





import argparse

import torch

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


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






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	):
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							WavaVecaForSequenceClassification.from_pretrained(__lowerCamelCase,       config=__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							downstream_dict["projector.weight"]
     _SCREAMING_SNAKE_CASE       :	int						    =							downstream_dict["projector.bias"]
     _SCREAMING_SNAKE_CASE       :	int						    =							downstream_dict["model.post_net.linear.weight"]
     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							downstream_dict["model.post_net.linear.bias"]
     return model






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	):
     _SCREAMING_SNAKE_CASE       :	Any						    =							WavaVecaForAudioFrameClassification.from_pretrained(__lowerCamelCase,       config=__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	int						    =							downstream_dict["model.linear.weight"]
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							downstream_dict["model.linear.bias"]
     return model






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	):
     _SCREAMING_SNAKE_CASE       :	Any						    =							WavaVecaForXVector.from_pretrained(__lowerCamelCase,       config=__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	int						    =							downstream_dict["connector.weight"]
     _SCREAMING_SNAKE_CASE       :	Dict						    =							downstream_dict["connector.bias"]
     for i, kernel_size in enumerate(hf_config.tdnn_kernel	):
          _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							downstream_dict[
              f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
          ]
          _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

     _SCREAMING_SNAKE_CASE       :	List[Any]						    =							downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
     _SCREAMING_SNAKE_CASE       :	List[Any]						    =							downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
     _SCREAMING_SNAKE_CASE       :	List[Any]						    =							downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
     _SCREAMING_SNAKE_CASE       :	Dict						    =							downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
     _SCREAMING_SNAKE_CASE       :	Dict						    =							downstream_dict["objective.W"]
     return model






@torch.no_grad()
def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	):
     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							torch.load(__lowerCamelCase,       map_location="cpu"	)

     _SCREAMING_SNAKE_CASE       :	List[str]						    =							checkpoint["Downstream"]

     _SCREAMING_SNAKE_CASE       :	Any						    =							WavaVecaConfig.from_pretrained(__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	Dict						    =							WavaVecaFeatureExtractor.from_pretrained(
         __lowerCamelCase,       return_attention_mask=__lowerCamelCase,       do_normalize=__lowerCamelCase	)

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

     if hf_config.use_weighted_layer_sum:
          _SCREAMING_SNAKE_CASE       :	str						    =							checkpoint["Featurizer"]["weights"]

     hf_feature_extractor.save_pretrained(__lowerCamelCase	)
     hf_model.save_pretrained(__lowerCamelCase	)


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






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase	):
     while a != 0:
          _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Any						    =							b % a, a
     return b






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase	):
     if gcd(__lowerCamelCase,       __lowerCamelCase	) != 1:
          _SCREAMING_SNAKE_CASE       :	int						    =							f"""mod inverse of {a!r} and {m!r} does not exist"""
          raise ValueError(__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	List[str]						    =							1, 0, a
     _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	List[str]						    =							0, 1, m
     while va != 0:
          _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							ua // va
          _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							(ua - q * va), (ua - q * va), (ua - q * va), va, va, va
     return ua % m