"""
python3 -m unittest test_deepseek_ocr.py
"""

import gc
import json
import os
import unittest
from pathlib import Path

import requests
from transformers import AutoTokenizer

from sglang.srt.utils import kill_process_tree
from sglang.test.test_utils import (
    DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
    DEFAULT_URL_FOR_TEST,
    CustomTestCase,
    popen_launch_server,
)


class TestDeepSeekOCR(CustomTestCase):
    @classmethod
    def _cleanup_xpu_memory(cls):
        gc.collect()
        try:
            import torch

            if hasattr(torch, "xpu") and torch.xpu.is_available():
                torch.xpu.synchronize()
                torch.xpu.empty_cache()
        except Exception:
            # Best-effort cleanup only; tests should continue if cleanup is unavailable.
            pass

    @classmethod
    def setUpClass(cls):
        cls._cleanup_xpu_memory()
        cls.model = "deepseek-ai/DeepSeek-OCR"
        cls.tokenizer = AutoTokenizer.from_pretrained(
            cls.model, use_fast=False, trust_remote_code=True
        )
        cls.base_url = DEFAULT_URL_FOR_TEST
        cls.image_path = str(
            (Path(__file__).resolve().parents[3] / "examples/assets/example_image.png")
        )
        if not os.path.exists(cls.image_path):
            raise FileNotFoundError(f"Image not found: {cls.image_path}")
        cls.common_args = [
            "--device",
            "xpu",
            "--attention-backend",
            "intel_xpu",
        ]
        os.environ["SGLANG_USE_SGL_XPU"] = "1"
        cls.process = popen_launch_server(
            cls.model,
            cls.base_url,
            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
            other_args=[
                *cls.common_args,
            ],
        )

    @classmethod
    def tearDownClass(cls):
        """Fixture that is run once after all tests in the class."""
        if hasattr(cls, "process") and cls.process:
            kill_process_tree(cls.process.pid)
        cls._cleanup_xpu_memory()

    def get_request_json(self, max_new_tokens=32, n=1):
        response = requests.post(
            self.base_url + "/generate",
            json={
                "text": "<image>\n<|grounding|>Convert the document to pure text.",
                "image_data": self.image_path,
                "sampling_params": {
                    "temperature": 0 if n == 1 else 0.5,
                    "max_new_tokens": max_new_tokens,
                },
            },
        )
        return response.json()

    def run_decode(
        self,
        max_new_tokens=128,
        n=1,
    ):

        ret = self.get_request_json(max_new_tokens=max_new_tokens, n=n)
        print(json.dumps(ret, indent=2))

        def assert_one_item(item):
            if item["meta_info"]["finish_reason"]["type"] == "stop":
                self.assertEqual(
                    item["meta_info"]["finish_reason"]["matched"],
                    self.tokenizer.eos_token_id,
                )
            elif item["meta_info"]["finish_reason"]["type"] == "length":
                self.assertEqual(
                    len(item["output_ids"]), item["meta_info"]["completion_tokens"]
                )
                self.assertEqual(len(item["output_ids"]), max_new_tokens)

        # Determine whether to assert a single item or multiple items based on n
        if n == 1:
            assert_one_item(ret)
        else:
            self.assertEqual(len(ret), n)
            for i in range(n):
                assert_one_item(ret[i])

        print("=" * 100)

    def test_moe(self):
        self.run_decode()


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