
import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__lowerCAmelCase			:     Dict           =   logging.get_logger('transformers.models.speecht5')

def a__    (       A_,     A_,     A_					):






  '''simple docstring'''





  hf_model.apply_weight_norm()

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

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

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

  __magic_name__				     =       checkpoint["""output_conv.1.weight_g"""]
  __magic_name__				     =       checkpoint["""output_conv.1.weight_v"""]
  __magic_name__				     =       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:
    __magic_name__				     =       SpeechTaHifiGanConfig.from_pretrained(A_					)
  else:
    __magic_name__				     =       SpeechTaHifiGanConfig()

  __magic_name__				     =       SpeechTaHifiGan(A_					)

  __magic_name__				     =       torch.load(A_					)
  load_weights(orig_checkpoint["""model"""]["""generator"""],     A_,     A_					)

  __magic_name__				     =       np.load(A_					)
  __magic_name__				     =       stats[0].reshape(-1					)
  __magic_name__				     =       stats[1].reshape(-1					)
  __magic_name__				     =       torch.from_numpy(A_					).float()
  __magic_name__				     =       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__":
    __lowerCAmelCase			:     Union[str, 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.'
    )

    __lowerCAmelCase			:     List[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,
    )



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()

