
"""simple docstring"""




import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			str       =     torch.load(SCREAMING_SNAKE_CASE__ ,				map_location='''cpu'''						)

  _lowercase:			Tuple       =     chkpt["""model"""]

  # We have the base model one level deeper than the original XLM repository
  _lowercase:			int       =     {}
  for k, v in state_dict.items():
    if "pred_layer" in k:
      _lowercase:			Any       =     v
    else:
      _lowercase:			Optional[int]       =     v

  _lowercase:			int       =     chkpt["""params"""]
  _lowercase:			List[Any]       =     {n: v for n, v in config.items() if not isinstance(SCREAMING_SNAKE_CASE__ ,				(torch.FloatTensor, numpy.ndarray)						)}

  _lowercase:			List[str]       =     chkpt["""dico_word2id"""]
  _lowercase:			Any       =     {s + """</w>""" if s.find('''@@'''						) == -1 and i > 13 else s.replace('''@@''' ,				''''''						): i for s, i in vocab.items()}

  # Save pytorch-model
  _lowercase:			Optional[int]       =     pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
  _lowercase:			int       =     pytorch_dump_folder_path + """/""" + CONFIG_NAME
  _lowercase:			Union[str, Any]       =     pytorch_dump_folder_path + """/""" + VOCAB_FILES_NAMES["""vocab_file"""]

  print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''						)
  torch.save(SCREAMING_SNAKE_CASE__ ,				SCREAMING_SNAKE_CASE__						)

  print(f'''Save configuration file to {pytorch_config_dump_path}'''						)
  with open(SCREAMING_SNAKE_CASE__ ,				'''w''' ,				encoding='''utf-8'''						) as f:
    f.write(json.dumps(SCREAMING_SNAKE_CASE__ ,				indent=2						) + '''\n'''						)

  print(f'''Save vocab file to {pytorch_config_dump_path}'''						)
  with open(SCREAMING_SNAKE_CASE__ ,				'''w''' ,				encoding='''utf-8'''						) as f:
    f.write(json.dumps(SCREAMING_SNAKE_CASE__ ,				indent=2						) + '''\n'''						)


if __name__ == "__main__":
      A__   :					int							=   argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--xlm_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
      )
      parser.add_argument(
          '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
      )
      A__   :					str							=   parser.parse_args()
      convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)


"""simple docstring"""




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  if num <= 0:
    raise ValueError('''Input must be a positive integer'''						)

  _lowercase:			Tuple       =     [True] * (num + 1)

  _lowercase:			List[str]       =     2
  while p * p <= num:
    if primes[p]:
      for i in range(p * p ,				num + 1 ,				_UpperCamelCase						):
        _lowercase:			List[str]       =     False
    p += 1

  return [prime for prime in range(2 ,				num + 1						) if primes[prime]]


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  A__   :					List[Any]							=   int(input('Enter a positive integer: ').strip())
  print(prime_sieve_eratosthenes(user_num))
