init

transformers/tests/models/evolla/test_modeling_evolla.py

@@ -0,0 +1,387 @@
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Testing suite for the PyTorch Evolla model."""
+
+import unittest
+from functools import cached_property
+
+from parameterized import parameterized
+
+from transformers import BitsAndBytesConfig, EvollaConfig, is_torch_available
+from transformers.testing_utils import (
+    TestCasePlus,
+    require_bitsandbytes,
+    require_torch,
+    slow,
+    torch_device,
+)
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import (
+    TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION,
+    ModelTesterMixin,
+    _config_zero_init,
+    ids_tensor,
+    random_attention_mask,
+)
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import EvollaForProteinText2Text, EvollaModel, EvollaProcessor
+
+
+class EvollaModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=1,
+        is_training=False,
+        text_seq_length=20,
+        text_vocab_size=100,
+        protein_seq_length=10,
+        protein_vocab_size=20,
+        hidden_size=4,  # llama hidden size
+        intermediate_size=7,  # llama intermediate size
+        num_hidden_layers=1,  # llama hidden layers
+        num_attention_heads=2,  # llama attention heads
+        num_key_value_heads=2,  # llama key value heads
+        protein_hidden_size=8,  # protein encoder hidden size
+        protein_num_hidden_layers=1,  # protein encoder hidden layers
+        protein_num_attention_heads=4,  # protein encoder attention heads
+        protein_intermediate_size=11,  # protein encoder intermediate size
+        resampler_num_latents=7,  # sequence compressor num latents
+        resampler_ff_mult=1,  # sequence compressor ff mult
+        resampler_depth=2,  # sequence compressor depth
+        resampler_dim_head=4,  # sequence compressor dim head
+        resampler_heads=2,  # sequence compressor heads
+        aligner_num_add_layers=1,  # sequence aligner num add layers
+        aligner_ffn_mult=1,  # sequence aligner ffn mult
+        use_input_mask=True,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.protein_seq_length = protein_seq_length
+        self.protein_vocab_size = protein_vocab_size
+        self.text_seq_length = text_seq_length
+        self.text_vocab_size = text_vocab_size
+        self.seq_length = text_seq_length
+
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.protein_hidden_size = protein_hidden_size
+        self.protein_num_hidden_layers = protein_num_hidden_layers
+        self.protein_num_attention_heads = protein_num_attention_heads
+        self.protein_intermediate_size = protein_intermediate_size
+
+        self.resampler_num_latents = resampler_num_latents
+        self.resampler_ff_mult = resampler_ff_mult
+        self.resampler_depth = resampler_depth
+        self.resampler_dim_head = resampler_dim_head
+        self.resampler_heads = resampler_heads
+
+        self.aligner_num_add_layers = aligner_num_add_layers
+        self.aligner_ffn_mult = aligner_ffn_mult
+
+        self.use_input_mask = use_input_mask
+        self.is_training = is_training
+
+    @property
+    def is_encoder_decoder(self):
+        return False
+
+    def prepare_config_and_inputs(self, num_proteins=None):
+        batch_size = num_proteins if num_proteins is not None else self.batch_size
+        text_input_ids = ids_tensor([batch_size, self.text_seq_length], self.text_vocab_size)
+
+        protein_input_ids = ids_tensor([batch_size, self.protein_seq_length], self.protein_vocab_size)
+
+        if self.use_input_mask:
+            text_input_mask = random_attention_mask([batch_size, self.text_seq_length])
+            protein_input_mask = random_attention_mask([batch_size, self.protein_seq_length])
+
+        config = self.get_config()
+        return (config, text_input_ids, text_input_mask, protein_input_ids, protein_input_mask)
+
+    def get_config(self):
+        return EvollaConfig(
+            protein_encoder_config={
+                "vocab_size": self.protein_vocab_size,
+                "hidden_size": self.protein_hidden_size,
+                "num_hidden_layers": self.protein_num_hidden_layers,
+                "num_attention_heads": self.protein_num_attention_heads,
+                "intermediate_size": self.protein_intermediate_size,
+            },
+            vocab_size=self.text_vocab_size,
+            hidden_size=self.hidden_size,
+            intermediate_size=self.intermediate_size,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            num_key_value_heads=self.num_key_value_heads,
+            aligner_ffn_mult=self.aligner_ffn_mult,
+            aligner_num_add_layers=self.aligner_num_add_layers,
+            resampler_depth=self.resampler_depth,
+            resampler_dim_head=self.resampler_dim_head,
+            resampler_heads=self.resampler_heads,
+            resampler_num_latents=self.resampler_num_latents,
+            resampler_ff_mult=self.resampler_ff_mult,
+        )
+
+    def create_and_check_model(
+        self,
+        config,
+        input_ids,
+        input_mask,
+        protein_input_ids,
+        protein_input_mask,
+        batch_size=None,
+    ):
+        batch_size = batch_size if batch_size is not None else self.batch_size
+        model = EvollaModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(
+            input_ids,
+            attention_mask=input_mask,
+            protein_input_ids=protein_input_ids,
+            protein_attention_mask=protein_input_mask,
+        )
+        self.parent.assertEqual(result.last_hidden_state.shape, (batch_size, input_ids.shape[1], self.hidden_size))
+
+    def create_and_check_model_gen(
+        self,
+        config,
+        input_ids,
+        input_mask,
+        protein_input_ids,
+        protein_input_mask,
+    ):
+        model = EvollaForProteinText2Text(config)
+        model.to(torch_device)
+        model.eval()
+        model.generate(
+            input_ids,
+            attention_mask=input_mask,
+            protein_input_ids=protein_input_ids,
+            protein_attention_mask=protein_input_mask,
+            max_length=self.seq_length + 2,
+        )
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        (config, text_input_ids, text_input_mask, protein_input_ids, protein_input_mask) = config_and_inputs
+        inputs_dict = {
+            "input_ids": text_input_ids,
+            "attention_mask": text_input_mask,
+            "protein_input_ids": protein_input_ids,
+            "protein_attention_mask": protein_input_mask,
+        }
+        return config, inputs_dict
+
+
+@require_torch
+class EvollaModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (EvollaModel, EvollaForProteinText2Text) if is_torch_available() else ()
+    pipeline_model_mapping = {"feature-extraction": EvollaModel} if is_torch_available() else {}
+    test_pruning = False
+    test_headmasking = False
+    test_torchscript = False
+    test_resize_embeddings = False
+    maxDiff = None
+
+    def setUp(self):
+        self.model_tester = EvollaModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=EvollaConfig, hidden_size=37)
+
+    @property
+    def is_encoder_decoder(self):
+        return self.model_tester.is_encoder_decoder
+
+    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
+        inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
+        # XXX: EvollaForProteinText2Text has no MODEL_FOR group yet, but it should be the same
+        # as MODEL_FOR_CAUSAL_LM_MAPPING_NAMES, so for now manually changing to do the right thing
+        # as super won't do it
+        if return_labels:
+            inputs_dict["labels"] = torch.zeros(
+                (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
+            )
+
+        return inputs_dict
+
+    def test_model_outputs_equivalence(self):
+        try:
+            orig = self.all_model_classes
+            # EvollaModel.forward doesn't have labels input arg - only EvollaForProteinText2Text does
+            self.all_model_classes = (EvollaForProteinText2Text,) if is_torch_available() else ()
+            super().test_model_outputs_equivalence()
+        finally:
+            self.all_model_classes = orig
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_model_single_protein(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs(num_proteins=1)
+        self.model_tester.create_and_check_model(*config_and_inputs, batch_size=1)
+
+    def test_model_multiple_proteins(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs(num_proteins=2)
+        self.model_tester.create_and_check_model(*config_and_inputs, batch_size=2)
+
+    def test_generate_single_protein(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs(num_proteins=1)
+        self.model_tester.create_and_check_model_gen(*config_and_inputs)
+
+    def test_generate_multiple_proteins(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs(num_proteins=2)
+        self.model_tester.create_and_check_model_gen(*config_and_inputs)
+
+    def test_saprot_output(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.return_dict = True
+        protein_information = {
+            "input_ids": inputs_dict["protein_input_ids"],
+            "attention_mask": inputs_dict["protein_attention_mask"],
+        }
+        for model_class in self.all_model_classes:
+            if model_class is not EvollaModel:
+                continue
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+            protein_encoder_outputs = model.protein_encoder.model(**protein_information, return_dict=True)
+            print(model_class, protein_encoder_outputs)
+
+    def test_protein_encoder_output(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.return_dict = True
+        protein_information = {
+            "input_ids": inputs_dict["protein_input_ids"],
+            "attention_mask": inputs_dict["protein_attention_mask"],
+        }
+        for model_class in self.all_model_classes:
+            if model_class is not EvollaModel:
+                continue
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+            protein_encoder_outputs = model.protein_encoder(**protein_information, return_dict=True)
+            print(model_class, protein_encoder_outputs)
+
+    def test_single_forward(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.return_dict = True
+
+        for model_class in self.all_model_classes:
+            inputs_dict["output_attentions"] = True
+            inputs_dict["output_hidden_states"] = False
+            config.return_dict = True
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+            print(outputs)
+
+    def test_initialization(self):
+        # we skip the latents initialization test
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        configs_no_init = _config_zero_init(config)
+        for model_class in self.all_model_classes:
+            model = model_class(config=configs_no_init)
+            for name, param in model.named_parameters():
+                if param.requires_grad:
+                    # skip latents
+                    if name.endswith("latents"):
+                        print(f"Skipping latents {name}")
+                        continue
+                    self.assertIn(
+                        ((param.data.mean() * 1e9).round() / 1e9).item(),
+                        [0.0, 1.0],
+                        msg=f"Parameter {name} of model {model_class} seems not properly initialized",
+                    )
+
+    @parameterized.expand(TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION)
+    @unittest.skip("Evolla requires both text and protein inputs which is currently not done in this test.")
+    def test_eager_matches_sdpa_inference(self):
+        pass
+
+    @unittest.skip("Evolla does not support eager attention implementation.")
+    def test_eager_padding_matches_padding_free_with_position_ids(self):
+        pass
+
+    @unittest.skip(
+        "Evolla has a separate test runner for generation tests with complex inheritance, causing this check to fail."
+    )
+    def test_generation_tester_mixin_inheritance(self):
+        pass
+
+    @unittest.skip("Evolla requires both text and protein inputs which is currently not done in this test.")
+    def test_flex_attention_with_grads(self):
+        pass
+
+
+@require_torch
+class EvollaModelIntegrationTest(TestCasePlus):
+    def _prepare_for_inputs(self):
+        aa_seq = "MLLEETLKSCPIVKRGKYHYFIHPISDGVPLVEPKLLREVATRIIKIGNFEGVNKIVTAEAMGIPLVTTLSLYTDIPYVIMRKREYKLPGEVPVFQSTGYSKGQLYLNGIEKGDKVIIIDDVISTGGTMIAIINALERAGAEIKDIICVIERGDGKKIVEEKTGYKIKTLVKIDVVDGEVVIL"
+        foldseek = "dvvvvqqqpfawdddppdtdgcgclapvpdpddpvvlvvllvlcvvpadpvqaqeeeeeddscpsnvvsncvvpvhyydywylddppdppkdwqwf######gitidpdqaaaheyeyeeaeqdqlrvvlsvvvrcvvrnyhhrayeyaeyhycnqvvccvvpvghyhynwywdqdpsgidtd"
+        question = "What is the function of this protein?"
+
+        protein_information = {
+            "aa_seq": aa_seq,
+            "foldseek": foldseek,
+        }
+        messages = [
+            {"role": "system", "content": "You are an AI expert that can answer any questions about protein."},
+            {"role": "user", "content": question},
+        ]
+        return protein_information, messages
+
+    @cached_property
+    def default_processor(self):
+        return EvollaProcessor.from_pretrained("westlake-repl/Evolla-10B-hf")
+
+    @require_bitsandbytes
+    @slow
+    def test_inference_natural_language_protein_reasoning(self):
+        protein_information, messages = self._prepare_for_inputs()
+        processor = self.default_processor
+        inputs = processor(
+            messages_list=[messages], proteins=[protein_information], return_tensors="pt", padding="longest"
+        ).to(torch_device)
+
+        # the CI gpu is small so using quantization to fit
+        quantization_config = BitsAndBytesConfig(
+            load_in_4bit=True,
+            bnb_4bit_compute_dtype="float16",
+        )
+        model = EvollaForProteinText2Text.from_pretrained(
+            "westlake-repl/Evolla-10B-hf",
+            quantization_config=quantization_config,
+            device_map=torch_device,
+        )
+        generated_ids = model.generate(**inputs, max_new_tokens=100, do_sample=False)
+        generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)
+
+        self.assertIn("This protein", generated_text[0])
+        self.assertIn("purine", generated_text[0])

transformers/tests/models/evolla/test_processing_evolla.py

@@ -0,0 +1,295 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import random
+import shutil
+import tempfile
+import unittest
+
+from transformers import (
+    AutoProcessor,
+    EvollaProcessor,
+)
+from transformers.testing_utils import require_torch
+from transformers.utils import is_torch_available
+
+from ...test_processing_common import ProcessorTesterMixin
+
+
+if is_torch_available():
+    import torch
+
+
+EVOLLA_VALID_AA = list("ACDEFGHIKLMNPQRSTVWY#")
+EVOLLA_VALID_FS = list("pynwrqhgdlvtmfsaeikc#")
+
+
+@require_torch
+class EvollaProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = EvollaProcessor
+
+    def setUp(self):
+        self.tmpdirname = tempfile.mkdtemp()
+
+        processor = EvollaProcessor.from_pretrained("westlake-repl/Evolla-10B-hf")
+
+        processor.save_pretrained(self.tmpdirname)
+
+        self.input_keys = ["protein_input_ids", "protein_attention_mask", "input_ids", "attention_mask"]
+
+    def prepare_input_and_expected_output(self):
+        amino_acid_sequence = "AAAA"
+        foldseek_sequence = "dddd"
+        question = "What is the function of this protein?"
+
+        expected_output = {
+            "protein_input_ids": torch.tensor([[0, 13, 13, 13, 13, 2]]),
+            "protein_attention_mask": torch.tensor([[1, 1, 1, 1, 1, 1]]),
+            "input_ids": torch.tensor(
+                [
+                    [
+                        128000,
+                        128006,
+                        9125,
+                        128007,
+                        271,
+                        2675,
+                        527,
+                        459,
+                        15592,
+                        6335,
+                        430,
+                        649,
+                        4320,
+                        904,
+                        4860,
+                        922,
+                        13128,
+                        13,
+                        128009,
+                        128006,
+                        882,
+                        128007,
+                        271,
+                        3923,
+                        374,
+                        279,
+                        734,
+                        315,
+                        420,
+                        13128,
+                        30,
+                        128009,
+                        128006,
+                        78191,
+                        128007,
+                        271,
+                    ]
+                ]
+            ),
+            "attention_mask": torch.tensor(
+                [
+                    [
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                        1,
+                    ]
+                ]
+            ),
+        }
+        protein_dict = {"aa_seq": amino_acid_sequence, "foldseek": foldseek_sequence}
+        message = [
+            {"role": "system", "content": "You are an AI expert that can answer any questions about protein."},
+            {"role": "user", "content": question},
+        ]
+        return protein_dict, message, expected_output
+
+    def test_processor(self):
+        protein_tokenizer = self.get_protein_tokenizer()
+        tokenizer = self.get_tokenizer()
+
+        processor = EvollaProcessor(protein_tokenizer, tokenizer)
+
+        protein_dict, message, expected_output = self.prepare_input_and_expected_output()
+        inputs = processor(proteins=[protein_dict], messages_list=[message])
+
+        # check if the input is correct
+        for key, value in expected_output.items():
+            self.assertTrue(
+                torch.equal(inputs[key], value),
+                f"inputs[key] is {inputs[key]} and expected_output[key] is {value}",
+            )
+
+    def get_tokenizer(self, **kwargs):
+        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).tokenizer
+
+    def get_protein_tokenizer(self, **kwargs):
+        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).protein_tokenizer
+
+    def tearDown(self):
+        shutil.rmtree(self.tmpdirname)
+
+    def prepare_inputs_single(self):
+        proteins = {
+            "aa_seq": "".join(random.choices(EVOLLA_VALID_AA, k=100)),
+            "foldseek": "".join(random.choices(EVOLLA_VALID_FS, k=100)),
+        }
+        return proteins
+
+    def prepare_inputs_pair(self):
+        proteins = [
+            {
+                "aa_seq": "".join(random.choices(EVOLLA_VALID_AA, k=100)),
+                "foldseek": "".join(random.choices(EVOLLA_VALID_FS, k=100)),
+            },
+            {
+                "aa_seq": "".join(random.choices(EVOLLA_VALID_AA, k=100)),
+                "foldseek": "".join(random.choices(EVOLLA_VALID_FS, k=100)),
+            },
+        ]
+        return proteins
+
+    def prepare_inputs_long(self):
+        proteins = [
+            {
+                "aa_seq": "".join(random.choices(EVOLLA_VALID_AA, k=100)),
+                "foldseek": "".join(random.choices(EVOLLA_VALID_FS, k=100)),
+            },
+            {
+                "aa_seq": "".join(random.choices(EVOLLA_VALID_AA, k=2000)),
+                "foldseek": "".join(random.choices(EVOLLA_VALID_FS, k=2000)),
+            },
+        ]
+        return proteins
+
+    def prepare_inputs_short(self):
+        proteins = [
+            {
+                "aa_seq": "".join(random.choices(EVOLLA_VALID_AA, k=1)),
+                "foldseek": "".join(random.choices(EVOLLA_VALID_FS, k=1)),
+            },
+            {
+                "aa_seq": "".join(random.choices(EVOLLA_VALID_AA, k=100)),
+                "foldseek": "".join(random.choices(EVOLLA_VALID_FS, k=100)),
+            },
+        ]
+        return proteins
+
+    def prepare_inputs_empty(self):
+        proteins = [
+            {
+                "aa_seq": "",
+                "foldseek": "",
+            },
+            {
+                "aa_seq": "".join(random.choices(EVOLLA_VALID_AA, k=100)),
+                "foldseek": "".join(random.choices(EVOLLA_VALID_FS, k=100)),
+            },
+        ]
+        return proteins
+
+    def prepare_inputs(self, protein_types="pair"):
+        r"""
+        Prepare inputs for the test.
+
+        Args:
+            protein_types (`str`): the types of proteins to prepare.
+                - "single": a single correct protein.
+                - "pair": a pair of correct proteins.
+                - "long": a long sequence of correct proteins and a correct protein.
+                - "short": a short sequence of correct proteins (only have 1 aa) and a correct protein.
+                - "empty": an empty sequence of proteins and a correct protein.
+        """
+        if protein_types == "single":
+            proteins = self.prepare_inputs_single()
+        elif protein_types == "pair":
+            proteins = self.prepare_inputs_pair()
+        elif protein_types == "long":
+            proteins = self.prepare_inputs_long()
+        elif protein_types == "short":
+            proteins = self.prepare_inputs_short()
+        elif protein_types == "empty":
+            proteins = self.prepare_inputs_empty()
+        else:
+            raise ValueError(
+                f"protein_types should be one of 'single', 'pair', 'long','short', 'empty', but got {protein_types}"
+            )
+
+        questions = ["What is the function of the protein?"] * len(proteins)
+        messages_list = []
+        for question in questions:
+            messages = [
+                {"role": "system", "content": "You are an AI expert that can answer any questions about protein."},
+                {"role": "user", "content": question},
+            ]
+            messages_list.append(messages)
+        return proteins, messages_list
+
+    def test_tokenizer_decode(self):
+        protein_tokenizer = self.get_protein_tokenizer()
+        tokenizer = self.get_tokenizer()
+
+        processor = EvollaProcessor(tokenizer=tokenizer, protein_tokenizer=protein_tokenizer, return_tensors="pt")
+
+        predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
+
+        decoded_processor = processor.batch_decode(predicted_ids)
+        decoded_tok = tokenizer.batch_decode(predicted_ids)
+
+        self.assertListEqual(decoded_tok, decoded_processor)
+
+    def test_model_input_names(self):
+        protein_tokenizer = self.get_protein_tokenizer()
+        tokenizer = self.get_tokenizer()
+
+        processor = EvollaProcessor(tokenizer=tokenizer, protein_tokenizer=protein_tokenizer)
+        proteins, messages_list = self.prepare_inputs()
+
+        inputs = processor(messages_list=messages_list, proteins=proteins, padding="longest", return_tensors="pt")
+
+        # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
+        self.assertSetEqual(set(inputs.keys()), set(self.input_keys))