






'''simple docstring'''
from typing import Union

import fire
import torch
from tqdm import tqdm




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase = "cpu"    ,		_UpperCamelCase = None				):


   """simple docstring"""




   lowercase_						:     str							     =  torch.load(_UpperCamelCase    ,		map_location=_UpperCamelCase				)
   for k, v in tqdm(state_dict.items()				):
      if not isinstance(_UpperCamelCase    ,		torch.Tensor				):
         raise TypeError("FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"				)
      lowercase_						:     str							     =  v.half()
   if save_path is None:  # overwrite src_path
      lowercase_						:     Optional[int]							     =  src_path
   torch.save(_UpperCamelCase    ,		_UpperCamelCase				)


if __name__ == "__main__":
 fire.Fire(convert)












'''simple docstring'''
import argparse

import torch

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


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




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Optional[Any]							     =  WavaVecaForSequenceClassification.from_pretrained(_UpperCamelCase    ,		config=_UpperCamelCase				)
   lowercase_						:     Optional[int]							     =  downstream_dict["projector.weight"]
   lowercase_						:     str							     =  downstream_dict["projector.bias"]
   lowercase_						:     int							     =  downstream_dict["model.post_net.linear.weight"]
   lowercase_						:     Optional[Any]							     =  downstream_dict["model.post_net.linear.bias"]
   return model




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Tuple							     =  WavaVecaForAudioFrameClassification.from_pretrained(_UpperCamelCase    ,		config=_UpperCamelCase				)
   lowercase_						:     Any							     =  downstream_dict["model.linear.weight"]
   lowercase_						:     List[str]							     =  downstream_dict["model.linear.bias"]
   return model




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Any							     =  WavaVecaForXVector.from_pretrained(_UpperCamelCase    ,		config=_UpperCamelCase				)
   lowercase_						:     str							     =  downstream_dict["connector.weight"]
   lowercase_						:     List[str]							     =  downstream_dict["connector.bias"]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel				):
      lowercase_						:     Union[str, Any]							     =  downstream_dict[
          F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
      ]
      lowercase_						:     Dict							     =  downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

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




@torch.no_grad()
def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Tuple							     =  torch.load(_UpperCamelCase    ,		map_location="cpu"				)

   lowercase_						:     Dict							     =  checkpoint["Downstream"]

   lowercase_						:     Optional[Any]							     =  WavaVecaConfig.from_pretrained(_UpperCamelCase				)
   lowercase_						:     Dict							     =  WavaVecaFeatureExtractor.from_pretrained(
       _UpperCamelCase    ,		return_attention_mask=_UpperCamelCase    ,		do_normalize=_UpperCamelCase				)

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

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

   hf_feature_extractor.save_pretrained(_UpperCamelCase				)
   hf_model.save_pretrained(_UpperCamelCase				)


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)




