


'''simple docstring'''







import itertools







def _UpperCamelCase			(	__A = 1000000       )				->						int:


      '''simple docstring'''



      UpperCamelCase__      =							[1]

      for i in itertools.count(len(__A       )       ):
            UpperCamelCase__      =							0
            for j in itertools.count(1       ):
                  UpperCamelCase__      =							-1 if j % 2 == 0 else +1
                  UpperCamelCase__      =							(j * j * 3 - j) // 2
                  if index > i:
                        break
                  item += partitions[i - index] * sign
                  item %= number
                  index += j
                  if index > i:
                        break
                  item += partitions[i - index] * sign
                  item %= number

            if item == 0:
                  return i
            partitions.append(__A       )

      return 0


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(F"""{solution() = }""")









'''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)





