
import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowerCamelCase__          =		logging.get_logger("""transformers.models.speecht5""")
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		Dict:
			hf_model.apply_weight_norm()

			lowerCAmelCase__      :      List[str]         =		checkpoint["input_conv.weight_g"]
			lowerCAmelCase__      :      List[Any]         =		checkpoint["input_conv.weight_v"]
			lowerCAmelCase__      :      List[str]         =		checkpoint["input_conv.bias"]

			for i in range(len(config.upsample_rates     )     ):
						lowerCAmelCase__      :      Tuple         =		checkpoint[F'''upsamples.{i}.1.weight_g''']
						lowerCAmelCase__      :      Any         =		checkpoint[F'''upsamples.{i}.1.weight_v''']
						lowerCAmelCase__      :      Tuple         =		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     )     ):
									lowerCAmelCase__      :      List[str]         =		checkpoint[F'''blocks.{i}.convs1.{j}.1.weight_g''']
									lowerCAmelCase__      :      Tuple         =		checkpoint[F'''blocks.{i}.convs1.{j}.1.weight_v''']
									lowerCAmelCase__      :      int         =		checkpoint[F'''blocks.{i}.convs1.{j}.1.bias''']

									lowerCAmelCase__      :      Any         =		checkpoint[F'''blocks.{i}.convs2.{j}.1.weight_g''']
									lowerCAmelCase__      :      List[Any]         =		checkpoint[F'''blocks.{i}.convs2.{j}.1.weight_v''']
									lowerCAmelCase__      :      Any         =		checkpoint[F'''blocks.{i}.convs2.{j}.1.bias''']

			lowerCAmelCase__      :      Any         =		checkpoint["output_conv.1.weight_g"]
			lowerCAmelCase__      :      str         =		checkpoint["output_conv.1.weight_v"]
			lowerCAmelCase__      :      Any         =		checkpoint["output_conv.1.bias"]

			hf_model.remove_weight_norm()
@torch.no_grad()
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_=None       ,							SCREAMING_SNAKE_CASE_=None       ,							)				->		List[Any]:







			if config_path is not None:
						lowerCAmelCase__      :      Tuple         =		SpeechTaHifiGanConfig.from_pretrained(SCREAMING_SNAKE_CASE_     )
			else:
						lowerCAmelCase__      :      Optional[Any]         =		SpeechTaHifiGanConfig()

			lowerCAmelCase__      :      List[str]         =		SpeechTaHifiGan(SCREAMING_SNAKE_CASE_     )

			lowerCAmelCase__      :      Any         =		torch.load(SCREAMING_SNAKE_CASE_     )
			load_weights(orig_checkpoint['model']['generator']       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )

			lowerCAmelCase__      :      List[str]         =		np.load(SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      Any         =		stats[0].reshape(-1     )
			lowerCAmelCase__      :      Union[str, Any]         =		stats[1].reshape(-1     )
			lowerCAmelCase__      :      List[Any]         =		torch.from_numpy(SCREAMING_SNAKE_CASE_     ).float()
			lowerCAmelCase__      :      Any         =		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__":
					lowerCamelCase__          =		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."""
					)

					lowerCamelCase__          =		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__			(				SCREAMING_SNAKE_CASE    :    int				,      SCREAMING_SNAKE_CASE    :    List[Any]				,      SCREAMING_SNAKE_CASE    :    str				,      SCREAMING_SNAKE_CASE    :    List[Any]				):


  '''simple docstring'''







  if height >= 1:
    move_tower(height - 1				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)
    move_disk(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)
    move_tower(height - 1				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)
def 					a__			(				SCREAMING_SNAKE_CASE    :    Tuple				,      SCREAMING_SNAKE_CASE    :    Optional[int]				):


  '''simple docstring'''







  print("moving disk from"				,      SCREAMING_SNAKE_CASE				,      "to"				,      SCREAMING_SNAKE_CASE				)
def 					a__			(				):


  '''simple docstring'''







  lowerCAmelCase     :     Optional[int]								=							int(input("Height of hanoi: "				).strip()				)
  move_tower(SCREAMING_SNAKE_CASE				,      "A"				,      "B"				,      "C"				)


if __name__ == "__main__":
     main()





