src/transformers/models/fsmt/convert_fsmt_original_pytorch_checkpoint_to_pytorch.py/convert_fsmt_checkpoint_to_pytorch
def convert_fsmt_checkpoint_to_pytorch(fsmt_checkpoint_path, pytorch_dump_folder_path):
    # prep
    assert os.path.exists(fsmt_checkpoint_path)
    os.makedirs(pytorch_dump_folder_path, exist_ok=True)
    print(f"Writing results to {pytorch_dump_folder_path}")

    # handle various types of models

    checkpoint_file = basename(fsmt_checkpoint_path)
    fsmt_folder_path = dirname(fsmt_checkpoint_path)

    cls = fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
    models = cls.hub_models()
    kwargs = {"bpe": "fastbpe", "tokenizer": "moses"}
    data_name_or_path = "."
    # note: since the model dump is old, fairseq has upgraded its model some
    # time later, and it does a whole lot of rewrites and splits on the saved
    # weights, therefore we can't use torch.load() directly on the model file.
    # see: upgrade_state_dict(state_dict) in fairseq_model.py
    print(f"using checkpoint {checkpoint_file}")
    chkpt = hub_utils.from_pretrained(
        fsmt_folder_path, checkpoint_file, data_name_or_path, archive_map=models, **kwargs
    )

    args = vars(chkpt["args"]["model"])

    src_lang = args["source_lang"]
    tgt_lang = args["target_lang"]

    data_root = dirname(pytorch_dump_folder_path)
    model_dir = basename(pytorch_dump_folder_path)

    # dicts
    src_dict_file = os.path.join(fsmt_folder_path, f"dict.{src_lang}.txt")
    tgt_dict_file = os.path.join(fsmt_folder_path, f"dict.{tgt_lang}.txt")

    src_dict = Dictionary.load(src_dict_file)
    src_vocab = rewrite_dict_keys(src_dict.indices)
    src_vocab_size = len(src_vocab)
    src_vocab_file = os.path.join(pytorch_dump_folder_path, "vocab-src.json")
    print(f"Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records")
    with open(src_vocab_file, "w", encoding="utf-8") as f:
        f.write(json.dumps(src_vocab, ensure_ascii=False, indent=json_indent))

    # detect whether this is a do_lower_case situation, which can be derived by checking whether we
    # have at least one uppercase letter in the source vocab
    do_lower_case = True
    for k in src_vocab.keys():
        if not k.islower():
            do_lower_case = False
            break

    tgt_dict = Dictionary.load(tgt_dict_file)
    tgt_vocab = rewrite_dict_keys(tgt_dict.indices)
    tgt_vocab_size = len(tgt_vocab)
    tgt_vocab_file = os.path.join(pytorch_dump_folder_path, "vocab-tgt.json")
    print(f"Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records")
    with open(tgt_vocab_file, "w", encoding="utf-8") as f:
        f.write(json.dumps(tgt_vocab, ensure_ascii=False, indent=json_indent))

    # merges_file (bpecodes)
    merges_file = os.path.join(pytorch_dump_folder_path, VOCAB_FILES_NAMES["merges_file"])
    for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
        fsmt_merges_file = os.path.join(fsmt_folder_path, fn)
        if os.path.exists(fsmt_merges_file):
            break
    with open(fsmt_merges_file, encoding="utf-8") as fin:
        merges = fin.read()
    merges = re.sub(r" \d+$", "", merges, 0, re.M)  # remove frequency number
    print(f"Generating {merges_file}")
    with open(merges_file, "w", encoding="utf-8") as fout:
        fout.write(merges)

    # model config
    fsmt_model_config_file = os.path.join(pytorch_dump_folder_path, "config.json")

    # validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
    # may have to modify the tokenizer if a different type is used by a future model
    assert args["bpe"] == "fastbpe", f"need to extend tokenizer to support bpe={args['bpe']}"
    assert args["tokenizer"] == "moses", f"need to extend tokenizer to support bpe={args['tokenizer']}"

    model_conf = {
        "architectures": ["FSMTForConditionalGeneration"],
        "model_type": "fsmt",
        "activation_dropout": args["activation_dropout"],
        "activation_function": "relu",
        "attention_dropout": args["attention_dropout"],
        "d_model": args["decoder_embed_dim"],
        "dropout": args["dropout"],
        "init_std": 0.02,
        "max_position_embeddings": args["max_source_positions"],
        "num_hidden_layers": args["encoder_layers"],
        "src_vocab_size": src_vocab_size,
        "tgt_vocab_size": tgt_vocab_size,
        "langs": [src_lang, tgt_lang],
        "encoder_attention_heads": args["encoder_attention_heads"],
        "encoder_ffn_dim": args["encoder_ffn_embed_dim"],
        "encoder_layerdrop": args["encoder_layerdrop"],
        "encoder_layers": args["encoder_layers"],
        "decoder_attention_heads": args["decoder_attention_heads"],
        "decoder_ffn_dim": args["decoder_ffn_embed_dim"],
        "decoder_layerdrop": args["decoder_layerdrop"],
        "decoder_layers": args["decoder_layers"],
        "bos_token_id": 0,
        "pad_token_id": 1,
        "eos_token_id": 2,
        "is_encoder_decoder": True,
        "scale_embedding": not args["no_scale_embedding"],
        "tie_word_embeddings": args["share_all_embeddings"],
    }

    # good hparam defaults to start with
    model_conf["num_beams"] = 5
    model_conf["early_stopping"] = False
    if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
        model_conf["length_penalty"] = best_score_hparams[model_dir]["length_penalty"]
    else:
        model_conf["length_penalty"] = 1.0

    print(f"Generating {fsmt_model_config_file}")
    with open(fsmt_model_config_file, "w", encoding="utf-8") as f:
        f.write(json.dumps(model_conf, ensure_ascii=False, indent=json_indent))

    # tokenizer config
    fsmt_tokenizer_config_file = os.path.join(pytorch_dump_folder_path, TOKENIZER_CONFIG_FILE)

    tokenizer_conf = {
        "langs": [src_lang, tgt_lang],
        "model_max_length": 1024,
        "do_lower_case": do_lower_case,
    }

    print(f"Generating {fsmt_tokenizer_config_file}")
    with open(fsmt_tokenizer_config_file, "w", encoding="utf-8") as f:
        f.write(json.dumps(tokenizer_conf, ensure_ascii=False, indent=json_indent))

    # model
    model = chkpt["models"][0]
    model_state_dict = model.state_dict()

    # rename keys to start with 'model.'
    model_state_dict = OrderedDict(("model." + k, v) for k, v in model_state_dict.items())

    # remove unneeded keys
    ignore_keys = [
        "model.model",
        "model.encoder.version",
        "model.decoder.version",
        "model.encoder_embed_tokens.weight",
        "model.decoder_embed_tokens.weight",
        "model.encoder.embed_positions._float_tensor",
        "model.decoder.embed_positions._float_tensor",
    ]
    for k in ignore_keys:
        model_state_dict.pop(k, None)

    config = FSMTConfig.from_pretrained(pytorch_dump_folder_path)
    model_new = FSMTForConditionalGeneration(config)

    # check that it loads ok
    model_new.load_state_dict(model_state_dict, strict=False)

    # save
    pytorch_weights_dump_path = os.path.join(pytorch_dump_folder_path, WEIGHTS_NAME)
    print(f"Generating {pytorch_weights_dump_path}")
    torch.save(model_state_dict, pytorch_weights_dump_path)

    print("Conversion is done!")
    print("\nLast step is to upload the files to s3")
    print(f"cd {data_root}")
    print(f"transformers-cli upload {model_dir}")