"""
Copyright 2023-2024 SGLang Team
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 multiprocessing as mp
import unittest

import torch

from sglang.test.runners import HFRunner, SRTRunner

MODELS = [
    ("LxzGordon/URM-LLaMa-3.1-8B", 1, 3e-2),
    ("Skywork/Skywork-Reward-Llama-3.1-8B-v0.2", 1, 3e-2),
]
TORCH_DTYPES = [torch.float16]

# PROMPT = "Jane has 12 apples. She gives 4 apples to her friend Mark, then buys 1 more apple, and finally splits all her apples equally among herself and her 2 siblings. How many apples does each person get?"
# RESPONSE1 = "1. Jane starts with 12 apples and gives 4 to Mark. 12 - 4 = 8. Jane now has 8 apples.\n2. Jane buys 1 more apple. 8 + 1 = 9. Jane now has 9 apples.\n3. Jane splits the 9 apples equally among herself and her 2 siblings (3 people in total). 9 ÷ 3 = 3 apples each. Each person gets 3 apples."
# RESPONSE2 = "1. Jane starts with 12 apples and gives 4 to Mark. 12 - 4 = 8. Jane now has 8 apples.\n2. Jane buys 1 more apple. 8 + 1 = 9. Jane now has 9 apples.\n3. Jane splits the 9 apples equally among her 2 siblings (2 people in total). 9 ÷ 2 = 4.5 apples each. Each person gets 4 apples."

PROMPT = (
    "What is the range of the numeric output of a sigmoid node in a neural network?"
)
RESPONSE1 = "The output of a sigmoid node is bounded between -1 and 1."
RESPONSE2 = "The output of a sigmoid node is bounded between 0 and 1."

CONVS = [
    [{"role": "user", "content": PROMPT}, {"role": "assistant", "content": RESPONSE1}],
    [{"role": "user", "content": PROMPT}, {"role": "assistant", "content": RESPONSE2}],
]


class TestRewardModels(unittest.TestCase):

    @classmethod
    def setUpClass(cls):
        mp.set_start_method("spawn", force=True)

    def assert_close_reward_scores(
        self,
        convs,
        model_path,
        tp_size,
        torch_dtype,
        tolerance,
    ) -> None:
        with HFRunner(
            model_path,
            torch_dtype=torch_dtype,
            model_type="reward",
        ) as hf_runner:
            hf_outputs = hf_runner.forward(convs)

        with SRTRunner(
            model_path,
            torch_dtype=torch_dtype,
            model_type="reward",
        ) as srt_runner:
            prompts = srt_runner.tokenizer.apply_chat_template(convs, tokenize=False)
            srt_outputs = srt_runner.forward(prompts)

        hf_scores = torch.tensor(hf_outputs.scores)
        srt_scores = torch.tensor(srt_outputs.scores)
        print(f"{hf_scores=}")
        print(f"{srt_scores=}")

        assert torch.all(
            abs(hf_scores - srt_scores) < tolerance
        ), "reward scores are not all close"

    def test_reward_scores(self):
        for model, tp_size, tolerance in MODELS:
            for torch_dtype in TORCH_DTYPES:
                self.assert_close_reward_scores(
                    CONVS, model, tp_size, torch_dtype, tolerance
                )


if __name__ == "__main__":
    unittest.main()