






'''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 							lowercase_				(    __A :       str					,      __A :       List[str]			)       ->      Tuple:



 """simple docstring"""




 lowercase					:   int				  =torch.load(__A					,      map_location='''cpu'''			)

 lowercase					:   str				  =chkpt['''model''']

 # We have the base model one level deeper than the original XLM repository
 lowercase					:   Tuple				  ={}
 for k, v in state_dict.items():
  if "pred_layer" in k:
   lowercase					:   str				  =v
  else:
   lowercase					:   Tuple				  =v

 lowercase					:   Union[str, Any]				  =chkpt['''params''']
 lowercase					:   List[str]				  ={n: v for n, v in config.items() if not isinstance(__A					,      (torch.FloatTensor, numpy.ndarray)			)}

 lowercase					:   str				  =chkpt['''dico_word2id''']
 lowercase					:   int				  ={s + '''</w>''' if s.find('''@@'''			) == -1 and i > 1_3 else s.replace('''@@'''					,      ''''''			): i for s, i in vocab.items()}

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

 print(F'Save PyTorch model to {pytorch_weights_dump_path}'			)
 torch.save(__A					,      __A			)

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

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


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE						=	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.'
      )
      SCREAMING_SNAKE_CASE						=	parser.parse_args()
      convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)











'''simple docstring'''



def 							lowercase_				(    __A :       int = 6_0_0_8_5_1_4_7_5_1_4_3			)       ->      int:



 """simple docstring"""




 try:
  lowercase					:   Any				  =int(__A			)
 except (TypeError, ValueError):
  raise TypeError('''Parameter n must be int or castable to int.'''			)
 if n <= 0:
  raise ValueError('''Parameter n must be greater than or equal to one.'''			)
 lowercase					:   Optional[Any]				  =1
 lowercase					:   Dict				  =2
 while i * i <= n:
  while n % i == 0:
   lowercase					:   Optional[int]				  =i
   n //= i
  i += 1
 if n > 1:
  lowercase					:   Dict				  =n
 return int(__A			)


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



