

"""simple docstring"""




import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE					:  List[Any]             =      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					:  List[str]             =      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					:  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,
       )





from __future__ import annotations
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   len(_A    ) // 2

     # choose the middle 3 elements
     SCREAMING_SNAKE_CASE__   =   lst[m - 1 : m + 2]

     # if middle element is peak
     if three[1] > three[0] and three[1] > three[2]:
          return three[1]

     # if increasing, recurse on right
     elif three[0] < three[2]:
          if len(lst[:m]    ) == 2:
               m -= 1
          return peak(lst[m:]    )

     # decreasing
     else:
          if len(lst[:m]    ) == 2:
               m += 1
          return peak(lst[:m]    )


if __name__ == "__main__":
       import doctest

       doctest.testmod()


