





"""simple docstring"""
import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__UpperCamelCase     :						Optional[Any]						       =  logging.get_logger('''transformers.models.speecht5''')

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    List[str]  ,     _UpperCAmelCase					:    Optional[int]  ,     _UpperCAmelCase					:    Dict			):
 hf_model.apply_weight_norm()

 lowerCAmelCase    				= checkpoint['input_conv.weight_g']
 lowerCAmelCase    				= checkpoint['input_conv.weight_v']
 lowerCAmelCase    				= checkpoint['input_conv.bias']

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

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

 lowerCAmelCase    				= checkpoint['output_conv.1.weight_g']
 lowerCAmelCase    				= checkpoint['output_conv.1.weight_v']
 lowerCAmelCase    				= checkpoint['output_conv.1.bias']

 hf_model.remove_weight_norm()

@torch.no_grad()
def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Optional[Any]  ,     _UpperCAmelCase					:    Optional[int]  ,     _UpperCAmelCase					:    Optional[Any]  ,     _UpperCAmelCase					:    Any=None  ,     _UpperCAmelCase					:    Union[str, Any]=None  ,     ):
 if config_path is not None:
  lowerCAmelCase    				= SpeechTaHifiGanConfig.from_pretrained(_UpperCAmelCase			)
 else:
  lowerCAmelCase    				= SpeechTaHifiGanConfig()

 lowerCAmelCase    				= SpeechTaHifiGan(_UpperCAmelCase			)

 lowerCAmelCase    				= torch.load(_UpperCAmelCase			)
 load_weights(orig_checkpoint['model']['generator']  ,     _UpperCAmelCase  ,     _UpperCAmelCase			)

 lowerCAmelCase    				= np.load(_UpperCAmelCase			)
 lowerCAmelCase    				= stats[0].reshape(-1			)
 lowerCAmelCase    				= stats[1].reshape(-1			)
 lowerCAmelCase    				= torch.from_numpy(_UpperCAmelCase			).float()
 lowerCAmelCase    				= 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__":
       __UpperCamelCase     :						Dict						       =  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     :						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'''

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A			)			->			list:






       """simple docstring"""







       lowercase__      =					len(A			)
       lowercase__      =					[[0] * n for i in range(A			)]
       for i in range(A			):
              lowercase__      =					y_points[i]

       for i in range(2    ,					A			):
              for j in range(A    ,					A			):
                     lowercase__      =					(
                         (xa - x_points[j - i + 1]) * q[j][i - 1]
                         - (xa - x_points[j]) * q[j - 1][i - 1]
                     ) / (x_points[j] - x_points[j - i + 1])

       return [q[n - 1][n - 1], q]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




