
'''simple docstring'''







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_		(    __SCREAMING_SNAKE_CASE :			List[str]				,				__SCREAMING_SNAKE_CASE :			Dict							):



						"""simple docstring"""





						lowercase_     :					str													=	torch.load(__SCREAMING_SNAKE_CASE				,				map_location='''cpu'''							)

						lowercase_     :					Dict													=	chkpt['''model''']

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

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

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

						# Save pytorch-model
						lowercase_     :					str													=	pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
						lowercase_     :					Any													=	pytorch_dump_folder_path + '''/''' + CONFIG_NAME
						lowercase_     :					Dict													=	pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

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

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

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


if __name__ == "__main__":
							_lowercase			:  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."
							)
							_lowercase			:  Optional[int]   							=	parser.parse_args()
							convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)



'''simple docstring'''







def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str							):



						"""simple docstring"""





						return [
						    txt[:a] + txt[a].upper() + txt[a + 1 :]
						    for a in range(len(__SCREAMING_SNAKE_CASE							)							)
						    if txt[a].isalpha()
						]


if __name__ == "__main__":
							__import__("doctest").testmod()



