'''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						(       a_       ,      a_		)      ->	Optional[Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       torch.load(a_       ,      map_location='cpu'		)

     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       chkpt['model']

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

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

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

     # Save pytorch-model
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       pytorch_dump_folder_path + '/' + WEIGHTS_NAME
     SCREAMING_SNAKE_CASE					:			Dict    	=       pytorch_dump_folder_path + '/' + CONFIG_NAME
     SCREAMING_SNAKE_CASE					:			Union[str, 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__":
 _lowerCAmelCase  :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."""
 )
 _lowerCAmelCase  :str  				=    parser.parse_args()
 convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)




'''simple docstring'''





def     __lowerCAmelCase						(       a_       ,      a_		)      ->	str:





     '''simple docstring'''





     if number < 0 or shift_amount < 0:
          raise ValueError('both inputs must be positive integers'		)

     SCREAMING_SNAKE_CASE					:			str    	=       str(bin(a_		)		)
     binary_number += "0" * shift_amount
     return binary_number


def     __lowerCAmelCase						(       a_       ,      a_		)      ->	str:





     '''simple docstring'''





     if number < 0 or shift_amount < 0:
          raise ValueError('both inputs must be positive integers'		)

     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       str(bin(a_		)		)[2:]
     if shift_amount >= len(a_		):
          return "0b0"
     SCREAMING_SNAKE_CASE					:			Dict    	=       binary_number[: len(a_		) - shift_amount]
     return "0b" + shifted_binary_number


def     __lowerCAmelCase						(       a_       ,      a_		)      ->	str:





     '''simple docstring'''





     if number >= 0:  # Get binary representation of positive number
          SCREAMING_SNAKE_CASE					:			Tuple    	=       '0' + str(bin(a_		)		).strip('-'		)[2:]
     else:  # Get binary (2's complement) representation of negative number
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       len(bin(a_		)[3:]		)  # Find 2's complement of number
          SCREAMING_SNAKE_CASE					:			Any    	=       bin(abs(a_		) - (1 << binary_number_length)		)[3:]
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       (
              '1' + '0' * (binary_number_length - len(a_		)) + binary_number
          )

     if shift_amount >= len(a_		):
          return "0b" + binary_number[0] * len(a_		)
     return (
         "0b"
         + binary_number[0] * shift_amount
         + binary_number[: len(a_		) - shift_amount]
     )


if __name__ == "__main__":
 import doctest

 doctest.testmod()



