




'''simple docstring'''



def   lowercase__(			__UpperCamelCase:					List[str]      ,__UpperCamelCase:					List[str]      ,__UpperCamelCase:					Optional[Any]      ,__UpperCamelCase:					List[Any]    ):

      """simple docstring"""






      if height >= 1:
            move_tower(height - 1      ,__UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase    )
            move_disk(__UpperCamelCase      ,__UpperCamelCase    )
            move_tower(height - 1      ,__UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase    )




def   lowercase__(			__UpperCamelCase:					List[Any]      ,__UpperCamelCase:					str    ):

      """simple docstring"""






      print('moving disk from'      ,__UpperCamelCase      ,'to'      ,__UpperCamelCase    )




def   lowercase__(			):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      str    					=						int(input('Height of hanoi: '    ).strip()    )
      move_tower(__UpperCamelCase      ,'A'      ,'B'      ,'C'    )


if __name__ == "__main__":
      main()








'''simple docstring'''



import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
UpperCamelCase_    	=				logging.get_logger("transformers.models.speecht5")




def   lowercase__(			__UpperCamelCase:					List[Any]      ,__UpperCamelCase:					List[Any]      ,__UpperCamelCase:					Any    ):

      """simple docstring"""






      hf_model.apply_weight_norm()

      SCREAMING_SNAKE_CASE   :      Any    					=						checkpoint['input_conv.weight_g']
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						checkpoint['input_conv.weight_v']
      SCREAMING_SNAKE_CASE   :      str    					=						checkpoint['input_conv.bias']

      for i in range(len(config.upsample_rates    )    ):
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						checkpoint[f"upsamples.{i}.1.weight_g"]
            SCREAMING_SNAKE_CASE   :      Dict    					=						checkpoint[f"upsamples.{i}.1.weight_v"]
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						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   :      int    					=						checkpoint[f"blocks.{i}.convs1.{j}.1.weight_g"]
                  SCREAMING_SNAKE_CASE   :      str    					=						checkpoint[f"blocks.{i}.convs1.{j}.1.weight_v"]
                  SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						checkpoint[f"blocks.{i}.convs1.{j}.1.bias"]

                  SCREAMING_SNAKE_CASE   :      Dict    					=						checkpoint[f"blocks.{i}.convs2.{j}.1.weight_g"]
                  SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						checkpoint[f"blocks.{i}.convs2.{j}.1.weight_v"]
                  SCREAMING_SNAKE_CASE   :      Tuple    					=						checkpoint[f"blocks.{i}.convs2.{j}.1.bias"]

      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						checkpoint['output_conv.1.weight_g']
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						checkpoint['output_conv.1.weight_v']
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						checkpoint['output_conv.1.bias']

      hf_model.remove_weight_norm()




@torch.no_grad()
def   lowercase__(			__UpperCamelCase:					str      ,__UpperCamelCase:					int      ,__UpperCamelCase:					Any      ,__UpperCamelCase:					str=None      ,__UpperCamelCase:					Tuple=None      ,):

      """simple docstring"""






      if config_path is not None:
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						SpeechTaHifiGanConfig.from_pretrained(__UpperCamelCase    )
      else:
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						SpeechTaHifiGanConfig()

      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						SpeechTaHifiGan(__UpperCamelCase    )

      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						torch.load(__UpperCamelCase    )
      load_weights(orig_checkpoint['model']['generator']      ,__UpperCamelCase      ,__UpperCamelCase    )

      SCREAMING_SNAKE_CASE   :      int    					=						np.load(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						stats[0].reshape(-1    )
      SCREAMING_SNAKE_CASE   :      Tuple    					=						stats[1].reshape(-1    )
      SCREAMING_SNAKE_CASE   :      Tuple    					=						torch.from_numpy(__UpperCamelCase    ).float()
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						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_    	=				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_    	=				parser.parse_args()
      convert_hifigan_checkpoint(
          args.checkpoint_path,
          args.stats_path,
          args.pytorch_dump_folder_path,
          args.config_path,
          args.push_to_hub,
      )


