






import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__a   :							Any						= logging.get_logger("transformers.models.speecht5")



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      List[Any]      ,					__lowercase :      Any      ,					__lowercase :      int     )    ->   int:





    """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       _SCREAMING_SNAKE_CASE    (			__lowercase :      Tuple      ,					__lowercase :      Optional[Any]      ,					__lowercase :      Dict      ,					__lowercase :      Any=None      ,					__lowercase :      Optional[int]=None      ,					)    ->   Union[str, Any]:





    """simple docstring"""

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

    __A       =						SpeechTaHifiGan(__lowercase     )

    __A       =						torch.load(__lowercase     )
    load_weights(orig_checkpoint["""model"""]["""generator"""]      ,					__lowercase      ,					__lowercase     )

    __A       =						np.load(__lowercase     )
    __A       =						stats[0].reshape(-1     )
    __A       =						stats[1].reshape(-1     )
    __A       =						torch.from_numpy(__lowercase     ).float()
    __A       =						torch.from_numpy(__lowercase     ).float()

    model.save_pretrained(__lowercase     )

    if repo_id:
        print("""Pushing to the hub..."""     )
        model.push_to_hub(__lowercase     )


if __name__ == "__main__":
  __a   :							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."
  )

  __a   :							Union[str, 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,
  )












def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str     )    ->   str:





    """simple docstring"""

    __A       =						""""""
    for ch in key:
        if ch == " " or ch not in key_no_dups and ch.isalpha():
            key_no_dups += ch
    return key_no_dups



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str     )    ->   dict[str, str]:





    """simple docstring"""

    __A       =						[chr(i + 6_5     ) for i in range(2_6     )]
    # Remove duplicate characters from key
    __A       =						remove_duplicates(key.upper()     )
    __A       =						len(__lowercase     )
    # First fill cipher with key characters
    __A       =						{alphabet[i]: char for i, char in enumerate(__lowercase     )}
    # Then map remaining characters in alphabet to
    # the alphabet from the beginning
    for i in range(len(__lowercase     )      ,					2_6     ):
        __A       =						alphabet[i - offset]
        # Ensure we are not mapping letters to letters previously mapped
        while char in key:
            offset -= 1
            __A       =						alphabet[i - offset]
        __A       =						char
    return cipher_alphabet



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str      ,					__lowercase :      dict[str, str]     )    ->   str:





    """simple docstring"""

    return "".join(cipher_map.get(__lowercase      ,					__lowercase     ) for ch in message.upper()     )



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str      ,					__lowercase :      dict[str, str]     )    ->   str:





    """simple docstring"""

    __A       =						{v: k for k, v in cipher_map.items()}
    return "".join(rev_cipher_map.get(__lowercase      ,					__lowercase     ) for ch in message.upper()     )



def       _SCREAMING_SNAKE_CASE    (			)    ->   None:





    """simple docstring"""

    __A       =						input("""Enter message to encode or decode: """     ).strip()
    __A       =						input("""Enter keyword: """     ).strip()
    __A       =						input("""Encipher or decipher? E/D:"""     ).strip()[0].lower()
    try:
        __A       =						{"""e""": encipher, """d""": decipher}[option]
    except KeyError:
        raise KeyError("""invalid input option"""     )
    __A       =						create_cipher_map(__lowercase     )
    print(func(__lowercase      ,					__lowercase     )     )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




