

import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE     :  Dict   			=			logging.get_logger('''transformers.models.speecht5''')
def 				UpperCAmelCase_   (						_A      ,		_A      ,		_A				):





     '''simple docstring'''




     hf_model.apply_weight_norm()

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

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

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

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

     hf_model.remove_weight_norm()
@torch.no_grad()
def 				UpperCAmelCase_   (						_A      ,		_A      ,		_A      ,		_A=None      ,		_A=None      ,		):





     '''simple docstring'''




     if config_path is not None:
          SCREAMING_SNAKE_CASE__    =    SpeechTaHifiGanConfig.from_pretrained(_A				)
     else:
          SCREAMING_SNAKE_CASE__    =    SpeechTaHifiGanConfig()

     SCREAMING_SNAKE_CASE__    =    SpeechTaHifiGan(_A				)

     SCREAMING_SNAKE_CASE__    =    torch.load(_A				)
     load_weights(orig_checkpoint['''model''']['''generator''']      ,		_A      ,		_A				)

     SCREAMING_SNAKE_CASE__    =    np.load(_A				)
     SCREAMING_SNAKE_CASE__    =    stats[0].reshape(-1				)
     SCREAMING_SNAKE_CASE__    =    stats[1].reshape(-1				)
     SCREAMING_SNAKE_CASE__    =    torch.from_numpy(_A				).float()
     SCREAMING_SNAKE_CASE__    =    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__":
       _SCREAMING_SNAKE_CASE     :  Tuple   			=			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.'''
       )

       _SCREAMING_SNAKE_CASE     :  Tuple   			=			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  _lowerCamelCase				( lowercase						:      int						)	->	bool:
				_a								= n ** (1 / 3)
				return (val * val * val) == n


if __name__ == "__main__":
					print(perfect_cube(27))
					print(perfect_cube(4))
