init

transformers/tests/models/apertus/test_modeling_apertus.py

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+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team and the Swiss AI Initiative. All rights reserved.
+#
+# This code is based on HuggingFace's LLaMA implementation in this library.
+# It has been modified from its original forms to accommodate minor architectural
+# differences compared to LLaMA used by the Swiss AI Initiative that trained the model.
+#
+# 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 Apertus model."""
+
+import unittest
+
+from transformers import is_torch_available
+from transformers.testing_utils import (
+    require_read_token,
+    require_torch,
+    require_torch_accelerator,
+    slow,
+)
+
+from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester
+
+
+if is_torch_available():
+    from transformers import (
+        ApertusConfig,
+        ApertusForCausalLM,
+        ApertusForTokenClassification,
+        ApertusModel,
+    )
+
+
+class ApertusModelTester(CausalLMModelTester):
+    if is_torch_available():
+        config_class = ApertusConfig
+        base_model_class = ApertusModel
+        causal_lm_class = ApertusForCausalLM
+        token_class = ApertusForTokenClassification
+
+
+@require_torch
+class ApertusModelTest(CausalLMModelTest, unittest.TestCase):
+    all_model_classes = (
+        (
+            ApertusModel,
+            ApertusForCausalLM,
+            ApertusForTokenClassification,
+        )
+        if is_torch_available()
+        else ()
+    )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": ApertusModel,
+            "text-generation": ApertusForCausalLM,
+            "token-classification": ApertusForTokenClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
+    test_headmasking = False
+    test_pruning = False
+    model_tester_class = ApertusModelTester
+
+    # 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 = ApertusForCausalLM if is_torch_available() else None
+
+
+@require_torch_accelerator
+@require_read_token
+@slow
+class ApertusIntegrationTest(unittest.TestCase):
+    pass