



'''simple docstring'''





import argparse

import torch

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


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




def lowerCAmelCase						(__A						,			__A						,			__A):



     """simple docstring"""



     _a											=						WavaVecaForSequenceClassification.from_pretrained(__A						,			config=__A)
     _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 lowerCAmelCase						(__A						,			__A						,			__A):



     """simple docstring"""



     _a											=						WavaVecaForAudioFrameClassification.from_pretrained(__A						,			config=__A)
     _a											=						downstream_dict['''model.linear.weight''']
     _a											=						downstream_dict['''model.linear.bias''']
     return model




def lowerCAmelCase						(__A						,			__A						,			__A):



     """simple docstring"""



     _a											=						WavaVecaForXVector.from_pretrained(__A						,			config=__A)
     _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 lowerCAmelCase						(__A						,			__A						,			__A						,			__A):



     """simple docstring"""



     _a											=						torch.load(__A						,			map_location='''cpu''')

     _a											=						checkpoint['''Downstream''']

     _a											=						WavaVecaConfig.from_pretrained(__A)
     _a											=						WavaVecaFeatureExtractor.from_pretrained(
         __A						,			return_attention_mask=__A						,			do_normalize=__A)

     _a											=						hf_config.architectures[0]
     if arch.endswith('''ForSequenceClassification'''):
          _a											=						convert_classification(__A						,			__A						,			__A)
     elif arch.endswith('''ForAudioFrameClassification'''):
          _a											=						convert_diarization(__A						,			__A						,			__A)
     elif arch.endswith('''ForXVector'''):
          _a											=						convert_xvector(__A						,			__A						,			__A)
     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(__A)
     hf_model.save_pretrained(__A)


if __name__ == "__main__":
  lowercase_	 =						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.")
  lowercase_	 =						parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)








'''simple docstring'''





def lowerCAmelCase						(__A):



     """simple docstring"""



     _a											=						set()
     # To detect a back edge, keep track of vertices currently in the recursion stack
     _a											=						set()
     return any(
         node not in visited and depth_first_search(__A						,			__A						,			__A						,			__A)
         for node in graph)




def lowerCAmelCase						(__A						,			__A						,			__A						,			__A):



     """simple docstring"""



     visited.add(__A)
     rec_stk.add(__A)

     for node in graph[vertex]:
          if node not in visited:
               if depth_first_search(__A						,			__A						,			__A						,			__A):
                    return True
          elif node in rec_stk:
               return True

    # The node needs to be removed from recursion stack before function ends
     rec_stk.remove(__A)
     return False


if __name__ == "__main__":
  from doctest import testmod

  testmod()



