


'''simple docstring'''







import argparse

import torch

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


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






def       lowercase					(_A ,    _A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Optional[Any]       =							WavaVecaForSequenceClassification.from_pretrained(_A ,    config=_A       )
       _lowerCAmelCase     :				Tuple       =							downstream_dict['projector.weight']
       _lowerCAmelCase     :				Optional[int]       =							downstream_dict['projector.bias']
       _lowerCAmelCase     :				List[Any]       =							downstream_dict['model.post_net.linear.weight']
       _lowerCAmelCase     :				Tuple       =							downstream_dict['model.post_net.linear.bias']
       return model






def       lowercase					(_A ,    _A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Tuple       =							WavaVecaForAudioFrameClassification.from_pretrained(_A ,    config=_A       )
       _lowerCAmelCase     :				Any       =							downstream_dict['model.linear.weight']
       _lowerCAmelCase     :				int       =							downstream_dict['model.linear.bias']
       return model






def       lowercase					(_A ,    _A ,    _A       ):







       """simple docstring"""







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






@torch.no_grad()
def       lowercase					(_A ,    _A ,    _A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Optional[Any]       =							torch.load(_A ,    map_location='cpu'       )

       _lowerCAmelCase     :				List[str]       =							checkpoint['Downstream']

       _lowerCAmelCase     :				Dict       =							WavaVecaConfig.from_pretrained(_A       )
       _lowerCAmelCase     :				Union[str, Any]       =							WavaVecaFeatureExtractor.from_pretrained(
           _A ,    return_attention_mask=_A ,    do_normalize=_A       )

       _lowerCAmelCase     :				Optional[int]       =							hf_config.architectures[0]
       if arch.endswith('ForSequenceClassification'       ):
              _lowerCAmelCase     :				List[str]       =							convert_classification(_A ,    _A ,    _A       )
       elif arch.endswith('ForAudioFrameClassification'       ):
              _lowerCAmelCase     :				Dict       =							convert_diarization(_A ,    _A ,    _A       )
       elif arch.endswith('ForXVector'       ):
              _lowerCAmelCase     :				Union[str, Any]       =							convert_xvector(_A ,    _A ,    _A       )
       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(_A       )
       hf_model.save_pretrained(_A       )


if __name__ == "__main__":
       lowerCAmelCase     :  Tuple											=    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     :  Optional[int]											=    parser.parse_args()
       convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





'''simple docstring'''







from ..utils import DummyObject, requires_backends
class 						UpperCamelCase__       (			metaclass=SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     __magic_name__           =     ["torch", "torchsde"]







     def __init__(			self   ,						*snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

            requires_backends(self   ,						['torch', 'torchsde'] )







     @classmethod
     def a						(			cls   ,						*snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

            requires_backends(cls   ,						['torch', 'torchsde'] )







     @classmethod
     def a						(			cls   ,						*snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

            requires_backends(cls   ,						['torch', 'torchsde'] )

