def METHOD_NAME(
    roberta_checkpoint_path: str, pytorch_dump_folder_path: str, classification_head: bool
):
    """
    Copy/paste/tweak roberta's weights to our BERT structure.
    """
    roberta = FairseqRobertaModel.from_pretrained(roberta_checkpoint_path)
    roberta.eval()  # disable dropout
    roberta_sent_encoder = roberta.model.decoder.sentence_encoder
    config = RobertaConfig(
        vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings,
        hidden_size=roberta.args.encoder_embed_dim,
        num_hidden_layers=roberta.args.encoder_layers,
        num_attention_heads=roberta.args.encoder_attention_heads,
        intermediate_size=roberta.args.encoder_ffn_embed_dim,
        max_position_embeddings=514,
        type_vocab_size=1,
        layer_norm_eps=1e-5,  # PyTorch default used in fairseq
    )
    if classification_head:
        config.num_labels = roberta.args.num_classes
    print("Our BERT config:", config)
    model = RobertaForSequenceClassification(config) if classification_head else RobertaForMaskedLM(config)
    model.eval()
    # Now let's copy all the weights.
    # Embeddings
    model.roberta.embeddings.word_embeddings.weight = roberta_sent_encoder.embed_tokens.weight
    model.roberta.embeddings.position_embeddings.weight = roberta_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 RoBERTa doesn't use them.
    model.roberta.embeddings.LayerNorm.weight = roberta_sent_encoder.emb_layer_norm.weight
    model.roberta.embeddings.LayerNorm.bias = roberta_sent_encoder.emb_layer_norm.bias
    for i in range(config.num_hidden_layers):
        # Encoder: start of layer
        layer: BertLayer = model.roberta.encoder.layer[i]
        roberta_layer: TransformerSentenceEncoderLayer = roberta_sent_encoder.layers[i]
        # self attention
        self_attn: BertSelfAttention = layer.attention.self
        assert (
            roberta_layer.self_attn.k_proj.weight.data.shape
            == roberta_layer.self_attn.q_proj.weight.data.shape
            == roberta_layer.self_attn.v_proj.weight.data.shape
            == torch.Size((config.hidden_size, config.hidden_size))
        )
        self_attn.query.weight.data = roberta_layer.self_attn.q_proj.weight
        self_attn.query.bias.data = roberta_layer.self_attn.q_proj.bias
        self_attn.key.weight.data = roberta_layer.self_attn.k_proj.weight
        self_attn.key.bias.data = roberta_layer.self_attn.k_proj.bias
        self_attn.value.weight.data = roberta_layer.self_attn.v_proj.weight
        self_attn.value.bias.data = roberta_layer.self_attn.v_proj.bias
        # self-attention output
        self_output: BertSelfOutput = layer.attention.output
        assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
        self_output.dense.weight = roberta_layer.self_attn.out_proj.weight
        self_output.dense.bias = roberta_layer.self_attn.out_proj.bias
        self_output.LayerNorm.weight = roberta_layer.self_attn_layer_norm.weight
        self_output.LayerNorm.bias = roberta_layer.self_attn_layer_norm.bias
        # intermediate
        intermediate: BertIntermediate = layer.intermediate
        assert intermediate.dense.weight.shape == roberta_layer.fc1.weight.shape
        intermediate.dense.weight = roberta_layer.fc1.weight
        intermediate.dense.bias = roberta_layer.fc1.bias
        # output
        bert_output: BertOutput = layer.output
        assert bert_output.dense.weight.shape == roberta_layer.fc2.weight.shape
        bert_output.dense.weight = roberta_layer.fc2.weight
        bert_output.dense.bias = roberta_layer.fc2.bias
        bert_output.LayerNorm.weight = roberta_layer.final_layer_norm.weight
        bert_output.LayerNorm.bias = roberta_layer.final_layer_norm.bias
        # end of layer
    if classification_head:
        model.classifier.dense.weight = roberta.model.classification_heads["mnli"].dense.weight
        model.classifier.dense.bias = roberta.model.classification_heads["mnli"].dense.bias
        model.classifier.out_proj.weight = roberta.model.classification_heads["mnli"].out_proj.weight
        model.classifier.out_proj.bias = roberta.model.classification_heads["mnli"].out_proj.bias
    else:
        # LM Head
        model.lm_head.dense.weight = roberta.model.decoder.lm_head.dense.weight
        model.lm_head.dense.bias = roberta.model.decoder.lm_head.dense.bias
        model.lm_head.layer_norm.weight = roberta.model.decoder.lm_head.layer_norm.weight
        model.lm_head.layer_norm.bias = roberta.model.decoder.lm_head.layer_norm.bias
        model.lm_head.decoder.weight = roberta.model.decoder.lm_head.weight
        model.lm_head.decoder.bias = roberta.model.decoder.lm_head.bias
    # Let's check that we get the same results.
    input_ids: torch.Tensor = roberta.encode(SAMPLE_TEXT).unsqueeze(0)  # batch of size 1
    our_output = model(input_ids)[0]
    if classification_head:
        their_output = roberta.model.classification_heads["mnli"](roberta.extract_features(input_ids))
    else:
        their_output = roberta.model(input_ids)[0]
    print(our_output.shape, their_output.shape, our_output[0][0], their_output[0][0])
    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")
    pathlib.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)