


"""simple docstring"""





import argparse

import torch

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


logging.set_verbosity_info()
snake_case__		:  Dict     					=					logging.get_logger(__name__)


def   _snake_case    (    _snake_case					:      Dict			, _snake_case					:      Union[str, Any]			, _snake_case					:      str      ):
      lowerCAmelCase       :						Any							= WavLMForSequenceClassification.from_pretrained(_snake_case			, config=_snake_case      )
      lowerCAmelCase       :						List[Any]							= downstream_dict['''projector.weight''']
      lowerCAmelCase       :						Any							= downstream_dict['''projector.bias''']
      lowerCAmelCase       :						int							= downstream_dict['''model.post_net.linear.weight''']
      lowerCAmelCase       :						str							= downstream_dict['''model.post_net.linear.bias''']
      return model


def   _snake_case    (    _snake_case					:      Optional[int]			, _snake_case					:      Optional[Any]			, _snake_case					:      str      ):
      lowerCAmelCase       :						int							= WavLMForAudioFrameClassification.from_pretrained(_snake_case			, config=_snake_case      )
      lowerCAmelCase       :						Tuple							= downstream_dict['''model.linear.weight''']
      lowerCAmelCase       :						Tuple							= downstream_dict['''model.linear.bias''']
      return model


def   _snake_case    (    _snake_case					:      Optional[int]			, _snake_case					:      Optional[int]			, _snake_case					:      List[Any]      ):
      lowerCAmelCase       :						Optional[Any]							= WavLMForXVector.from_pretrained(_snake_case			, config=_snake_case      )
      lowerCAmelCase       :						Any							= downstream_dict['''connector.weight''']
      lowerCAmelCase       :						str							= downstream_dict['''connector.bias''']
      for i, kernel_size in enumerate(hf_config.tdnn_kernel      ):
            lowerCAmelCase       :						List[str]							= downstream_dict[
                f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
            ]
            lowerCAmelCase       :						str							= downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

      lowerCAmelCase       :						str							= downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
      lowerCAmelCase       :						Dict							= downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
      lowerCAmelCase       :						Optional[int]							= downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
      lowerCAmelCase       :						Tuple							= downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
      lowerCAmelCase       :						Optional[Any]							= downstream_dict['''objective.W''']
      return model


@torch.no_grad()
def   _snake_case    (    _snake_case					:      Optional[int]			, _snake_case					:      Any			, _snake_case					:      Union[str, Any]			, _snake_case					:      str      ):
      lowerCAmelCase       :						int							= torch.load(_snake_case			, map_location='''cpu'''      )

      lowerCAmelCase       :						Dict							= checkpoint['''Downstream''']

      lowerCAmelCase       :						int							= WavLMConfig.from_pretrained(_snake_case      )
      lowerCAmelCase       :						Optional[int]							= WavaVecaFeatureExtractor.from_pretrained(
          _snake_case			, return_attention_mask=_snake_case			, do_normalize=_snake_case      )

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

      if hf_config.use_weighted_layer_sum:
            lowerCAmelCase       :						Tuple							= checkpoint['''Featurizer''']['''weights''']

      hf_feature_extractor.save_pretrained(_snake_case      )
      hf_model.save_pretrained(_snake_case      )


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






"""simple docstring"""





def   _snake_case    (    _snake_case					:      int      ):
      if not isinstance(_snake_case			, _snake_case      ):
            lowerCAmelCase       :						int							= f'''Input value of [number={number}] must be an integer'''
            raise TypeError(_snake_case      )
      if number < 0 or number % 2 == 1:
            return False
      lowerCAmelCase       :						int							= int(number**0.5      )
      return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
 import doctest

 doctest.testmod()


