





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						(    UpperCAmelCase			,  UpperCAmelCase     )				->				Optional[int]:







   """simple docstring"""
   lowerCamelCase__   :						int					   =							torch.load(__snake_case			,  map_location='''cpu'''     )

   lowerCamelCase__   :						List[str]					   =							chkpt['model']

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

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

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

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











def      lowercase__      (     __snake_case    :							int  ):


					'''simple docstring'''


					UpperCAmelCase_    :      list[list[int]]            =						[[0 for _ in range(__snake_case  )] for _ in range(m + 1  )]
					for i in range(m + 1  ):
										UpperCAmelCase_    :      Optional[Any]            =						1

					for n in range(m + 1  ):
										for k in range(1     ,       __snake_case  ):
															memo[n][k] += memo[n][k - 1]
															if n - k > 0:
																				memo[n][k] += memo[n - k - 1][k]

					return memo[m][m - 1]


if __name__ == "__main__":
		import sys

		if len(sys.argv) == 1:
				try:
						__UpperCAmelCase								=						int(input('Enter a number: ').strip())
						print(partition(n))
				except ValueError:
						print('Please enter a number.')
		else:
				try:
						__UpperCAmelCase								=						int(sys.argv[1])
						print(partition(n))
				except ValueError:
						print('Please pass a number.')






