





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__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     Tuple:
		# Load checkpoint
		lowerCamelCase   : str								=torch.load(_lowercase							,       map_location='''cpu'''		)

		lowerCamelCase   : Union[str, Any]								=chkpt['model']

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

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

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

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








def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     bool:
		lowerCamelCase   : Optional[Any]								=[int(SCREAMING_SNAKE_CASE_		) for i in ip_va_address.split('''.'''		) if i.isdigit()]
		return len(SCREAMING_SNAKE_CASE_		) == 4 and all(0 <= int(SCREAMING_SNAKE_CASE_		) <= 2_5_4 for octet in octets		)


if __name__ == "__main__":
				snake_case_							=			input().strip()
				snake_case_							=			'''valid''' if is_ip_va_address_valid(ip) else '''invalid'''
				print(F"""{ip} is a {valid_or_invalid} IP v4 address.""")

