





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     a_       (					lowerCAmelCase_ :     Dict,      lowerCAmelCase_ :     Tuple      ):
   # Load checkpoint
   __lowerCAmelCase				      =      torch.load(lowerCAmelCase_,      map_location='cpu'      )

   __lowerCAmelCase				      =      chkpt['model']

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

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

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

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

   print(F"""Save PyTorch model to {pytorch_weights_dump_path}"""      )
   torch.save(lowerCAmelCase_,      lowerCAmelCase_      )

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

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


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




import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp
def 							__SCREAMING_SNAKE_CASE     (   )					->				Tuple:
	__A :			List[Any]		    =			ArgumentParser(
	    description=(
	        """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
	    )       )

	# Optional arguments for the launch helper
	parser.add_argument("""--num_cores"""			,type=a__			,default=1			,help="""Number of TPU cores to use (1 or 8)."""       )

	# positional
	parser.add_argument(
	    """training_script"""			,type=a__			,help=(
	        """The full path to the single TPU training """
	        """program/script to be launched in parallel, """
	        """followed by all the arguments for the """
	        """training script"""
	    )			,)

	# rest from the training program
	parser.add_argument("""training_script_args"""			,nargs=a__       )

	return parser.parse_args()
def 							__SCREAMING_SNAKE_CASE     (   )					->				str:
	__A :			Union[str, Any]		    =			parse_args()

	# Import training_script as a module.
	__A :			List[Any]		    =			Path(args.training_script       )
	sys.path.append(str(script_fpath.parent.resolve()       )       )
	__A :			str		    =			script_fpath.stem
	__A :			int		    =			importlib.import_module(a__       )

	# Patch sys.argv
	__A :			List[str]		    =			[args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores       )]

	xmp.spawn(mod._mp_fn			,args=()			,nprocs=args.num_cores       )


if __name__ == "__main__":
			main()





