
"""simple docstring"""
import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
lowerCamelCase_      :			Optional[int]					=							logging.get_logger(__name__)
def 				a__   (   a   :				List[str]       ,     a   :				Dict       ,     a   :				List[str]		):

  """simple docstring"""







  _snake_case     :						Tuple						=   WavaVecaForSequenceClassification.from_pretrained(a       ,     config=a		)
  _snake_case     :						Tuple						=   downstream_dict["projector.weight"]
  _snake_case     :						int						=   downstream_dict["projector.bias"]
  _snake_case     :						int						=   downstream_dict["model.post_net.linear.weight"]
  _snake_case     :						Union[str, Any]						=   downstream_dict["model.post_net.linear.bias"]
  return model
def 				a__   (   a   :				int       ,     a   :				str       ,     a   :				Tuple		):

  """simple docstring"""







  _snake_case     :						Any						=   WavaVecaForAudioFrameClassification.from_pretrained(a       ,     config=a		)
  _snake_case     :						int						=   downstream_dict["model.linear.weight"]
  _snake_case     :						List[str]						=   downstream_dict["model.linear.bias"]
  return model
def 				a__   (   a   :				str       ,     a   :				Union[str, Any]       ,     a   :				Dict		):

  """simple docstring"""







  _snake_case     :						Any						=   WavaVecaForXVector.from_pretrained(a       ,     config=a		)
  _snake_case     :						int						=   downstream_dict["connector.weight"]
  _snake_case     :						Dict						=   downstream_dict["connector.bias"]
  for i, kernel_size in enumerate(hf_config.tdnn_kernel		):
    _snake_case     :						Optional[Any]						=   downstream_dict[
        f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
    ]
    _snake_case     :						Union[str, Any]						=   downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

  _snake_case     :						List[Any]						=   downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
  _snake_case     :						List[Any]						=   downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
  _snake_case     :						List[Any]						=   downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
  _snake_case     :						Dict						=   downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
  _snake_case     :						Dict						=   downstream_dict["objective.W"]
  return model
@torch.no_grad()
def 				a__   (   a   :				List[Any]       ,     a   :				List[Any]       ,     a   :				List[str]       ,     a   :				int		):

  """simple docstring"""







  _snake_case     :						Union[str, Any]						=   torch.load(a       ,     map_location="cpu"		)

  _snake_case     :						List[str]						=   checkpoint["Downstream"]

  _snake_case     :						Any						=   WavaVecaConfig.from_pretrained(a		)
  _snake_case     :						Dict						=   WavaVecaFeatureExtractor.from_pretrained(
      a       ,     return_attention_mask=a       ,     do_normalize=a		)

  _snake_case     :						Optional[Any]						=   hf_config.architectures[0]
  if arch.endswith("ForSequenceClassification"		):
    _snake_case     :						Any						=   convert_classification(a       ,     a       ,     a		)
  elif arch.endswith("ForAudioFrameClassification"		):
    _snake_case     :						List[Any]						=   convert_diarization(a       ,     a       ,     a		)
  elif arch.endswith("ForXVector"		):
    _snake_case     :						str						=   convert_xvector(a       ,     a       ,     a		)
  else:
    raise NotImplementedError(f'S3PRL weights conversion is not supported for {arch}'		)

  if hf_config.use_weighted_layer_sum:
    _snake_case     :						str						=   checkpoint["Featurizer"]["weights"]

  hf_feature_extractor.save_pretrained(a		)
  hf_model.save_pretrained(a		)


if __name__ == "__main__":
    lowerCamelCase_      :			List[str]					=							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.""")
    lowerCamelCase_      :			int					=							parser.parse_args()
    convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



"""simple docstring"""
def 				a__   (   a   :				int		):

  """simple docstring"""







  if not isinstance(a       ,     a		):
    raise TypeError("Input value must be an 'int' type"		)

  _snake_case     :						Union[str, Any]						=   0
  while number:
    position += 1
    number >>= 1

  return position


if __name__ == "__main__":
    import doctest

    doctest.testmod()

