



'''simple docstring'''





import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


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




def lowerCAmelCase  (__A  ,	__A  ,	__A):



     """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 lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A=None  ,	__A=None  ,	):



     """simple docstring"""



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

     _a      =  SpeechTaHifiGan(__A)

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

     _a      =  np.load(__A)
     _a      =  stats[0].reshape(-1)
     _a      =  stats[1].reshape(-1)
     _a      =  torch.from_numpy(__A).float()
     _a      =  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__":
  lowercase_		       =  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."
  )

  lowercase_		       =  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'''





import math






class __A			:



 '''simple docstring'''







 def __init__(self ,   A=0       )    ->     Dict:  # a graph with Node 0,1,...,N-1


      """simple docstring"""



      _a      =  n
      _a      =  [
          [math.inf for j in range(0 ,   A       )] for i in range(0 ,   A       )
      ]  # adjacency matrix for weight
      _a      =  [
          [math.inf for j in range(0 ,   A       )] for i in range(0 ,   A       )
      ]  # dp[i][j] stores minimum distance from i to j



 def 					a__  (self ,   A ,   A ,   A       )    ->     Tuple:


      """simple docstring"""



      _a      =  w



 def 					a__  (self       )    ->     List[Any]:


      """simple docstring"""



      for k in range(0 ,   self.n       ):
           for i in range(0 ,   self.n       ):
                for j in range(0 ,   self.n       ):
                     _a      =  min(self.dp[i][j] ,   self.dp[i][k] + self.dp[k][j]       )



 def 					a__  (self ,   A ,   A       )    ->     str:


      """simple docstring"""



      return self.dp[u][v]


if __name__ == "__main__":
  lowercase_		       =  Graph(5)
  graph.add_edge(0, 2, 9)
  graph.add_edge(0, 4, 10)
  graph.add_edge(1, 3, 5)
  graph.add_edge(2, 3, 7)
  graph.add_edge(3, 0, 10)
  graph.add_edge(3, 1, 2)
  graph.add_edge(3, 2, 1)
  graph.add_edge(3, 4, 6)
  graph.add_edge(4, 1, 3)
  graph.add_edge(4, 2, 4)
  graph.add_edge(4, 3, 9)
  graph.floyd_warshall()
  graph.show_min(1, 4)
  graph.show_min(0, 3)



