"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
import argparse
import json
import numpy
import torch
from sapiens_transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from sapiens_transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def convert_xlm_checkpoint_to_pytorch(xlm_checkpoint_path, pytorch_dump_folder_path):
    chkpt = torch.load(xlm_checkpoint_path, map_location="cpu")
    state_dict = chkpt["model"]
    two_levels_state_dict = {}
    for k, v in state_dict.items():
        if "pred_layer" in k: two_levels_state_dict[k] = v
        else: two_levels_state_dict["transformer." + k] = v
    config = chkpt["params"]
    config = {n: v for n, v in config.items() if not isinstance(v, (torch.FloatTensor, numpy.ndarray))}
    vocab = chkpt["dico_word2id"]
    vocab = {s + "</w>" if s.find("@@") == -1 and i > 13 else s.replace("@@", ""): i for s, i in vocab.items()}
    pytorch_weights_dump_path = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
    pytorch_config_dump_path = pytorch_dump_folder_path + "/" + CONFIG_NAME
    pytorch_vocab_dump_path = pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["vocab_file"]
    print(f"Save PyTorch model to {pytorch_weights_dump_path}")
    torch.save(two_levels_state_dict, pytorch_weights_dump_path)
    print(f"Save configuration file to {pytorch_config_dump_path}")
    with open(pytorch_config_dump_path, "w", encoding="utf-8") as f: f.write(json.dumps(config, indent=2) + "\n")
    print(f"Save vocab file to {pytorch_config_dump_path}")
    with open(pytorch_vocab_dump_path, "w", encoding="utf-8") as f: f.write(json.dumps(vocab, indent=2) + "\n")
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    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.")
    args = parser.parse_args()
    convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
