




import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_UpperCAmelCase		:  Optional[int]	=   logging.get_logger("transformers.models.speecht5")



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase,	lowerCamelCase      ):
  hf_model.apply_weight_norm()

  lowercase			:str							=  checkpoint["input_conv.weight_g"]
  lowercase			:str							=  checkpoint["input_conv.weight_v"]
  lowercase			:Optional[int]							=  checkpoint["input_conv.bias"]

  for i in range(len(config.upsample_rates      )      ):
    lowercase			:Optional[int]							=  checkpoint[F"upsamples.{i}.1.weight_g"]
    lowercase			:List[Any]							=  checkpoint[F"upsamples.{i}.1.weight_v"]
    lowercase			:List[str]							=  checkpoint[F"upsamples.{i}.1.bias"]

  for i in range(len(config.upsample_rates      ) * len(config.resblock_kernel_sizes      )      ):
    for j in range(len(config.resblock_dilation_sizes      )      ):
      lowercase			:List[str]							=  checkpoint[F"blocks.{i}.convs1.{j}.1.weight_g"]
      lowercase			:Dict							=  checkpoint[F"blocks.{i}.convs1.{j}.1.weight_v"]
      lowercase			:int							=  checkpoint[F"blocks.{i}.convs1.{j}.1.bias"]

      lowercase			:Any							=  checkpoint[F"blocks.{i}.convs2.{j}.1.weight_g"]
      lowercase			:Union[str, Any]							=  checkpoint[F"blocks.{i}.convs2.{j}.1.weight_v"]
      lowercase			:Optional[int]							=  checkpoint[F"blocks.{i}.convs2.{j}.1.bias"]

  lowercase			:Union[str, Any]							=  checkpoint["output_conv.1.weight_g"]
  lowercase			:Any							=  checkpoint["output_conv.1.weight_v"]
  lowercase			:List[str]							=  checkpoint["output_conv.1.bias"]

  hf_model.remove_weight_norm()



@torch.no_grad()
def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase=None,	lowerCamelCase=None,	):
  if config_path is not None:
    lowercase			:Optional[Any]							=  SpeechTaHifiGanConfig.from_pretrained(lowerCamelCase      )
  else:
    lowercase			:Union[str, Any]							=  SpeechTaHifiGanConfig()

  lowercase			:int							=  SpeechTaHifiGan(lowerCamelCase      )

  lowercase			:Optional[Any]							=  torch.load(lowerCamelCase      )
  load_weights(orig_checkpoint["model"]["generator"],	lowerCamelCase,	lowerCamelCase      )

  lowercase			:Optional[int]							=  np.load(lowerCamelCase      )
  lowercase			:Tuple							=  stats[0].reshape(-1      )
  lowercase			:Union[str, Any]							=  stats[1].reshape(-1      )
  lowercase			:Tuple							=  torch.from_numpy(lowerCamelCase      ).float()
  lowercase			:Dict							=  torch.from_numpy(lowerCamelCase      ).float()

  model.save_pretrained(lowerCamelCase      )

  if repo_id:
    print("Pushing to the hub..."      )
    model.push_to_hub(lowerCamelCase      )


if __name__ == "__main__":
     _UpperCAmelCase		:  Optional[Any]	=   argparse.ArgumentParser()
     parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
     parser.add_argument("--stats_path", required=True, default=None, type=str, help="Path to stats.npy file")
     parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
     parser.add_argument(
         "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
     )
     parser.add_argument(
         "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
     )

     _UpperCAmelCase		:  Tuple	=   parser.parse_args()
     convert_hifigan_checkpoint(
         args.checkpoint_path,
         args.stats_path,
         args.pytorch_dump_folder_path,
         args.config_path,
         args.push_to_hub,
     )









def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase      ):
  lowercase			:List[str]							=  ""
  for word_or_phrase in separated:
    if not isinstance(lowerCamelCase,	lowerCamelCase      ):
      raise Exception("join() accepts only strings to be joined"      )
    joined += word_or_phrase + separator
  return joined.strip(lowerCamelCase      )


if __name__ == "__main__":
     from doctest import testmod

     testmod()



