


'''simple docstring'''







import math
from timeit import timeit







def _UpperCamelCase			(	__A       )				->						int:


      '''simple docstring'''



      UpperCamelCase__      =							0
      UpperCamelCase__      =							abs(__A       )
      while True:
            UpperCamelCase__      =							n // 10
            digits += 1
            if n == 0:
                  break
      return digits







def _UpperCamelCase			(	__A       )				->						int:


      '''simple docstring'''



      return 1 if n == 0 else math.floor(math.log(abs(__A       ) ,     10       ) + 1       )







def _UpperCamelCase			(	__A       )				->						int:


      '''simple docstring'''



      return len(str(abs(__A       )       )       )







def _UpperCamelCase			(	)				->						None:


      '''simple docstring'''



      from collections.abc import Callable

      def benchmark_a_function(__A ,     __A       ) -> None:
            UpperCamelCase__      =							F'''{func.__name__}({value})'''
            UpperCamelCase__      =							timeit(F'''__main__.{call}''' ,     setup="import __main__"       )
            print(F'''{call}: {func(__A       )} -- {timing} seconds'''       )

      for value in (262144, 1125899906842624, 1267650600228229401496703205376):
            for func in (num_digits, num_digits_fast, num_digits_faster):
                  benchmark_a_function(__A ,     __A       )
            print()


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  benchmark()









'''simple docstring'''







import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
a__    :   Optional[Any]								=			logging.get_logger(__name__)







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						List[Any]:


      '''simple docstring'''



      UpperCamelCase__      =							WavLMForSequenceClassification.from_pretrained(__A ,     config=__A       )
      UpperCamelCase__      =							downstream_dict["projector.weight"]
      UpperCamelCase__      =							downstream_dict["projector.bias"]
      UpperCamelCase__      =							downstream_dict["model.post_net.linear.weight"]
      UpperCamelCase__      =							downstream_dict["model.post_net.linear.bias"]
      return model







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						List[str]:


      '''simple docstring'''



      UpperCamelCase__      =							WavLMForAudioFrameClassification.from_pretrained(__A ,     config=__A       )
      UpperCamelCase__      =							downstream_dict["model.linear.weight"]
      UpperCamelCase__      =							downstream_dict["model.linear.bias"]
      return model







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						List[Any]:


      '''simple docstring'''



      UpperCamelCase__      =							WavLMForXVector.from_pretrained(__A ,     config=__A       )
      UpperCamelCase__      =							downstream_dict["connector.weight"]
      UpperCamelCase__      =							downstream_dict["connector.bias"]
      for i, kernel_size in enumerate(hf_config.tdnn_kernel       ):
            UpperCamelCase__      =							downstream_dict[
                F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
            ]
            UpperCamelCase__      =							downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

      UpperCamelCase__      =							downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
      UpperCamelCase__      =							downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
      UpperCamelCase__      =							downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
      UpperCamelCase__      =							downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
      UpperCamelCase__      =							downstream_dict["objective.W"]
      return model







@torch.no_grad()
def _UpperCamelCase			(	__A ,     __A ,     __A ,     __A       )				->						str:


      '''simple docstring'''



      UpperCamelCase__      =							torch.load(__A ,     map_location="cpu"       )

      UpperCamelCase__      =							checkpoint["Downstream"]

      UpperCamelCase__      =							WavLMConfig.from_pretrained(__A       )
      UpperCamelCase__      =							WavaVecaFeatureExtractor.from_pretrained(
          __A ,     return_attention_mask=__A ,     do_normalize=__A       )

      UpperCamelCase__      =							hf_config.architectures[0]
      if arch.endswith("ForSequenceClassification"       ):
            UpperCamelCase__      =							convert_classification(__A ,     __A ,     __A       )
      elif arch.endswith("ForAudioFrameClassification"       ):
            UpperCamelCase__      =							convert_diarization(__A ,     __A ,     __A       )
      elif arch.endswith("ForXVector"       ):
            UpperCamelCase__      =							convert_xvector(__A ,     __A ,     __A       )
      else:
            raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''       )

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

      hf_feature_extractor.save_pretrained(__A       )
      hf_model.save_pretrained(__A       )


if __name__ == "__main__":
  a__    :   Union[str, Any]								=			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.')
  a__    :   Optional[Any]								=			parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





