

"""simple docstring"""






import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


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

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   int  =    UniSpeechSatForSequenceClassification.from_pretrained(_lowerCamelCase ,   config=_lowerCamelCase   )
    _lowerCAmelCase       :   Any  =    downstream_dict['projector.weight']
    _lowerCAmelCase       :   Any  =    downstream_dict['projector.bias']
    _lowerCAmelCase       :   List[Any]  =    downstream_dict['model.post_net.linear.weight']
    _lowerCAmelCase       :   List[Any]  =    downstream_dict['model.post_net.linear.bias']
    return model

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Dict  =    UniSpeechSatForAudioFrameClassification.from_pretrained(_lowerCamelCase ,   config=_lowerCamelCase   )
    _lowerCAmelCase       :   Union[str, Any]  =    downstream_dict['model.linear.weight']
    _lowerCAmelCase       :   int  =    downstream_dict['model.linear.bias']
    return model

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Dict  =    UniSpeechSatForXVector.from_pretrained(_lowerCamelCase ,   config=_lowerCamelCase   )
    _lowerCAmelCase       :   Tuple  =    downstream_dict['connector.weight']
    _lowerCAmelCase       :   List[Any]  =    downstream_dict['connector.bias']
    for i, kernel_size in enumerate(hf_config.tdnn_kernel   ):
        _lowerCAmelCase       :   Optional[int]  =    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       :   Tuple  =    downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
    _lowerCAmelCase       :   Optional[int]  =    downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
    _lowerCAmelCase       :   List[Any]  =    downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
    _lowerCAmelCase       :   List[Any]  =    downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
    _lowerCAmelCase       :   str  =    downstream_dict['objective.W']
    return model

@torch.no_grad()
def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Any  =    torch.load(_lowerCamelCase ,   map_location='cpu'   )

    _lowerCAmelCase       :   Dict  =    checkpoint['Downstream']

    _lowerCAmelCase       :   Union[str, Any]  =    UniSpeechSatConfig.from_pretrained(_lowerCamelCase   )
    _lowerCAmelCase       :   Optional[int]  =    WavaVecaFeatureExtractor.from_pretrained(
        _lowerCamelCase ,   return_attention_mask=_lowerCamelCase ,   do_normalize=_lowerCamelCase   )

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

    if hf_config.use_weighted_layer_sum:
        _lowerCAmelCase       :   int  =    checkpoint['Featurizer']['weights']

    hf_feature_extractor.save_pretrained(_lowerCamelCase   )
    hf_model.save_pretrained(_lowerCamelCase   )


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








"""simple docstring"""






import baseaa

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    return baseaa.aaaencode(string.encode('utf-8'   )   )

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    return baseaa.aaadecode(_lowerCamelCase   ).decode('utf-8'   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()





