src/transformers/models/xmod/convert_xmod_original_pytorch_checkpoint_to_pytorch.py/convert_xmod_checkpoint_to_pytorch
def convert_xmod_checkpoint_to_pytorch(
    xmod_checkpoint_path: str, pytorch_dump_folder_path: str, classification_head: bool
):
    data_dir = Path("data_bin")
    xmod = FairseqXmodModel.from_pretrained(
        model_name_or_path=str(Path(xmod_checkpoint_path).parent),
        checkpoint_file=Path(xmod_checkpoint_path).name,
        _name="xmod_base",
        arch="xmod_base",
        task="multilingual_masked_lm",
        data_name_or_path=str(data_dir),
        bpe="sentencepiece",
        sentencepiece_model=str(Path(xmod_checkpoint_path).parent / "sentencepiece.bpe.model"),
        src_dict=str(data_dir / "dict.txt"),
    )
    xmod.eval()  # disable dropout
    print(xmod)

    xmod_sent_encoder = xmod.model.encoder.sentence_encoder
    config = XmodConfig(
        vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings,
        hidden_size=xmod.cfg.model.encoder_embed_dim,
        num_hidden_layers=xmod.cfg.model.encoder_layers,
        num_attention_heads=xmod.cfg.model.encoder_attention_heads,
        intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim,
        max_position_embeddings=514,
        type_vocab_size=1,
        layer_norm_eps=1e-5,  # PyTorch default used in fairseq
        pre_norm=xmod.cfg.model.encoder_normalize_before,
        adapter_reduction_factor=getattr(xmod.cfg.model, "bottleneck", 2),
        adapter_layer_norm=xmod.cfg.model.adapter_layer_norm,
        adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm,
        ln_before_adapter=xmod.cfg.model.ln_before_adapter,
        languages=xmod.cfg.model.languages,
    )
    if classification_head:
        config.num_labels = xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

    print("Our X-MOD config:", config)

    model = XmodForSequenceClassification(config) if classification_head else XmodForMaskedLM(config)
    model.eval()

    # Now let's copy all the weights.
    # Embeddings
    model.roberta.embeddings.word_embeddings.weight = xmod_sent_encoder.embed_tokens.weight
    model.roberta.embeddings.position_embeddings.weight = xmod_sent_encoder.embed_positions.weight
    model.roberta.embeddings.token_type_embeddings.weight.data = torch.zeros_like(
        model.roberta.embeddings.token_type_embeddings.weight
    )  # just zero them out b/c xmod doesn't use them.

    model.roberta.embeddings.LayerNorm.weight = xmod_sent_encoder.layernorm_embedding.weight
    model.roberta.embeddings.LayerNorm.bias = xmod_sent_encoder.layernorm_embedding.bias

    for i in range(config.num_hidden_layers):
        # Encoder: start of layer
        layer = model.roberta.encoder.layer[i]
        xmod_layer = xmod_sent_encoder.layers[i]

        # self attention
        self_attn = layer.attention.self
        if not (
            xmod_layer.self_attn.k_proj.weight.data.shape
            == xmod_layer.self_attn.q_proj.weight.data.shape
            == xmod_layer.self_attn.v_proj.weight.data.shape
            == torch.Size((config.hidden_size, config.hidden_size))
        ):
            raise AssertionError("Dimensions of self-attention weights do not match.")

        self_attn.query.weight.data = xmod_layer.self_attn.q_proj.weight
        self_attn.query.bias.data = xmod_layer.self_attn.q_proj.bias
        self_attn.key.weight.data = xmod_layer.self_attn.k_proj.weight
        self_attn.key.bias.data = xmod_layer.self_attn.k_proj.bias
        self_attn.value.weight.data = xmod_layer.self_attn.v_proj.weight
        self_attn.value.bias.data = xmod_layer.self_attn.v_proj.bias

        # self-attention output
        self_output = layer.attention.output
        if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
            raise AssertionError("Dimensions of self-attention output weights do not match.")
        self_output.dense.weight = xmod_layer.self_attn.out_proj.weight
        self_output.dense.bias = xmod_layer.self_attn.out_proj.bias
        self_output.LayerNorm.weight = xmod_layer.self_attn_layer_norm.weight
        self_output.LayerNorm.bias = xmod_layer.self_attn_layer_norm.bias

        # intermediate
        intermediate = layer.intermediate
        if intermediate.dense.weight.shape != xmod_layer.fc1.weight.shape:
            raise AssertionError("Dimensions of intermediate weights do not match.")
        intermediate.dense.weight = xmod_layer.fc1.weight
        intermediate.dense.bias = xmod_layer.fc1.bias

        # output
        bert_output = layer.output
        if bert_output.dense.weight.shape != xmod_layer.fc2.weight.shape:
            raise AssertionError("Dimensions of feed-forward weights do not match.")
        bert_output.dense.weight = xmod_layer.fc2.weight
        bert_output.dense.bias = xmod_layer.fc2.bias
        bert_output.LayerNorm.weight = xmod_layer.final_layer_norm.weight
        bert_output.LayerNorm.bias = xmod_layer.final_layer_norm.bias
        if bert_output.adapter_layer_norm is not None:
            bert_output.adapter_layer_norm.weight = xmod_layer.adapter_layer_norm.weight
            bert_output.adapter_layer_norm.bias = xmod_layer.adapter_layer_norm.bias

        if sorted(bert_output.adapter_modules.keys()) != sorted(xmod_layer.adapter_modules.keys()):
            raise AssertionError("Lists of language adapters do not match.")
        for lang_code, adapter in xmod_layer.adapter_modules.items():
            to_adapter = bert_output.adapter_modules[lang_code]
            from_adapter = xmod_layer.adapter_modules[lang_code]
            to_adapter.dense1.weight = from_adapter.fc1.weight
            to_adapter.dense1.bias = from_adapter.fc1.bias
            to_adapter.dense2.weight = from_adapter.fc2.weight
            to_adapter.dense2.bias = from_adapter.fc2.bias

        # end of layer

    if xmod_sent_encoder.layer_norm is not None:
        model.roberta.encoder.LayerNorm.weight = xmod_sent_encoder.layer_norm.weight
        model.roberta.encoder.LayerNorm.bias = xmod_sent_encoder.layer_norm.bias

    if classification_head:
        model.classifier.dense.weight = xmod.model.classification_heads["mnli"].dense.weight
        model.classifier.dense.bias = xmod.model.classification_heads["mnli"].dense.bias
        model.classifier.out_proj.weight = xmod.model.classification_heads["mnli"].out_proj.weight
        model.classifier.out_proj.bias = xmod.model.classification_heads["mnli"].out_proj.bias
    else:
        # LM Head
        model.lm_head.dense.weight = xmod.model.encoder.lm_head.dense.weight
        model.lm_head.dense.bias = xmod.model.encoder.lm_head.dense.bias
        model.lm_head.layer_norm.weight = xmod.model.encoder.lm_head.layer_norm.weight
        model.lm_head.layer_norm.bias = xmod.model.encoder.lm_head.layer_norm.bias
        model.lm_head.decoder.weight = xmod.model.encoder.lm_head.weight
        model.lm_head.decoder.bias = xmod.model.encoder.lm_head.bias

    # Let's check that we get the same results.
    input_ids = xmod.encode(SAMPLE_TEXT).unsqueeze(0)  # batch of size 1
    model.roberta.set_default_language(SAMPLE_LANGUAGE)

    our_output = model(input_ids)[0]
    if classification_head:
        their_output = xmod.model.classification_heads["mnli"](xmod.extract_features(input_ids))
    else:
        their_output = xmod.model(input_ids, lang_id=[SAMPLE_LANGUAGE])[0]
    print(our_output.shape, their_output.shape)
    max_absolute_diff = torch.max(torch.abs(our_output - their_output)).item()
    print(f"max_absolute_diff = {max_absolute_diff}")  # ~ 1e-7
    success = torch.allclose(our_output, their_output, atol=1e-3)
    print("Do both models output the same tensors?", "🔥" if success else "💩")
    if not success:
        raise Exception("Something went wRoNg")

    Path(pytorch_dump_folder_path).mkdir(parents=True, exist_ok=True)
    print(f"Saving model to {pytorch_dump_folder_path}")
    model.save_pretrained(pytorch_dump_folder_path)