'''simple docstring'''

import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__a:			Tuple	       =       logging.get_logger("""transformers.models.speecht5""")
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					):
 hf_model.apply_weight_norm()

 lowercase__    :   List[Any]						=				checkpoint['''input_conv.weight_g''']
 lowercase__    :   Tuple						=				checkpoint['''input_conv.weight_v''']
 lowercase__    :   int						=				checkpoint['''input_conv.bias''']

 for i in range(len(config.upsample_rates					)					):
  lowercase__    :   List[str]						=				checkpoint[F"""upsamples.{i}.1.weight_g"""]
  lowercase__    :   Optional[int]						=				checkpoint[F"""upsamples.{i}.1.weight_v"""]
  lowercase__    :   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__    :   Any						=				checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
   lowercase__    :   List[str]						=				checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
   lowercase__    :   Tuple						=				checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]

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

 lowercase__    :   Any						=				checkpoint['''output_conv.1.weight_g''']
 lowercase__    :   str						=				checkpoint['''output_conv.1.weight_v''']
 lowercase__    :   Optional[int]						=				checkpoint['''output_conv.1.bias''']

 hf_model.remove_weight_norm()
@torch.no_grad()
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase=None		,					UpperCAmelCase=None		,					):
 if config_path is not None:
  lowercase__    :   Optional[int]						=				SpeechTaHifiGanConfig.from_pretrained(UpperCAmelCase					)
 else:
  lowercase__    :   Union[str, Any]						=				SpeechTaHifiGanConfig()

 lowercase__    :   int						=				SpeechTaHifiGan(UpperCAmelCase					)

 lowercase__    :   List[str]						=				torch.load(UpperCAmelCase					)
 load_weights(orig_checkpoint['''model''']['''generator''']		,					UpperCAmelCase		,					UpperCAmelCase					)

 lowercase__    :   Any						=				np.load(UpperCAmelCase					)
 lowercase__    :   List[Any]						=				stats[0].reshape(-1					)
 lowercase__    :   Dict						=				stats[1].reshape(-1					)
 lowercase__    :   Dict						=				torch.from_numpy(UpperCAmelCase					).float()
 lowercase__    :   List[str]						=				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__":
  __a:			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."""
  )

  __a:			Optional[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'''

import numpy as np
def       __UpperCamelCase	(       UpperCAmelCase					):
 return 1 / (1 + np.exp(-vector					))
def       __UpperCamelCase	(       UpperCAmelCase					):
 return vector * sigmoid(UpperCAmelCase					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()

