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tests/models/eurobert/test_modeling_eurobert.py

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+# Copyright 2025 Nicolas Boizard, Duarte M. Alves, Hippolyte Gisserot-Boukhlef and the EuroBert 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 EuroBERT model."""
+
+import unittest
+
+from parameterized import parameterized
+
+from transformers import AutoTokenizer, EuroBertConfig, is_torch_available, set_seed
+from transformers.testing_utils import require_torch, slow, torch_device
+
+from ...causal_lm_tester import _config_supports_rope_scaling, _set_config_rope_params
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        EuroBertForMaskedLM,
+        EuroBertForSequenceClassification,
+        EuroBertForTokenClassification,
+        EuroBertModel,
+    )
+
+
+class EuroBertModelTester:
+    if is_torch_available():
+        base_model_class = EuroBertModel
+
+    def __init__(
+        self,
+        parent,
+        batch_size=13,
+        seq_length=7,
+        is_training=True,
+        use_input_mask=True,
+        use_token_type_ids=False,
+        use_labels=True,
+        vocab_size=99,
+        hidden_size=32,
+        num_hidden_layers=2,
+        num_attention_heads=4,
+        intermediate_size=37,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        max_position_embeddings=512,
+        type_vocab_size=16,
+        type_sequence_label_size=2,
+        initializer_range=0.02,
+        num_labels=3,
+        num_choices=4,
+        pad_token_id=0,
+        scope=None,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.seq_length = seq_length
+        self.is_training = is_training
+        self.use_input_mask = use_input_mask
+        self.use_token_type_ids = use_token_type_ids
+        self.use_labels = use_labels
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.type_vocab_size = type_vocab_size
+        self.type_sequence_label_size = type_sequence_label_size
+        self.initializer_range = initializer_range
+        self.num_labels = num_labels
+        self.num_choices = num_choices
+        self.pad_token_id = pad_token_id
+        self.scope = scope
+
+    def prepare_config_and_inputs(self):
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+
+        input_mask = None
+        if self.use_input_mask:
+            input_mask = random_attention_mask([self.batch_size, self.seq_length]).to(torch_device)
+
+        token_type_ids = None
+        if self.use_token_type_ids:
+            token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
+
+        sequence_labels = None
+        token_labels = None
+        choice_labels = None
+        if self.use_labels:
+            sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
+            token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
+            choice_labels = ids_tensor([self.batch_size], self.num_choices)
+
+        config = self.get_config()
+
+        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+
+    def get_config(self):
+        return EuroBertConfig(
+            vocab_size=self.vocab_size,
+            hidden_size=self.hidden_size,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            intermediate_size=self.intermediate_size,
+            hidden_act=self.hidden_act,
+            attention_dropout=self.attention_probs_dropout_prob,
+            max_position_embeddings=self.max_position_embeddings,
+            initializer_range=self.initializer_range,
+            pad_token_id=self.pad_token_id,
+        )
+
+    def create_and_check_model(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        model = EuroBertModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
+        result = model(input_ids, attention_mask=input_mask)
+        result = model(input_ids)
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
+
+    def create_and_check_for_masked_lm(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        model = EuroBertForMaskedLM(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
+
+    def create_and_check_for_sequence_classification(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        config.num_labels = self.num_labels
+        model = EuroBertForSequenceClassification(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
+
+    def create_and_check_for_token_classification(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        config.num_labels = self.num_labels
+        model = EuroBertForTokenClassification(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        (
+            config,
+            input_ids,
+            token_type_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+        ) = config_and_inputs
+        inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
+        return config, inputs_dict
+
+
+@require_torch
+class EuroBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (
+        (
+            EuroBertModel,
+            EuroBertForMaskedLM,
+            EuroBertForSequenceClassification,
+            EuroBertForTokenClassification,
+        )
+        if is_torch_available()
+        else ()
+    )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": EuroBertModel,
+            "fill-mask": EuroBertForMaskedLM,
+            "text-classification": EuroBertForSequenceClassification,
+            "token-classification": EuroBertForTokenClassification,
+            "zero-shot": EuroBertForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
+    model_tester_class = EuroBertModelTester
+    test_headmasking = False
+    test_pruning = False
+    fx_compatible = False  # Broken by attention refactor cc @Cyrilvallez
+
+    # Need to use `0.8` instead of `0.9` for `test_cpu_offload`
+    # This is because we are hitting edge cases with the causal_mask buffer
+    model_split_percents = [0.5, 0.7, 0.8]
+
+    # used in `test_torch_compile_for_training`
+    _torch_compile_train_cls = EuroBertForMaskedLM if is_torch_available() else None
+
+    def setUp(self):
+        self.model_tester = EuroBertModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=EuroBertConfig, hidden_size=32, num_attention_heads=2)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_model_various_embeddings(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        for type in ["absolute", "relative_key", "relative_key_query"]:
+            config_and_inputs[0].position_embedding_type = type
+            self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_eurobert_sequence_classification_model(self):
+        config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.num_labels = 3
+        input_ids = input_dict["input_ids"]
+        attention_mask = input_ids.ne(1).to(torch_device)
+        sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
+        model = EuroBertForSequenceClassification(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
+        self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
+
+    def test_eurobert_sequence_classification_model_for_single_label(self):
+        config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.num_labels = 3
+        config.problem_type = "single_label_classification"
+        input_ids = input_dict["input_ids"]
+        attention_mask = input_ids.ne(1).to(torch_device)
+        sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
+        model = EuroBertForSequenceClassification(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
+        self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
+
+    def test_eurobert_sequence_classification_model_for_multi_label(self):
+        config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.num_labels = 3
+        config.problem_type = "multi_label_classification"
+        input_ids = input_dict["input_ids"]
+        attention_mask = input_ids.ne(1).to(torch_device)
+        sequence_labels = ids_tensor(
+            [self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size
+        ).to(torch.float)
+        model = EuroBertForSequenceClassification(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
+        self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
+
+    def test_for_masked_lm(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
+
+    def test_for_sequence_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
+
+    def test_for_token_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
+
+    @unittest.skip(reason="EuroBert buffers include complex numbers, which breaks this test")
+    def test_save_load_fast_init_from_base(self):
+        pass
+
+    @parameterized.expand([("linear",), ("dynamic",), ("yarn",)])
+    def test_model_rope_scaling_from_config(self, scaling_type):
+        """
+        Tests that we can initialize a model with RoPE scaling in the config, that it can run a forward pass, and
+        that a few basic model output properties are honored.
+        """
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+
+        if not _config_supports_rope_scaling(config):
+            self.skipTest("This model does not support RoPE scaling")
+
+        partial_rotary_factor = config.rope_parameters.get("partial_rotary_factor", 1.0)
+        short_input = ids_tensor([1, 10], config.vocab_size)
+        long_input = ids_tensor([1, int(config.max_position_embeddings * 1.5)], config.vocab_size)
+
+        set_seed(42)  # Fixed seed at init time so the two models get the same random weights
+        _set_config_rope_params(
+            config,
+            {
+                "rope_type": "default",
+                "rope_theta": 10_000.0,
+                "partial_rotary_factor": partial_rotary_factor,
+                "original_max_position_embeddings": 16384,
+            },
+        )
+        original_model = self.model_tester_class.base_model_class(config)
+        original_model.to(torch_device)
+        original_model.eval()
+        original_short_output = original_model(short_input).last_hidden_state
+        original_long_output = original_model(long_input).last_hidden_state
+
+        set_seed(42)  # Fixed seed at init time so the two models get the same random weights
+        _set_config_rope_params(
+            config,
+            {
+                "rope_type": scaling_type,
+                "factor": 10.0,
+                "rope_theta": 10_000.0,
+                "partial_rotary_factor": partial_rotary_factor,
+            },
+        )
+        scaled_model = self.model_tester_class.base_model_class(config)
+        scaled_model.to(torch_device)
+        scaled_model.eval()
+        scaled_short_output = scaled_model(short_input).last_hidden_state
+        scaled_long_output = scaled_model(long_input).last_hidden_state
+
+        # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
+        # maximum sequence length, so the outputs for the short input should match.
+        if scaling_type == "dynamic":
+            torch.testing.assert_close(original_short_output, scaled_short_output, rtol=1e-5, atol=1e-5)
+        else:
+            self.assertFalse(torch.allclose(original_short_output, scaled_short_output, atol=1e-5))
+
+        # The output should be different for long inputs
+        self.assertFalse(torch.allclose(original_long_output, scaled_long_output, atol=1e-5))
+
+    def test_model_rope_scaling_frequencies(self):
+        """Tests the frequency properties of the different RoPE scaling types on the model RoPE layer."""
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+
+        if not _config_supports_rope_scaling(config):
+            self.skipTest("This model does not support RoPE scaling")
+
+        # Retrieves the RoPE layer class from the base model class. Uses `.named_modules()` to avoid hardcoding the
+        # named location of the RoPE layer class.
+        base_model = self.model_tester.base_model_class(config)
+        possible_rope_attributes = [
+            "pos_emb",
+            "rotary_emb",  # most common case
+            "global_rotary_emb",
+            "local_rotary_emb",
+        ]
+        for name, module in base_model.named_modules():
+            if any(potential_name in name for potential_name in possible_rope_attributes):
+                rope_class = type(module)
+                break
+
+        scaling_factor = 10
+        short_input_length = 10
+        partial_rotary_factor = config.rope_parameters.get("partial_rotary_factor", 1.0)
+        long_input_length = int(config.max_position_embeddings * 1.5)
+
+        # Inputs
+        x = torch.randn(
+            1, dtype=torch.float32, device=torch_device
+        )  # used exclusively to get the dtype and the device
+        position_ids_short = torch.arange(short_input_length, dtype=torch.long, device=torch_device)
+        position_ids_short = position_ids_short.unsqueeze(0)
+        position_ids_long = torch.arange(long_input_length, dtype=torch.long, device=torch_device)
+        position_ids_long = position_ids_long.unsqueeze(0)
+
+        # Sanity check original RoPE
+        _set_config_rope_params(
+            config, {"rope_type": "default", "rope_theta": 10_000.0, "partial_rotary_factor": partial_rotary_factor}
+        )
+        original_rope = rope_class(config=config).to(torch_device)
+        original_cos_short, original_sin_short = original_rope(x, position_ids_short)
+        original_cos_long, original_sin_long = original_rope(x, position_ids_long)
+        torch.testing.assert_close(original_cos_short, original_cos_long[:, :short_input_length, :])
+        torch.testing.assert_close(original_sin_short, original_sin_long[:, :short_input_length, :])
+
+        # Sanity check linear RoPE scaling
+        # New position "x" should match original position with index "x/scaling_factor"
+        _set_config_rope_params(
+            config,
+            {
+                "rope_type": "linear",
+                "factor": scaling_factor,
+                "rope_theta": 10_000.0,
+                "partial_rotary_factor": partial_rotary_factor,
+            },
+        )
+        linear_scaling_rope = rope_class(config=config).to(torch_device)
+        linear_cos_short, linear_sin_short = linear_scaling_rope(x, position_ids_short)
+        linear_cos_long, linear_sin_long = linear_scaling_rope(x, position_ids_long)
+        torch.testing.assert_close(linear_cos_short, linear_cos_long[:, :short_input_length, :])
+        torch.testing.assert_close(linear_sin_short, linear_sin_long[:, :short_input_length, :])
+        for new_position in range(0, long_input_length, scaling_factor):
+            original_position = int(new_position // scaling_factor)
+            torch.testing.assert_close(linear_cos_long[:, new_position, :], original_cos_long[:, original_position, :])
+            torch.testing.assert_close(linear_sin_long[:, new_position, :], original_sin_long[:, original_position, :])
+
+        # Sanity check Dynamic NTK RoPE scaling
+        # Scaling should only be observed after a long input is fed. We can observe that the frequencies increase
+        # with scaling_factor (or that `inv_freq` decreases)
+        _set_config_rope_params(
+            config,
+            {
+                "rope_type": "dynamic",
+                "factor": scaling_factor,
+                "rope_theta": 10_000.0,
+                "partial_rotary_factor": partial_rotary_factor,
+            },
+        )
+        ntk_scaling_rope = rope_class(config=config).to(torch_device)
+        ntk_cos_short, ntk_sin_short = ntk_scaling_rope(x, position_ids_short)
+        ntk_cos_long, ntk_sin_long = ntk_scaling_rope(x, position_ids_long)
+        torch.testing.assert_close(ntk_cos_short, original_cos_short)
+        torch.testing.assert_close(ntk_sin_short, original_sin_short)
+        with self.assertRaises(AssertionError):
+            torch.testing.assert_close(ntk_cos_long, original_cos_long)
+        with self.assertRaises(AssertionError):
+            torch.testing.assert_close(ntk_sin_long, original_sin_long)
+        self.assertTrue((ntk_scaling_rope.inv_freq <= original_rope.inv_freq).all())
+
+        # Sanity check Yarn RoPE scaling
+        # Scaling should be over the entire input
+        _set_config_rope_params(
+            config,
+            {
+                "rope_type": "yarn",
+                "factor": scaling_factor,
+                "rope_theta": 10_000.0,
+                "partial_rotary_factor": partial_rotary_factor,
+            },
+        )
+        yarn_scaling_rope = rope_class(config=config).to(torch_device)
+        yarn_cos_short, yarn_sin_short = yarn_scaling_rope(x, position_ids_short)
+        yarn_cos_long, yarn_sin_long = yarn_scaling_rope(x, position_ids_long)
+        torch.testing.assert_close(yarn_cos_short, yarn_cos_long[:, :short_input_length, :])
+        torch.testing.assert_close(yarn_sin_short, yarn_sin_long[:, :short_input_length, :])
+        with self.assertRaises(AssertionError):
+            torch.testing.assert_close(yarn_cos_short, original_cos_short)
+        with self.assertRaises(AssertionError):
+            torch.testing.assert_close(yarn_sin_short, original_sin_short)
+        with self.assertRaises(AssertionError):
+            torch.testing.assert_close(yarn_cos_long, original_cos_long)
+        with self.assertRaises(AssertionError):
+            torch.testing.assert_close(yarn_sin_long, original_sin_long)
+
+    def test_model_loading_old_rope_configs(self):
+        def _reinitialize_config(base_config, new_kwargs):
+            # Reinitialize the config with the new kwargs, forcing the config to go through its __init__ validation
+            # steps.
+            base_config_dict = base_config.to_dict()
+            new_config = EuroBertConfig.from_dict(config_dict={**base_config_dict, **new_kwargs})
+            return new_config
+
+        # from untouched config -> ✅
+        base_config, model_inputs = self.model_tester.prepare_config_and_inputs_for_common()
+        original_model = EuroBertForMaskedLM(base_config).to(torch_device)
+        original_model(**model_inputs)
+
+        # from a config with the expected rope configuration -> ✅
+        config = _reinitialize_config(base_config, {"rope_scaling": {"rope_type": "linear", "factor": 10.0}})
+        original_model = EuroBertForMaskedLM(config).to(torch_device)
+        original_model(**model_inputs)
+
+        # from a config with the old rope configuration ('type' instead of 'rope_type')  -> ✅ we gracefully handle BC
+        config = _reinitialize_config(base_config, {"rope_scaling": {"type": "linear", "factor": 10.0}})
+        original_model = EuroBertForMaskedLM(config).to(torch_device)
+        original_model(**model_inputs)
+
+        # from a config with both 'type' and 'rope_type'  -> ✅ they can coexist (and both are present in the config)
+        config = _reinitialize_config(
+            base_config, {"rope_scaling": {"type": "linear", "rope_type": "linear", "factor": 10.0}}
+        )
+        self.assertTrue(config.rope_scaling["type"] == "linear")
+        self.assertTrue(config.rope_scaling["rope_type"] == "linear")
+        original_model = EuroBertForMaskedLM(config).to(torch_device)
+        original_model(**model_inputs)
+
+        # from a config with parameters in a bad range ('factor' should be >= 1.0) -> ⚠️ throws a warning
+        with self.assertLogs("transformers.modeling_rope_utils", level="WARNING") as logs:
+            config = _reinitialize_config(base_config, {"rope_scaling": {"rope_type": "linear", "factor": -999.0}})
+            original_model = EuroBertForMaskedLM(config).to(torch_device)
+            original_model(**model_inputs)
+            self.assertEqual(len(logs.output), 1)
+            self.assertIn("factor field", logs.output[0])
+
+        # from a config with unknown parameters ('foo' isn't a rope option) -> ⚠️ throws a warning
+        with self.assertLogs("transformers.modeling_rope_utils", level="WARNING") as logs:
+            config = _reinitialize_config(
+                base_config, {"rope_scaling": {"rope_type": "linear", "factor": 10.0, "foo": "bar"}}
+            )
+            original_model = EuroBertForMaskedLM(config).to(torch_device)
+            original_model(**model_inputs)
+            self.assertEqual(len(logs.output), 1)
+            self.assertIn("Unrecognized keys", logs.output[0])
+
+        # from a config with specific rope type but missing one of its mandatory parameters -> ❌ throws exception
+        with self.assertRaises(KeyError):
+            config = _reinitialize_config(base_config, {"rope_scaling": {"rope_type": "linear"}})  # missing "factor"
+
+
+@require_torch
+class EuroBertIntegrationTest(unittest.TestCase):
+    @slow
+    def test_inference_masked_lm(self):
+        model = EuroBertForMaskedLM.from_pretrained("EuroBERT/EuroBERT-210m", attn_implementation="sdpa")
+        tokenizer = AutoTokenizer.from_pretrained("EuroBERT/EuroBERT-210m")
+
+        inputs = tokenizer("Hello World!", return_tensors="pt")
+        with torch.no_grad():
+            output = model(**inputs)[0]
+        expected_shape = torch.Size((1, 4, 128256))
+        self.assertEqual(output.shape, expected_shape)
+
+        # compare the actual values for a slice.
+        expected_slice = torch.tensor([[[2.2926, 2.4539, 1.8910], [5.9669, 3.8567, 0.0723], [2.4965, 2.7193, 1.9904]]])
+        torch.testing.assert_close(output[:, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
+
+    @slow
+    def test_inference_no_head(self):
+        model = EuroBertModel.from_pretrained("EuroBERT/EuroBERT-210m", attn_implementation="sdpa")
+        tokenizer = AutoTokenizer.from_pretrained("EuroBERT/EuroBERT-210m")
+
+        inputs = tokenizer("Hello World!", return_tensors="pt")
+        with torch.no_grad():
+            output = model(**inputs)[0]
+        expected_shape = torch.Size((1, 4, 768))
+        self.assertEqual(output.shape, expected_shape)
+
+        # compare the actual values for a slice.
+        expected_slice = torch.tensor(
+            [[[1.2437, 1.8956, 50.9435], [-4.5560, -0.1686, -1.2776], [1.6557, 1.9383, 50.1393]]]
+        )
+        torch.testing.assert_close(output[:, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
+
+    @slow
+    def test_inference_token_classification(self):
+        model = EuroBertForTokenClassification.from_pretrained(
+            "hf-internal-testing/tiny-random-EuroBertForTokenClassification",
+            attn_implementation="sdpa",
+        )
+        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-EuroBertForTokenClassification")
+
+        inputs = tokenizer("Hello World!", return_tensors="pt")
+        with torch.no_grad():
+            output = model(**inputs)[0]
+        expected_shape = torch.Size((1, 4, 2))
+        self.assertEqual(output.shape, expected_shape)
+
+        expected = torch.tensor([[[-1.0817, -5.3000], [5.6100, -5.2878], [3.4393, -8.8765], [-0.0329, -3.8588]]])
+        torch.testing.assert_close(output, expected, rtol=1e-4, atol=1e-4)
+
+    @slow
+    def test_inference_sequence_classification(self):
+        model = EuroBertForSequenceClassification.from_pretrained(
+            "hf-internal-testing/tiny-random-EuroBertForSequenceClassification",
+            attn_implementation="sdpa",
+        )
+        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-EuroBertForSequenceClassification")
+
+        inputs = tokenizer("Hello World!", return_tensors="pt")
+        with torch.no_grad():
+            output = model(**inputs)[0]
+        expected_shape = torch.Size((1, 2))
+        self.assertEqual(output.shape, expected_shape)
+
+        expected = torch.tensor([[-1.8948, 6.2092]])
+        torch.testing.assert_close(output, expected, rtol=1e-4, atol=1e-4)