'''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__       :					Optional[int]      ,      UpperCAmelCase__       :					Optional[int] )			->      Union[str, Any]:
							# Load checkpoint
							lowercase_					:			str										=       torch.load(UpperCAmelCase__      ,      map_location="""cpu""" )

							lowercase_					:			int										=       chkpt["""model"""]

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

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

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

							# Save pytorch-model
							lowercase_					:			Any										=       pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
							lowercase_					:			Dict										=       pytorch_dump_folder_path + """/""" + CONFIG_NAME
							lowercase_					:			Optional[int]										=       pytorch_dump_folder_path + """/""" + VOCAB_FILES_NAMES["""vocab_file"""]

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

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

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


if __name__ == "__main__":
						_lowercase						: Dict						=						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."
						)
						_lowercase						: Any						=						parser.parse_args()
						convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)






'''simple docstring'''




def 							lowerCamelCase	(      UpperCAmelCase__       :					int      ,      UpperCAmelCase__       :					int )			->      bool:
							return numa ^ numa < 0


if __name__ == "__main__":
						import doctest

						doctest.testmod()





