

'''simple docstring'''

def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     list[list[str]]						 =			[[] for _ in range(UpperCAmelCase_     )]
 UpperCAmelCase	:     Optional[int]						 =			key - 1

 if key <= 0:
  raise ValueError('Height of grid can\'t be 0 or negative'     )
 if key == 1 or len(UpperCAmelCase_     ) <= key:
  return input_string

 for position, character in enumerate(UpperCAmelCase_     ):
  UpperCAmelCase	:     str						 =			position % (lowest * 2)  # puts it in bounds
  UpperCAmelCase	:     Any						 =			min(UpperCAmelCase_  ,	lowest * 2 - num     )  # creates zigzag pattern
  temp_grid[num].append(UpperCAmelCase_     )
 UpperCAmelCase	:     List[Any]						 =			[''.join(UpperCAmelCase_     ) for row in temp_grid]
 UpperCAmelCase	:     Optional[int]						 =			''.join(UpperCAmelCase_     )

 return output_string




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     List[Any]						 =			[]
 UpperCAmelCase	:     List[Any]						 =			key - 1

 if key <= 0:
  raise ValueError('Height of grid can\'t be 0 or negative'     )
 if key == 1:
  return input_string

 UpperCAmelCase	:     list[list[str]]						 =			[[] for _ in range(UpperCAmelCase_     )]  # generates template
 for position in range(len(UpperCAmelCase_     )     ):
  UpperCAmelCase	:     List[Any]						 =			position % (lowest * 2)  # puts it in bounds
  UpperCAmelCase	:     Union[str, Any]						 =			min(UpperCAmelCase_  ,	lowest * 2 - num     )  # creates zigzag pattern
  temp_grid[num].append('*'     )

 UpperCAmelCase	:     List[str]						 =			0
 for row in temp_grid:  # fills in the characters
  UpperCAmelCase	:     int						 =			input_string[counter : counter + len(UpperCAmelCase_     )]
  grid.append(list(UpperCAmelCase_     )     )
  counter += len(UpperCAmelCase_     )

 UpperCAmelCase	:     List[Any]						 =			''  # reads as zigzag
 for position in range(len(UpperCAmelCase_     )     ):
  UpperCAmelCase	:     Union[str, Any]						 =			position % (lowest * 2)  # puts it in bounds
  UpperCAmelCase	:     Optional[int]						 =			min(UpperCAmelCase_  ,	lowest * 2 - num     )  # creates zigzag pattern
  output_string += grid[num][0]
  grid[num].pop(0     )
 return output_string




def 				UpperCamelCase(					UpperCAmelCase_     ):
 UpperCAmelCase	:     Dict						 =			{}
 for key_guess in range(1  ,	len(UpperCAmelCase_     )     ):  # tries every key
  UpperCAmelCase	:     int						 =			decrypt(UpperCAmelCase_  ,	UpperCAmelCase_     )
 return results


if __name__ == "__main__":
 import doctest

 doctest.testmod()







'''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 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 hf_model.apply_weight_norm()

 UpperCAmelCase	:     Dict						 =			checkpoint['input_conv.weight_g']
 UpperCAmelCase	:     Any						 =			checkpoint['input_conv.weight_v']
 UpperCAmelCase	:     Any						 =			checkpoint['input_conv.bias']

 for i in range(len(config.upsample_rates     )     ):
  UpperCAmelCase	:     Any						 =			checkpoint[F"""upsamples.{i}.1.weight_g"""]
  UpperCAmelCase	:     Union[str, Any]						 =			checkpoint[F"""upsamples.{i}.1.weight_v"""]
  UpperCAmelCase	:     str						 =			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	:     Any						 =			checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
   UpperCAmelCase	:     Optional[int]						 =			checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
   UpperCAmelCase	:     Dict						 =			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[int]						 =			checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

 UpperCAmelCase	:     Dict						 =			checkpoint['output_conv.1.weight_g']
 UpperCAmelCase	:     str						 =			checkpoint['output_conv.1.weight_v']
 UpperCAmelCase	:     Union[str, Any]						 =			checkpoint['output_conv.1.bias']

 hf_model.remove_weight_norm()




@torch.no_grad()
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_=None  ,	UpperCAmelCase_=None  ,	):
 if config_path is not None:
  UpperCAmelCase	:     Any						 =			SpeechTaHifiGanConfig.from_pretrained(UpperCAmelCase_     )
 else:
  UpperCAmelCase	:     Optional[Any]						 =			SpeechTaHifiGanConfig()

 UpperCAmelCase	:     List[Any]						 =			SpeechTaHifiGan(UpperCAmelCase_     )

 UpperCAmelCase	:     int						 =			torch.load(UpperCAmelCase_     )
 load_weights(orig_checkpoint['model']['generator']  ,	UpperCAmelCase_  ,	UpperCAmelCase_     )

 UpperCAmelCase	:     int						 =			np.load(UpperCAmelCase_     )
 UpperCAmelCase	:     str						 =			stats[0].reshape(-1     )
 UpperCAmelCase	:     List[str]						 =			stats[1].reshape(-1     )
 UpperCAmelCase	:     Union[str, Any]						 =			torch.from_numpy(UpperCAmelCase_     ).float()
 UpperCAmelCase	:     List[str]						 =			torch.from_numpy(UpperCAmelCase_     ).float()

 model.save_pretrained(UpperCAmelCase_     )

 if repo_id:
  print('Pushing to the hub...'     )
  model.push_to_hub(UpperCAmelCase_     )


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,
 )




