





import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__lowerCamelCase	:					Dict			  =					logging.get_logger("""transformers.models.speecht5""")



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)						->			Tuple:
 hf_model.apply_weight_norm()

 UpperCamelCase     : int	     =       checkpoint["input_conv.weight_g"]
 UpperCamelCase     : Dict	     =       checkpoint["input_conv.weight_v"]
 UpperCamelCase     : List[Any]	     =       checkpoint["input_conv.bias"]

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

   UpperCamelCase     : Union[str, Any]	     =       checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
   UpperCamelCase     : int	     =       checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
   UpperCamelCase     : Optional[Any]	     =       checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

 UpperCamelCase     : Tuple	     =       checkpoint["output_conv.1.weight_g"]
 UpperCamelCase     : Tuple	     =       checkpoint["output_conv.1.weight_v"]
 UpperCamelCase     : int	     =       checkpoint["output_conv.1.bias"]

 hf_model.remove_weight_norm()



@torch.no_grad()
def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase=None							,       _lowerCAmelCase=None							,       )						->			Tuple:
 if config_path is not None:
  UpperCamelCase     : List[Any]	     =       SpeechTaHifiGanConfig.from_pretrained(_lowerCAmelCase					)
 else:
  UpperCamelCase     : Optional[int]	     =       SpeechTaHifiGanConfig()

 UpperCamelCase     : List[str]	     =       SpeechTaHifiGan(_lowerCAmelCase					)

 UpperCamelCase     : str	     =       torch.load(_lowerCAmelCase					)
 load_weights(orig_checkpoint["model"]["generator"]							,       _lowerCAmelCase							,       _lowerCAmelCase					)

 UpperCamelCase     : List[Any]	     =       np.load(_lowerCAmelCase					)
 UpperCamelCase     : List[str]	     =       stats[0].reshape(-1					)
 UpperCamelCase     : Tuple	     =       stats[1].reshape(-1					)
 UpperCamelCase     : Any	     =       torch.from_numpy(_lowerCAmelCase					).float()
 UpperCamelCase     : Any	     =       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__":
    __lowerCamelCase	:					Union[str, 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."""
    )

    __lowerCamelCase	:					Tuple			  =					parser.parse_args()
    convert_hifigan_checkpoint(
        args.checkpoint_path,
        args.stats_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.push_to_hub,
    )







import os



def 		A_    (					)						->			int:
 UpperCamelCase     : List[str]	     =       os.path.dirname(os.path.realpath(_lowerCAmelCase					)					)
 UpperCamelCase     : Any	     =       os.path.join(_lowerCAmelCase							,       "triangle.txt"					)

 with open(_lowerCAmelCase					) as f:
  UpperCamelCase     : Optional[Any]	     =       f.readlines()

 UpperCamelCase     : Tuple	     =       []
 for line in triangle:
  UpperCamelCase     : List[str]	     =       []
  for number in line.strip().split(" "					):
   numbers_from_line.append(int(_lowerCAmelCase					)					)
  a.append(_lowerCAmelCase					)

 for i in range(1							,       len(_lowerCAmelCase					)					):
  for j in range(len(a[i]					)					):
   UpperCamelCase     : List[Any]	     =       a[i - 1][j] if j != len(a[i - 1]					) else 0
   UpperCamelCase     : Dict	     =       a[i - 1][j - 1] if j > 0 else 0
   a[i][j] += max(_lowerCAmelCase							,       _lowerCAmelCase					)
 return max(a[-1]					)


if __name__ == "__main__":
    print(solution())
