



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     (       snake_case__	:   List[str]	,					snake_case__	:   str     )     ->					int:
   # Load checkpoint
   UpperCamelCase   :  Union[str, Any]						     =     torch.load(snake_case__	,					map_location='cpu'     )

   UpperCamelCase   :  Optional[Any]						     =     chkpt['model']

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

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

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

   # Save pytorch-model
   UpperCamelCase   :  Tuple						     =     pytorch_dump_folder_path + '/' + WEIGHTS_NAME
   UpperCamelCase   :  List[str]						     =     pytorch_dump_folder_path + '/' + CONFIG_NAME
   UpperCamelCase   :  Any						     =     pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['vocab_file']

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

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

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


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








from graphs.minimum_spanning_tree_kruskal import kruskal
def 		UpperCamelCase     (       )     ->					Tuple:
   UpperCamelCase   :  List[str]						     =     9
   UpperCamelCase   :  Optional[Any]						     =     [
       [0, 1, 4],
       [0, 7, 8],
       [1, 2, 8],
       [7, 8, 7],
       [7, 6, 1],
       [2, 8, 2],
       [8, 6, 6],
       [2, 3, 7],
       [2, 5, 4],
       [6, 5, 2],
       [3, 5, 14],
       [3, 4, 9],
       [5, 4, 10],
       [1, 7, 11],
   ]

   UpperCamelCase   :  int						     =     kruskal(snake_case__	,					snake_case__     )

   UpperCamelCase   :  List[str]						     =     [
       [7, 6, 1],
       [2, 8, 2],
       [6, 5, 2],
       [0, 1, 4],
       [2, 5, 4],
       [2, 3, 7],
       [0, 7, 8],
       [3, 4, 9],
   ]

   assert sorted(snake_case__     ) == sorted(snake_case__     )



