





'''simple docstring'''






import random



def        UpperCamelCase__		(						__magic_name__  :   int      )				->			bool:
 '''simple docstring'''






 snake_case__     :					List[str]    =  num - 1
 snake_case__     :					List[Any]    =  0

 while s % 2 == 0:
  snake_case__     :					Optional[Any]    =  s // 2
  t += 1

 for _ in range(5      ):
  snake_case__     :					Any    =  random.randrange(2		,       num - 1      )
  snake_case__     :					Optional[Any]    =  pow(__magic_name__		,       __magic_name__		,       __magic_name__      )
  if v != 1:
   snake_case__     :					int    =  0
   while v != (num - 1):
    if i == t - 1:
     return False
    else:
     snake_case__     :					int    =  i + 1
     snake_case__     :					str    =  (v**2) % num
 return True



def        UpperCamelCase__		(						__magic_name__  :   int      )				->			bool:
 '''simple docstring'''






 if num < 2:
  return False

 snake_case__     :					Dict    =  [
     2,
     3,
     5,
     7,
     11,
     13,
     17,
     19,
     23,
     29,
     31,
     37,
     41,
     43,
     47,
     53,
     59,
     61,
     67,
     71,
     73,
     79,
     83,
     89,
     97,
     1_01,
     1_03,
     1_07,
     1_09,
     1_13,
     1_27,
     1_31,
     1_37,
     1_39,
     1_49,
     1_51,
     1_57,
     1_63,
     1_67,
     1_73,
     1_79,
     1_81,
     1_91,
     1_93,
     1_97,
     1_99,
     2_11,
     2_23,
     2_27,
     2_29,
     2_33,
     2_39,
     2_41,
     2_51,
     2_57,
     2_63,
     2_69,
     2_71,
     2_77,
     2_81,
     2_83,
     2_93,
     3_07,
     3_11,
     3_13,
     3_17,
     3_31,
     3_37,
     3_47,
     3_49,
     3_53,
     3_59,
     3_67,
     3_73,
     3_79,
     3_83,
     3_89,
     3_97,
     4_01,
     4_09,
     4_19,
     4_21,
     4_31,
     4_33,
     4_39,
     4_43,
     4_49,
     4_57,
     4_61,
     4_63,
     4_67,
     4_79,
     4_87,
     4_91,
     4_99,
     5_03,
     5_09,
     5_21,
     5_23,
     5_41,
     5_47,
     5_57,
     5_63,
     5_69,
     5_71,
     5_77,
     5_87,
     5_93,
     5_99,
     6_01,
     6_07,
     6_13,
     6_17,
     6_19,
     6_31,
     6_41,
     6_43,
     6_47,
     6_53,
     6_59,
     6_61,
     6_73,
     6_77,
     6_83,
     6_91,
     7_01,
     7_09,
     7_19,
     7_27,
     7_33,
     7_39,
     7_43,
     7_51,
     7_57,
     7_61,
     7_69,
     7_73,
     7_87,
     7_97,
     8_09,
     8_11,
     8_21,
     8_23,
     8_27,
     8_29,
     8_39,
     8_53,
     8_57,
     8_59,
     8_63,
     8_77,
     8_81,
     8_83,
     8_87,
     9_07,
     9_11,
     9_19,
     9_29,
     9_37,
     9_41,
     9_47,
     9_53,
     9_67,
     9_71,
     9_77,
     9_83,
     9_91,
     9_97,
 ]

 if num in low_primes:
  return True

 for prime in low_primes:
  if (num % prime) == 0:
   return False

 return rabin_miller(__magic_name__      )



def        UpperCamelCase__		(						__magic_name__  :   int = 10_24      )				->			int:
 '''simple docstring'''






 while True:
  snake_case__     :					Optional[Any]    =  random.randrange(2 ** (keysize - 1)		,       2 ** (keysize)      )
  if is_prime_low_num(__magic_name__      ):
   return num


if __name__ == "__main__":
    A_		:  Dict		 =						generate_large_prime()
    print(("Prime number:", num))
    print(("is_prime_low_num:", is_prime_low_num(num)))










import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_lowerCamelCase      :						int							=  logging.get_logger("""transformers.models.speecht5""")







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_		)  ->			Tuple:



       """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_					,							lowercase_					,							lowercase_					,							lowercase_=None					,							lowercase_=None					,							)  ->			str:



       """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__":
  _lowerCamelCase      :						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[str]							=  parser.parse_args()
  convert_hifigan_checkpoint(
      args.checkpoint_path,
      args.stats_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.push_to_hub,
  )


