




import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowercase__				:      Any	   = logging.get_logger("transformers.models.speecht5")

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					Optional[Any]:
     hf_model.apply_weight_norm()

     a     =				checkpoint["input_conv.weight_g"]
     a     =				checkpoint["input_conv.weight_v"]
     a     =				checkpoint["input_conv.bias"]

     for i in range(len(config.upsample_rates)):
          a     =				checkpoint[f'''upsamples.{i}.1.weight_g''']
          a     =				checkpoint[f'''upsamples.{i}.1.weight_v''']
          a     =				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)):
               a     =				checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_g''']
               a     =				checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_v''']
               a     =				checkpoint[f'''blocks.{i}.convs1.{j}.1.bias''']

               a     =				checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_g''']
               a     =				checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_v''']
               a     =				checkpoint[f'''blocks.{i}.convs2.{j}.1.bias''']

     a     =				checkpoint["output_conv.1.weight_g"]
     a     =				checkpoint["output_conv.1.weight_v"]
     a     =				checkpoint["output_conv.1.bias"]

     hf_model.remove_weight_norm()

@torch.no_grad()
def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase=None		, __UpperCamelCase=None		, )      ->					Union[str, Any]:
     if config_path is not None:
          a     =				SpeechTaHifiGanConfig.from_pretrained(__UpperCamelCase)
     else:
          a     =				SpeechTaHifiGanConfig()

     a     =				SpeechTaHifiGan(__UpperCamelCase)

     a     =				torch.load(__UpperCamelCase)
     load_weights(orig_checkpoint["model"]["generator"]		, __UpperCamelCase		, __UpperCamelCase)

     a     =				np.load(__UpperCamelCase)
     a     =				stats[0].reshape(-1)
     a     =				stats[1].reshape(-1)
     a     =				torch.from_numpy(__UpperCamelCase).float()
     a     =				torch.from_numpy(__UpperCamelCase).float()

     model.save_pretrained(__UpperCamelCase)

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


if __name__ == "__main__":
   lowercase__				:      int	   = 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."
   )

   lowercase__				:      Union[str, Any]	   = parser.parse_args()
   convert_hifigan_checkpoint(
       args.checkpoint_path,
       args.stats_path,
       args.pytorch_dump_folder_path,
       args.config_path,
       args.push_to_hub,
   )










lowercase__				:      Optional[int]	   = [
    "DownloadConfig",
    "DownloadManager",
    "DownloadMode",
    "StreamingDownloadManager",
]

from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager

