




'''simple docstring'''


def 						snake_case_       (UpperCamelCase			:    list[int] ):





							'''simple docstring'''


							if not numbers:
														return 0

							if not isinstance(UpperCamelCase					,		(list, tuple) ) or not all(
							    isinstance(UpperCamelCase					,		UpperCamelCase ) for number in numbers ):
														raise ValueError('''numbers must be an iterable of integers''' )

							_a      	=							_a      	=							_a      	=							numbers[0]

							for i in range(1					,		len(UpperCamelCase ) ):
														# update the maximum and minimum subarray products
														_a      	=							numbers[i]
														if number < 0:
																					_a	,				_a      	=							min_till_now, max_till_now
														_a      	=							max(UpperCamelCase					,		max_till_now * number )
														_a      	=							min(UpperCamelCase					,		min_till_now * number )

														# update the maximum product found till now
														_a      	=							max(UpperCamelCase					,		UpperCamelCase )

							return max_prod


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		(      _A				,					_A       ):



 """simple docstring"""


 __a							=	torch.load(_A				,					map_location="cpu"       )

 __a							=	chkpt["model"]

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

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

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

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


