


'''simple docstring'''






import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
a_   :      Optional[Any]				    =      logging.get_logger("transformers.models.speecht5")






def 		_A			(lowerCAmelCase__					:str			,  lowerCAmelCase__					:int			,  lowerCAmelCase__					:Dict						)				->					Union[str, Any]:




     '''simple docstring'''



     hf_model.apply_weight_norm()

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

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

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

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

     hf_model.remove_weight_norm()






@torch.no_grad()
def 		_A			(lowerCAmelCase__					:str			,  lowerCAmelCase__					:Union[str, Any]			,  lowerCAmelCase__					:Union[str, Any]			,  lowerCAmelCase__					:Tuple=None			,  lowerCAmelCase__					:int=None			,  )				->					Any:




     '''simple docstring'''



     if config_path is not None:
          _a      =							SpeechTaHifiGanConfig.from_pretrained(lowerCAmelCase__						)
     else:
          _a      =							SpeechTaHifiGanConfig()

     _a      =							SpeechTaHifiGan(lowerCAmelCase__						)

     _a      =							torch.load(lowerCAmelCase__						)
     load_weights(orig_checkpoint['model']['generator']			,  lowerCAmelCase__			,  lowerCAmelCase__						)

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

     model.save_pretrained(lowerCAmelCase__						)

     if repo_id:
          print('Pushing to the hub...'						)
          model.push_to_hub(lowerCAmelCase__						)


if __name__ == "__main__":
 a_   :      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."
 )

 a_   :      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'''






def 		_A			(lowerCAmelCase__					:list[int]			,  lowerCAmelCase__					:list[int]						)				->					None:




     '''simple docstring'''



     _a      =							len(lowerCAmelCase__						)
     print('The following activities are selected:'						)

     # The first activity is always selected
     _a      =							0
     print(lowerCAmelCase__			,  end=','						)

     # Consider rest of the activities
     for j in range(lowerCAmelCase__						):
          # If this activity has start time greater than
          # or equal to the finish time of previously
          # selected activity, then select it
          if start[j] >= finish[i]:
               print(lowerCAmelCase__			,  end=','						)
               _a      =							j


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 a_   :      List[str]				    =      [1, 3, 0, 5, 8, 5]
 a_   :      str				    =      [2, 4, 6, 7, 9, 9]
 print_max_activities(start, finish)

