"""
    #################################################################################################################################################################################################
    # 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
from argparse import Namespace
import torch
from torch import nn
from sapiens_transformers import XGLMConfig, XGLMForCausalLM
def remove_ignore_keys_(state_dict):
    ignore_keys = ["decoder.version", "decoder.output_projection.weight", "_float_tensor", "decoder.embed_positions._float_tensor"]
    for k in ignore_keys: state_dict.pop(k, None)
def make_linear_from_emb(emb):
    vocab_size, emb_size = emb.weight.shape
    lin_layer = nn.Linear(vocab_size, emb_size, bias=False)
    lin_layer.weight.data = emb.weight.data
    return lin_layer
def convert_fairseq_xglm_checkpoint_from_disk(checkpoint_path):
    checkpoint = torch.load(checkpoint_path, map_location="cpu")
    args = Namespace(**checkpoint["cfg"]["model"])
    state_dict = checkpoint["model"]
    remove_ignore_keys_(state_dict)
    vocab_size = state_dict["decoder.embed_tokens.weight"].shape[0]
    state_dict = {key.replace("decoder", "model"): val for key, val in state_dict.items()}
    config = XGLMConfig(vocab_size=vocab_size, max_position_embeddings=args.max_target_positions, num_layers=args.decoder_layers, attention_heads=args.decoder_attention_heads,
    ffn_dim=args.decoder_ffn_embed_dim, d_model=args.decoder_embed_dim, layerdrop=args.decoder_layerdrop, dropout=args.dropout, attention_dropout=args.attention_dropout,
    activation_dropout=args.activation_dropout, activation_function="gelu", scale_embedding=not args.no_scale_embedding, tie_word_embeddings=args.share_decoder_input_output_embed)
    model = XGLMForCausalLM(config)
    missing = model.load_state_dict(state_dict, strict=False)
    print(missing)
    model.lm_head = make_linear_from_emb(model.model.embed_tokens)
    return model
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
    parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
    args = parser.parse_args()
    model = convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
    model.save_pretrained(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.                                                                 #
    #################################################################################################################################################################################################
"""
