



'''simple docstring'''






import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowerCAmelCase:      Tuple		 =		logging.get_logger('transformers.models.speecht5')
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  hf_model.apply_weight_norm()

  a		:      Dict       =  checkpoint['input_conv.weight_g']
  a		:      Optional[Any]       =  checkpoint['input_conv.weight_v']
  a		:      Dict       =  checkpoint['input_conv.bias']

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

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

  a		:      Dict       =  checkpoint['output_conv.1.weight_g']
  a		:      List[str]       =  checkpoint['output_conv.1.weight_v']
  a		:      List[Any]       =  checkpoint['output_conv.1.bias']

  hf_model.remove_weight_norm()
@torch.no_grad()
def    lowerCamelCase__ (		_A				,    _A				,    _A				,    _A=None				,    _A=None				,    ):
  if config_path is not None:
    a		:      Dict       =  SpeechTaHifiGanConfig.from_pretrained(_A       )
  else:
    a		:      List[Any]       =  SpeechTaHifiGanConfig()

  a		:      Optional[Any]       =  SpeechTaHifiGan(_A       )

  a		:      str       =  torch.load(_A       )
  load_weights(orig_checkpoint['model']['generator']				,    _A				,    _A       )

  a		:      Optional[Any]       =  np.load(_A       )
  a		:      Dict       =  stats[0].reshape(-1       )
  a		:      Union[str, Any]       =  stats[1].reshape(-1       )
  a		:      Union[str, Any]       =  torch.from_numpy(_A       ).float()
  a		:      int       =  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:      Optional[int]		 =		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'''






from math import factorial, pi
def    lowerCamelCase__ (		_A				,    _A = 30       ):

  if not isinstance(_A				,    (int, float)       ):
    raise ValueError('maclaurin_sin() requires either an int or float for theta'       )

  if not isinstance(_A				,    _A       ) or accuracy <= 0:
    raise ValueError('maclaurin_sin() requires a positive int for accuracy'       )

  a		:      List[Any]       =  float(_A       )
  a		:      Optional[int]       =  theta // (2 * pi)
  theta -= 2 * div * pi
  return sum(
      (-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1       ) for r in range(_A       )       )
def    lowerCamelCase__ (		_A				,    _A = 30       ):

  if not isinstance(_A				,    (int, float)       ):
    raise ValueError('maclaurin_cos() requires either an int or float for theta'       )

  if not isinstance(_A				,    _A       ) or accuracy <= 0:
    raise ValueError('maclaurin_cos() requires a positive int for accuracy'       )

  a		:      List[str]       =  float(_A       )
  a		:      List[str]       =  theta // (2 * pi)
  theta -= 2 * div * pi
  return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r       ) for r in range(_A       )       )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     print(maclaurin_sin(1_0))
     print(maclaurin_sin(-1_0))
     print(maclaurin_sin(1_0, 1_5))
     print(maclaurin_sin(-1_0, 1_5))

     print(maclaurin_cos(5))
     print(maclaurin_cos(-5))
     print(maclaurin_cos(1_0, 1_5))
     print(maclaurin_cos(-1_0, 1_5))