






"""simple docstring"""




import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_snake_case          =   logging.get_logger('transformers.models.speecht5')

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  hf_model.apply_weight_norm()

  _a  :       Dict             =	checkpoint["""input_conv.weight_g"""]
  _a  :       Tuple             =	checkpoint["""input_conv.weight_v"""]
  _a  :       Optional[int]             =	checkpoint["""input_conv.bias"""]

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

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

  _a  :       str             =	checkpoint["""output_conv.1.weight_g"""]
  _a  :       Tuple             =	checkpoint["""output_conv.1.weight_v"""]
  _a  :       Dict             =	checkpoint["""output_conv.1.bias"""]

  hf_model.remove_weight_norm()

@torch.no_grad()
def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__=None     ,   UpperCamelCase__=None     ,   ):



  '''simple docstring'''





  if config_path is not None:
    _a  :       Any             =	SpeechTaHifiGanConfig.from_pretrained(A_				)
  else:
    _a  :       int             =	SpeechTaHifiGanConfig()

  _a  :       int             =	SpeechTaHifiGan(A_				)

  _a  :       List[str]             =	torch.load(A_				)
  load_weights(orig_checkpoint["""model"""]["""generator"""]     ,   A_     ,   A_				)

  _a  :       List[str]             =	np.load(A_				)
  _a  :       Any             =	stats[0].reshape(-1				)
  _a  :       int             =	stats[1].reshape(-1				)
  _a  :       str             =	torch.from_numpy(A_				).float()
  _a  :       Optional[Any]             =	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__":
 _snake_case          =   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.'
 )

 _snake_case          =   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 a__    (       A_					):






  '''simple docstring'''





  if not isinstance(A_,     A_					):
    raise ValueError("""Input series is not valid, valid series - [2, 4, 6]"""					)
  if len(A_					) == 0:
    raise ValueError("""Input list must be a non empty list"""					)
  if len(A_					) == 1:
    return True
  __magic_name__				     =       series[1] - series[0]
  for index in range(len(A_					) - 1					):
    if series[index + 1] - series[index] != common_diff:
      return False
  return True

def a__    (       A_					):






  '''simple docstring'''





  if not isinstance(A_,     A_					):
    raise ValueError("""Input series is not valid, valid series - [2, 4, 6]"""					)
  if len(A_					) == 0:
    raise ValueError("""Input list must be a non empty list"""					)
  __magic_name__				     =       0
  for val in series:
    answer += val
  return answer / len(A_					)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

