




import argparse

import torch

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


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

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			Union[str, Any]:
       lowerCamelCase       :				Optional[int]      			= WavLMForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_			,config=SCREAMING_SNAKE_CASE_   )
       lowerCamelCase       :				List[str]      			= downstream_dict["projector.weight"]
       lowerCamelCase       :				Optional[Any]      			= downstream_dict["projector.bias"]
       lowerCamelCase       :				Optional[Any]      			= downstream_dict["model.post_net.linear.weight"]
       lowerCamelCase       :				List[str]      			= downstream_dict["model.post_net.linear.bias"]
       return model

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			List[Any]:
       lowerCamelCase       :				Union[str, Any]      			= WavLMForAudioFrameClassification.from_pretrained(SCREAMING_SNAKE_CASE_			,config=SCREAMING_SNAKE_CASE_   )
       lowerCamelCase       :				List[str]      			= downstream_dict["model.linear.weight"]
       lowerCamelCase       :				List[Any]      			= downstream_dict["model.linear.bias"]
       return model

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			Any:
       lowerCamelCase       :				str      			= WavLMForXVector.from_pretrained(SCREAMING_SNAKE_CASE_			,config=SCREAMING_SNAKE_CASE_   )
       lowerCamelCase       :				Union[str, Any]      			= downstream_dict["connector.weight"]
       lowerCamelCase       :				Optional[Any]      			= downstream_dict["connector.bias"]
       for i, kernel_size in enumerate(hf_config.tdnn_kernel   ):
              lowerCamelCase       :				Any      			= downstream_dict[
                  f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
              ]
              lowerCamelCase       :				Dict      			= downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

       lowerCamelCase       :				Dict      			= downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
       lowerCamelCase       :				Optional[Any]      			= downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
       lowerCamelCase       :				Union[str, Any]      			= downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
       lowerCamelCase       :				int      			= downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
       lowerCamelCase       :				Tuple      			= downstream_dict["objective.W"]
       return model

@torch.no_grad()
def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			int:
       lowerCamelCase       :				Optional[int]      			= torch.load(SCREAMING_SNAKE_CASE_			,map_location="cpu"   )

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

       lowerCamelCase       :				Any      			= WavLMConfig.from_pretrained(SCREAMING_SNAKE_CASE_   )
       lowerCamelCase       :				Any      			= WavaVecaFeatureExtractor.from_pretrained(
           SCREAMING_SNAKE_CASE_			,return_attention_mask=SCREAMING_SNAKE_CASE_			,do_normalize=SCREAMING_SNAKE_CASE_   )

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

       if hf_config.use_weighted_layer_sum:
              lowerCamelCase       :				Union[str, Any]      			= checkpoint["Featurizer"]["weights"]

       hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_   )
       hf_model.save_pretrained(SCREAMING_SNAKE_CASE_   )


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












import numpy as np

def 					A				(				_SCREAMING_SNAKE_CASE   )     ->			str:
       lowerCamelCase			,				lowerCamelCase       :				Dict      			= a.shape
       lowerCamelCase       :				Union[str, Any]      			= min(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				Union[str, Any]      			= np.eye(_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				List[Any]      			= a.copy()

       for k in range(t - 1   ):
              # select a column of modified matrix A':
              lowerCamelCase       :				Optional[Any]      			= r[k:, [k]]
              # construct first basis vector
              lowerCamelCase       :				Union[str, Any]      			= np.zeros_like(_SCREAMING_SNAKE_CASE   )
              lowerCamelCase       :				int      			= 1.0
              # determine scaling factor
              lowerCamelCase       :				Any      			= np.linalg.norm(_SCREAMING_SNAKE_CASE   )
              # construct vector v for Householder reflection
              lowerCamelCase       :				List[str]      			= x + np.sign(x[0]   ) * alpha * ea
              v /= np.linalg.norm(_SCREAMING_SNAKE_CASE   )

              # construct the Householder matrix
              lowerCamelCase       :				List[Any]      			= np.eye(m - k   ) - 2.0 * v @ v.T
              # pad with ones and zeros as necessary
              lowerCamelCase       :				Union[str, Any]      			= np.block([[np.eye(_SCREAMING_SNAKE_CASE   ), np.zeros((k, m - k)   )], [np.zeros((m - k, k)   ), q_k]]   )

              lowerCamelCase       :				Any      			= q @ q_k.T
              lowerCamelCase       :				List[Any]      			= q_k @ r

       return q, r


if __name__ == "__main__":
       import doctest

       doctest.testmod()






