
import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__lowerCAmelCase			:     Dict           =   logging.get_logger('transformers.models.speecht5')

def a__    (       A_,     A_,     A_					):






  '''simple docstring'''





  hf_model.apply_weight_norm()

  __magic_name__				     =       checkpoint["""input_conv.weight_g"""]
  __magic_name__				     =       checkpoint["""input_conv.weight_v"""]
  __magic_name__				     =       checkpoint["""input_conv.bias"""]

  for i in range(len(config.upsample_rates					)					):
    __magic_name__				     =       checkpoint[f'''upsamples.{i}.1.weight_g''']
    __magic_name__				     =       checkpoint[f'''upsamples.{i}.1.weight_v''']
    __magic_name__				     =       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					)					):
      __magic_name__				     =       checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_g''']
      __magic_name__				     =       checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_v''']
      __magic_name__				     =       checkpoint[f'''blocks.{i}.convs1.{j}.1.bias''']

      __magic_name__				     =       checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_g''']
      __magic_name__				     =       checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_v''']
      __magic_name__				     =       checkpoint[f'''blocks.{i}.convs2.{j}.1.bias''']

  __magic_name__				     =       checkpoint["""output_conv.1.weight_g"""]
  __magic_name__				     =       checkpoint["""output_conv.1.weight_v"""]
  __magic_name__				     =       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:
    __magic_name__				     =       SpeechTaHifiGanConfig.from_pretrained(A_					)
  else:
    __magic_name__				     =       SpeechTaHifiGanConfig()

  __magic_name__				     =       SpeechTaHifiGan(A_					)

  __magic_name__				     =       torch.load(A_					)
  load_weights(orig_checkpoint["""model"""]["""generator"""],     A_,     A_					)

  __magic_name__				     =       np.load(A_					)
  __magic_name__				     =       stats[0].reshape(-1					)
  __magic_name__				     =       stats[1].reshape(-1					)
  __magic_name__				     =       torch.from_numpy(A_					).float()
  __magic_name__				     =       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__":
    __lowerCAmelCase			:     Union[str, 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.'
    )

    __lowerCAmelCase			:     List[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,
    )







'''simple docstring'''







def 						snake_case_							(_a	:							str    ,   _a	:							str     ):
    UpperCAmelCase			       =		len(_a     ) + 1
    UpperCAmelCase			       =		len(_a     ) + 1

    # dp is a 2d matrix where dp[i][j] denotes whether prefix string of
    # length i of input_string matches with prefix string of length j of
    # given pattern.
    # "dp" stands for dynamic programming.
    UpperCAmelCase			       =		[[0 for i in range(_a     )] for j in range(_a     )]

    # since string of zero length match pattern of zero length
    UpperCAmelCase			       =		1

    # since pattern of zero length will never match with string of non-zero length
    for i in range(1    ,   _a     ):
        UpperCAmelCase			       =		0

    # since string of zero length will match with pattern where there
    # is at least one * alternatively
    for j in range(1    ,   _a     ):
        UpperCAmelCase			       =		dp[0][j - 2] if pattern[j - 1] == '''*''' else 0

    # now using bottom-up approach to find for all remaining lengths
    for i in range(1    ,   _a     ):
        for j in range(1    ,   _a     ):
            if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
                UpperCAmelCase			       =		dp[i - 1][j - 1]

            elif pattern[j - 1] == "*":
                if dp[i][j - 2] == 1:
                    UpperCAmelCase			       =		1
                elif pattern[j - 2] in (input_string[i - 1], "."):
                    UpperCAmelCase			       =		dp[i - 1][j]
                else:
                    UpperCAmelCase			       =		0
            else:
                UpperCAmelCase			       =		0

    return bool(dp[-1][-1]     )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   # inputing the strings
   # input_string = input("input a string :")
   # pattern = input("input a pattern :")

   A     ='aab'
   A     ='c*a*b'

   # using function to check whether given string matches the given pattern
   if match_pattern(input_string, pattern):
      print(f"""{input_string} matches the given pattern {pattern}""")
   else:
      print(f"""{input_string} does not match with the given pattern {pattern}""")




