






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 						_a    (      a :Any	,							a :List[str]	,							a :Tuple )							->			int:
 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 :Dict	,							a :int	,							a :Tuple	,							a :Dict=None	,							a :List[Any]=None	,							)							->			Optional[Any]:
 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__":
     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,
     )





'''simple docstring'''






from __future__ import annotations


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = len(A__      )
 # We need to create solution object to save path.
 __lowercase							  = [[0 for _ in range(A__      )] for _ in range(A__      )]
 __lowercase							  = run_maze(A__     ,				0     ,				0     ,				A__      )
 if solved:
  print('''\n'''.join(str(A__      ) for row in solutions      )      )
 else:
  print('''No solution exists!'''      )
 return solved


def 		_A     (    A__     ,				A__     ,				A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = len(A__      )
 # Final check point.
 if i == j == (size - 1):
  __lowercase							  = 1
  return True

 __lowercase							  = (not i < 0) and (not j < 0)  # Check lower bounds
 __lowercase							  = (i < size) and (j < size)  # Check upper bounds

 if lower_flag and upper_flag:
  # check for already visited and block points.
  __lowercase							  = (not solutions[i][j]) and (not maze[i][j])
  if block_flag:
   # check visited
   __lowercase							  = 1

   # check for directions
   if (
       run_maze(A__     ,				i + 1     ,				A__     ,				A__      )
       or run_maze(A__     ,				A__     ,				j + 1     ,				A__      )
       or run_maze(A__     ,				i - 1     ,				A__     ,				A__      )
       or run_maze(A__     ,				A__     ,				j - 1     ,				A__      )
   ):
    return True

   __lowercase							  = 0
   return False
 return False


if __name__ == "__main__":
 import doctest

 doctest.testmod()

