
"""simple docstring"""






import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowerCAmelCase_			=							logging.get_logger('''transformers.models.speecht5''')

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       List[str]:






       """simple docstring"""




       hf_model.apply_weight_norm()

       _UpperCAmelCase     =     checkpoint['input_conv.weight_g']
       _UpperCAmelCase     =     checkpoint['input_conv.weight_v']
       _UpperCAmelCase     =     checkpoint['input_conv.bias']

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

                     _UpperCAmelCase     =     checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
                     _UpperCAmelCase     =     checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
                     _UpperCAmelCase     =     checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

       _UpperCAmelCase     =     checkpoint['output_conv.1.weight_g']
       _UpperCAmelCase     =     checkpoint['output_conv.1.weight_v']
       _UpperCAmelCase     =     checkpoint['output_conv.1.bias']

       hf_model.remove_weight_norm()

@torch.no_grad()
def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE=None,SCREAMING_SNAKE_CASE=None,)		->       Any:






       """simple docstring"""




       if config_path is not None:
              _UpperCAmelCase     =     SpeechTaHifiGanConfig.from_pretrained(SCREAMING_SNAKE_CASE							)
       else:
              _UpperCAmelCase     =     SpeechTaHifiGanConfig()

       _UpperCAmelCase     =     SpeechTaHifiGan(SCREAMING_SNAKE_CASE							)

       _UpperCAmelCase     =     torch.load(SCREAMING_SNAKE_CASE							)
       load_weights(orig_checkpoint['model']['generator'],SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)

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

       model.save_pretrained(SCREAMING_SNAKE_CASE							)

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


if __name__ == "__main__":
    lowerCAmelCase_			=							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_			=							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"""






import sys

lowerCAmelCase_			=							(
    '''73167176531330624919225119674426574742355349194934'''
    '''96983520312774506326239578318016984801869478851843'''
    '''85861560789112949495459501737958331952853208805511'''
    '''12540698747158523863050715693290963295227443043557'''
    '''66896648950445244523161731856403098711121722383113'''
    '''62229893423380308135336276614282806444486645238749'''
    '''30358907296290491560440772390713810515859307960866'''
    '''70172427121883998797908792274921901699720888093776'''
    '''65727333001053367881220235421809751254540594752243'''
    '''52584907711670556013604839586446706324415722155397'''
    '''53697817977846174064955149290862569321978468622482'''
    '''83972241375657056057490261407972968652414535100474'''
    '''82166370484403199890008895243450658541227588666881'''
    '''16427171479924442928230863465674813919123162824586'''
    '''17866458359124566529476545682848912883142607690042'''
    '''24219022671055626321111109370544217506941658960408'''
    '''07198403850962455444362981230987879927244284909188'''
    '''84580156166097919133875499200524063689912560717606'''
    '''05886116467109405077541002256983155200055935729725'''
    '''71636269561882670428252483600823257530420752963450'''
)

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       int:






       """simple docstring"""




       _UpperCAmelCase     =     1
       for digit in s:
              product *= int(SCREAMING_SNAKE_CASE							)
       return product

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE = N							)		->       int:






       """simple docstring"""




       _UpperCAmelCase     =     -sys.maxsize - 1
       _UpperCAmelCase     =     n[:13]
       _UpperCAmelCase     =     13
       while cur_index < len(SCREAMING_SNAKE_CASE							) - 13:
              if int(n[cur_index]							) >= int(substr[0]							):
                     _UpperCAmelCase     =     substr[1:] + n[cur_index]
                     cur_index += 1
              else:
                     _UpperCAmelCase     =     max(SCREAMING_SNAKE_CASE,str_eval(SCREAMING_SNAKE_CASE							)							)
                     _UpperCAmelCase     =     n[cur_index : cur_index + 13]
                     cur_index += 13
       return largest_product


if __name__ == "__main__":
    print(F'''{solution() = }''')






