# 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_torch,
    require_torch_accelerator,
    slow,
)

from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester


if is_torch_available():
    from transformers import (
        ApertusForCausalLM,
        ApertusModel,
    )


class ApertusModelTester(CausalLMModelTester):
    if is_torch_available():
        base_model_class = ApertusModel

    def __init__(self, parent):
        super().__init__(parent=parent)
        # NOTE(3outeille): must be 0.0 for TP backward tests. In train mode, non-zero dropout causes
        # different RNG states between the non-TP and TP model forward passes (they run sequentially),
        # leading to different dropout masks and mismatched losses.
        self.attention_probs_dropout_prob = 0.0


@require_torch
class ApertusModelTest(CausalLMModelTest, unittest.TestCase):
    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
@slow
class ApertusIntegrationTest(unittest.TestCase):
    pass