
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  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					# Load checkpoint
					lowercase					=		torch.load(lowerCAmelCase__				,    map_location='''cpu'''	)

					lowercase					=		chkpt['''model''']

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

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

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

					# Save pytorch-model
					lowercase					=		pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
					lowercase					=		pytorch_dump_folder_path + '''/''' + CONFIG_NAME
					lowercase					=		pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

					print(f'Save PyTorch model to {pytorch_weights_dump_path}'	)
					torch.save(lowerCAmelCase__				,    lowerCAmelCase__	)

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

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


if __name__ == "__main__":
							lowercase__	: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."
							)
							lowercase__	:Tuple										=					parser.parse_args()
							convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)






from numpy import exp, pi, sqrt



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__ = 0.0				,    lowerCAmelCase__ = 1.0	):
					'''simple docstring'''
					return 1 / sqrt(2 * pi * sigma**2	) * exp(-((x - mu) ** 2) / (2 * sigma**2)	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




