




'''simple docstring'''





import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
UpperCamelCase_						:      List[Any]       =						logging.get_logger('''transformers.models.speecht5''')





def      __a     (       _UpperCamelCase:   Any ,						_UpperCamelCase:   Tuple ,						_UpperCamelCase:   str   )						->			Optional[int]:

     """simple docstring"""




     hf_model.apply_weight_norm()

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

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

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

     _snake_case							=				checkpoint["output_conv.1.weight_g"]
     _snake_case							=				checkpoint["output_conv.1.weight_v"]
     _snake_case							=				checkpoint["output_conv.1.bias"]

     hf_model.remove_weight_norm()





@torch.no_grad()
def      __a     (       _UpperCamelCase:   List[Any] ,						_UpperCamelCase:   Dict ,						_UpperCamelCase:   List[Any] ,						_UpperCamelCase:   Optional[int]=None ,						_UpperCamelCase:   Union[str, Any]=None ,						)						->			Optional[Any]:

     """simple docstring"""




     if config_path is not None:
          _snake_case							=				SpeechTaHifiGanConfig.from_pretrained(SCREAMING_SNAKE_CASE_   )
     else:
          _snake_case							=				SpeechTaHifiGanConfig()

     _snake_case							=				SpeechTaHifiGan(SCREAMING_SNAKE_CASE_   )

     _snake_case							=				torch.load(SCREAMING_SNAKE_CASE_   )
     load_weights(orig_checkpoint["model"]["generator"] ,						SCREAMING_SNAKE_CASE_ ,						SCREAMING_SNAKE_CASE_   )

     _snake_case							=				np.load(SCREAMING_SNAKE_CASE_   )
     _snake_case							=				stats[0].reshape(-1   )
     _snake_case							=				stats[1].reshape(-1   )
     _snake_case							=				torch.from_numpy(SCREAMING_SNAKE_CASE_   ).float()
     _snake_case							=				torch.from_numpy(SCREAMING_SNAKE_CASE_   ).float()

     model.save_pretrained(SCREAMING_SNAKE_CASE_   )

     if repo_id:
          print("Pushing to the hub..."   )
          model.push_to_hub(SCREAMING_SNAKE_CASE_   )


if __name__ == "__main__":
   UpperCamelCase_						:      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.'''
   )

   UpperCamelCase_						:      Union[str, 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      __a     (       _UpperCamelCase:   int   )						->			str:

     """simple docstring"""




     if number > 0:
          raise ValueError("input must be a negative integer"   )
     _snake_case							=				len(bin(_UpperCamelCase   )[3:]   )
     _snake_case							=				bin(abs(_UpperCamelCase   ) - (1 << binary_number_length)   )[3:]
     _snake_case							=				(
         (
             "1"
             + "0" * (binary_number_length - len(_UpperCamelCase   ))
             + twos_complement_number
         )
         if number < 0
         else "0"
     )
     return "0b" + twos_complement_number


if __name__ == "__main__":
   import doctest

   doctest.testmod()


