       convert_diarization(lowercase_    ,				lowercase_    ,				lowercase_		)
   elif arch.endswith("ForXVector"		):
    A__ =       convert_xvector(lowercase_    ,				lowercase_    ,				lowercase_		)
   else:
    raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""		)
  
   if hf_config.use_weighted_layer_sum:
    A__ =       checkpoint["Featurizer"]["weights"]
  
   hf_feature_extractor.save_pretrained(lowercase_		)
   hf_model.save_pretrained(lowercase_		)
  
  
  if __name__ == "__main__":
        SCREAMING_SNAKE_CASE       = argparse.ArgumentParser()
        parser.add_argument(
            "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
        )
        parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
        parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
        parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
        SCREAMING_SNAKE_CASE       = parser.parse_args()
        convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
  
  
  
  
  
  