

def 	__a     (      A__			:  int = 2000000       ):
    SCREAMING_SNAKE_CASE										=   [0 for i in range(n + 1       )]
    SCREAMING_SNAKE_CASE										=   1
    SCREAMING_SNAKE_CASE										=   1

    for i in range(2			,  int(n**0.5       ) + 1       ):
        if primality_list[i] == 0:
            for j in range(i * i			,  n + 1			,  A__       ):
                SCREAMING_SNAKE_CASE										=   1
    SCREAMING_SNAKE_CASE										=   0
    for i in range(A__       ):
        if primality_list[i] == 0:
            sum_of_primes += i
    return sum_of_primes


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





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,
  )


