






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 					lowercase_ (    __snake_case     :							str	,	__snake_case     :							Any      )     -> Optional[int]:





				'''simple docstring'''


				snake_case__      :Tuple         =   torch.load(UpperCamelCase__	,	map_location="cpu"      )

				snake_case__      :Union[str, Any]         =   chkpt["model"]

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

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

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

				# Save pytorch-model
				snake_case__      :List[Any]         =   pytorch_dump_folder_path + "/" + WEIGHTS_NAME
				snake_case__      :Any         =   pytorch_dump_folder_path + "/" + CONFIG_NAME
				snake_case__      :Optional[int]         =   pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["vocab_file"]

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

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

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


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







def 					lowercase_ (    __snake_case     :							list      )     -> list:





				'''simple docstring'''


				if any(not isinstance(__snake_case	,	__snake_case      ) or x < 0 for x in sequence      ):
								raise TypeError("Sequence must be list of non-negative integers"      )
				for _ in range(len(__snake_case      )      ):
								for i, (rod_upper, rod_lower) in enumerate(zip(__snake_case	,	sequence[1:]      )      ):
												if rod_upper > rod_lower:
																sequence[i] -= rod_upper - rod_lower
																sequence[i + 1] += rod_upper - rod_lower
				return sequence


if __name__ == "__main__":
	assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
	assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]