

'''simple docstring'''



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    a__       (  a__      ,    a__      ,    a__						):




     """simple docstring"""


     hf_model.apply_weight_norm()

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

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

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

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

     hf_model.remove_weight_norm()







@torch.no_grad()
def    a__       (  a__      ,    a__      ,    a__      ,    a__=None      ,    a__=None      ,    ):




     """simple docstring"""


     if config_path is not None:
          __SCREAMING_SNAKE_CASE     =      SpeechTaHifiGanConfig.from_pretrained(a__						)
     else:
          __SCREAMING_SNAKE_CASE     =      SpeechTaHifiGanConfig()

     __SCREAMING_SNAKE_CASE     =      SpeechTaHifiGan(a__						)

     __SCREAMING_SNAKE_CASE     =      torch.load(a__						)
     load_weights(orig_checkpoint["""model"""]["""generator"""]      ,    a__      ,    a__						)

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

     model.save_pretrained(a__						)

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


if __name__ == "__main__":
  UpperCAmelCase :       List[str]			   =     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 :       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,
  )









'''simple docstring'''



def    a__       (  a__						):




     """simple docstring"""


     if isinstance(a__      ,    a__						):
          raise TypeError("""'float' object cannot be interpreted as an integer"""						)
     if isinstance(a__      ,    a__						):
          raise TypeError("""'str' object cannot be interpreted as an integer"""						)

     if num == 0:
          return "0b0"

     __SCREAMING_SNAKE_CASE     =      False

     if num < 0:
          __SCREAMING_SNAKE_CASE     =      True
          __SCREAMING_SNAKE_CASE     =      -num

     __SCREAMING_SNAKE_CASE     =      []
     while num > 0:
          binary.insert(0      ,    num % 2						)
          num >>= 1

     if negative:
          return "-0b" + "".join(str(a__						) for e in binary						)

     return "0b" + "".join(str(a__						) for e in binary						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()






