


import operator
def      snake_case_						( __lowercase			,       __lowercase = False			,       __lowercase = None       ):
   UpperCAmelCase_							:      Any						=   operator.lt if reverse else operator.gt
   UpperCAmelCase_							:      Optional[int]						=   solution or []

   if not arr:
      return solution

   UpperCAmelCase_							:      Union[str, Any]						=   [arr.pop(0       )]
   for i, item in enumerate(__lowercase       ):
      if _operator(__lowercase			,       sublist[-1]       ):
         sublist.append(__lowercase       )
         arr.pop(__lowercase       )

    #  merging sublist into solution list
   if not solution:
      solution.extend(__lowercase       )
   else:
      while sublist:
         UpperCAmelCase_							:      Any						=   sublist.pop(0       )
         for i, xx in enumerate(__lowercase       ):
            if not _operator(__lowercase			,       __lowercase       ):
               solution.insert(__lowercase			,       __lowercase       )
               break
         else:
            solution.append(__lowercase       )

   strand_sort(__lowercase			,       __lowercase			,       __lowercase       )
   return solution


if __name__ == "__main__":
       assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
       assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]



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      snake_case_						( __lowercase			,       __lowercase       ):
   # Load checkpoint
   UpperCAmelCase_							:      Tuple						=   torch.load(__lowercase			,       map_location='''cpu'''       )

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

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

   UpperCAmelCase_							:      int						=   chkpt['''params''']
   UpperCAmelCase_							:      Union[str, Any]						=   {n: v for n, v in config.items() if not isinstance(__lowercase			,       (torch.FloatTensor, numpy.ndarray)       )}

   UpperCAmelCase_							:      int						=   chkpt['''dico_word2id''']
   UpperCAmelCase_							:      List[Any]						=   {s + '''</w>''' if s.find('''@@'''       ) == -1 and i > 1_3 else s.replace('''@@'''			,       ''''''       ): i for s, i in vocab.items()}

   # Save pytorch-model
   UpperCAmelCase_							:      Tuple						=   pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
   UpperCAmelCase_							:      Tuple						=   pytorch_dump_folder_path + '''/''' + CONFIG_NAME
   UpperCAmelCase_							:      Dict						=   pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

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

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

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


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