
"""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_				)->							Dict:







       """simple docstring"""
       # Load checkpoint
       UpperCamelCase					 =		torch.load(__A		,      map_location="cpu"				)

       UpperCamelCase					 =		chkpt["model"]

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

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

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

       # Save pytorch-model
       UpperCamelCase					 =		pytorch_dump_folder_path + "/" + WEIGHTS_NAME
       UpperCamelCase					 =		pytorch_dump_folder_path + "/" + CONFIG_NAME
       UpperCamelCase					 =		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 	lowerCamelCase__			(							UpperCAmelCase_				)->							int:







       """simple docstring"""
       if a < 0:
              raise ValueError("Input value must be a positive integer"				)
       elif isinstance(UpperCAmelCase_		,      UpperCAmelCase_				):
              raise TypeError("Input value must be a 'int' type"				)
       return bin(UpperCAmelCase_				).count("1"				)


if __name__ == "__main__":
      import doctest

      doctest.testmod()





