

'''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 	UpperCamelCase   (       lowercase_						:    Any     ,					lowercase_						:    int	)		->	Dict:




  '''simple docstring'''



  lowercase									=torch.load(lowercase_     ,					map_location='''cpu'''	)

  lowercase									=chkpt['''model''']

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

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

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

  # Save pytorch-model
  lowercase									=pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
  lowercase									=pytorch_dump_folder_path + '''/''' + CONFIG_NAME
  lowercase									=pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

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

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

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


if __name__ == "__main__":
     _UpperCAmelCase  :		Tuple  =		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.'''
     )
     _UpperCAmelCase  :		List[Any]  =		parser.parse_args()
     convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)







__lowerCamelCase			:	Any					    =	9.8_0_6_6_5



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase = g	)		->					float:

 if fluid_density <= 0:
  raise ValueError("Impossible fluid density"	)
 if volume < 0:
  raise ValueError("Impossible Object volume"	)
 if gravity <= 0:
  raise ValueError("Impossible Gravity"	)

 return fluid_density * gravity * volume


if __name__ == "__main__":
    import doctest

    # run doctest
    doctest.testmod()
