
'''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 :			Dict				,      __SCREAMING_SNAKE_CASE :			Tuple							):



						"""simple docstring"""





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

						lowercase_     :					List[Any]													=	chkpt["""model"""]

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

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

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

						# Save pytorch-model
						lowercase_     :					Optional[Any]													=	pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
						lowercase_     :					List[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			:  Optional[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			:  Optional[Any]   							=	parser.parse_args()
							convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)





'''simple docstring'''







from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			Optional[str] = None							):



						"""simple docstring"""





						if version.parse(hfh.__version__							).release < version.parse('''0.11.0'''							).release:
												# old versions of hfh don't url-encode the file path
												lowercase_     :					int													=	quote(__SCREAMING_SNAKE_CASE							)
						return hfh.hf_hub_url(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE				,				repo_type='''dataset'''				,				revision=__SCREAMING_SNAKE_CASE							)



