import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


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



def   __A		(			_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   __A		(			_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__":
  SCREAMING_SNAKE_CASE :	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."""
  )

  SCREAMING_SNAKE_CASE :	Any							     =		parser.parse_args()
  convert_hifigan_checkpoint(
      args.checkpoint_path,
      args.stats_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.push_to_hub,
  )



from __future__ import annotations

SCREAMING_SNAKE_CASE :	Optional[int]							     =		[]



def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


 for i in range(len(_A					)					):
  if board[row][i] == 1:
   return False
 for i in range(len(_A					)					):
  if board[i][column] == 1:
   return False
 for i, j in zip(range(_A							,				-1							,				-1					)							,				range(_A							,				-1							,				-1					)					):
  if board[i][j] == 1:
   return False
 for i, j in zip(range(_A							,				-1							,				-1					)							,				range(_A							,				len(_A					)					)					):
  if board[i][j] == 1:
   return False
 return True



def   __A		(			_A							,				_A					):



 """simple docstring"""


 if row >= len(_A					):
  solution.append(_A					)
  printboard(_A					)
  print()
  return True
 for i in range(len(_A					)					):
  if is_safe(_A							,				_A							,				_A					):
   __a       =  1
   solve(_A							,				row + 1					)
   __a       =  0
 return False



def   __A		(			_A					):



 """simple docstring"""


 for i in range(len(_A					)					):
  for j in range(len(_A					)					):
   if board[i][j] == 1:
    print("Q"							,				end=" "					)
   else:
    print("."							,				end=" "					)
  print()


# n=int(input("The no. of queens"))
SCREAMING_SNAKE_CASE :	List[str]							     =		8
SCREAMING_SNAKE_CASE :	str							     =		[[0 for i in range(n)] for j in range(n)]
solve(board, 0)






print("""The total no. of solutions are :""", len(solution))


