"""Nightly performance benchmark for DeepSeek-V3.1 model.

This test benchmarks the DeepSeek-V3.1 model with basic and MTP configurations on 8 GPUs.

The model path can be configured via DEEPSEEK_V31_MODEL_PATH environment variable.

Example usage:
    DEEPSEEK_V31_MODEL_PATH=deepseek-ai/DeepSeek-V3.1 python -m pytest test_deepseek_v31_perf.py -v
"""

import os
import unittest
from typing import List

from sglang.test.ci.ci_register import register_amd_ci
from sglang.test.nightly_bench_utils import BenchmarkResult
from sglang.test.nightly_utils import NightlyBenchmarkRunner
from sglang.test.test_utils import DEFAULT_URL_FOR_TEST, _parse_int_list_env

# Register for AMD CI - DeepSeek-V3.1 benchmark (basic + MTP, ~300 min)
register_amd_ci(est_time=18000, suite="nightly-perf-8-gpu-deepseek-v31", nightly=True)


def generate_simple_markdown_report(results: List[BenchmarkResult]) -> str:
    """Generate a simplified markdown report without traces and cost columns.

    Skips the first result if it's a warmup run (duplicate batch_size).
    """
    model_header = results[0].model_path
    if results[0].run_name and results[0].run_name != "default":
        model_header += f" ({results[0].run_name})"

    gpu_config = os.getenv("GPU_CONFIG", "")
    if gpu_config:
        model_header += f" [{gpu_config}]"

    summary = f"### {model_header}\n"
    summary += "| batch size | input len | latency (s) | input throughput (tok/s) | output throughput (tok/s) | ITL (ms) |\n"
    summary += "| ---------- | --------- | ----------- | ------------------------ | ------------------------- | -------- |\n"

    # Skip first result if it's a warmup (same batch_size as second result)
    report_results = (
        results[1:]
        if len(results) > 1 and results[0].batch_size == results[1].batch_size
        else results
    )

    for result in report_results:
        itl = 1 / (result.output_throughput / result.batch_size) * 1000
        summary += f"| {result.batch_size} | {result.input_len} | {result.latency:.2f} | {result.input_throughput:.2f} | {result.output_throughput:.2f} | {itl:.2f} |\n"

    return summary


# Model path can be overridden via environment variable
DEEPSEEK_V31_MODEL_PATH = os.environ.get(
    "DEEPSEEK_V31_MODEL_PATH", "deepseek-ai/DeepSeek-V3.1"
)
PROFILE_DIR = "performance_profiles_deepseek_v31"


class TestNightlyDeepseekV31Performance(unittest.TestCase):
    """Nightly performance benchmark for DeepSeek-V3.1 model.

    Tests the DeepSeek-V3.1 model with both basic and MTP configurations on TP=8.
    """

    @classmethod
    def setUpClass(cls):
        cls.model = DEEPSEEK_V31_MODEL_PATH
        cls.base_url = DEFAULT_URL_FOR_TEST
        cls.batch_sizes = [1, 8, 16, 64]
        cls.input_lens = tuple(_parse_int_list_env("NIGHTLY_INPUT_LENS", "4096"))
        cls.output_lens = tuple(_parse_int_list_env("NIGHTLY_OUTPUT_LENS", "512"))

        # Define variant configurations for DeepSeek-V3.1
        cls.variants = [
            {
                "name": "basic",
                "other_args": [
                    "--trust-remote-code",
                    "--tp",
                    "8",
                    "--mem-fraction-static",
                    "0.85",
                    "--model-loader-extra-config",
                    '{"enable_multithread_load": true}',
                ],
            },
            {
                "name": "mtp",
                "other_args": [
                    "--trust-remote-code",
                    "--tp",
                    "8",
                    "--speculative-algorithm",
                    "EAGLE",
                    "--speculative-num-steps",
                    "3",
                    "--speculative-eagle-topk",
                    "1",
                    "--speculative-num-draft-tokens",
                    "4",
                    "--mem-fraction-static",
                    "0.7",
                    "--model-loader-extra-config",
                    '{"enable_multithread_load": true}',
                ],
            },
        ]

        cls.runner = NightlyBenchmarkRunner(PROFILE_DIR, cls.__name__, cls.base_url)
        cls.runner.setup_profile_directory()
        # Override full_report to remove traces help text
        cls.runner.full_report = f"## {cls.__name__}\n"

    def test_bench_one_batch(self):
        """Run benchmark across all configured variants."""
        failed_variants = []

        try:
            for variant_config in self.variants:
                with self.subTest(variant=variant_config["name"]):
                    result_tuple = self.runner.run_benchmark_for_model(
                        model_path=self.model,
                        batch_sizes=self.batch_sizes,
                        input_lens=self.input_lens,
                        output_lens=self.output_lens,
                        other_args=variant_config["other_args"],
                        variant=variant_config["name"],
                        extra_bench_args=["--trust-remote-code"],
                        enable_profile=False,  # Disable profiling for AMD tests
                    )
                    results = result_tuple[0]
                    success = result_tuple[1]

                    if not success:
                        failed_variants.append(variant_config["name"])

                    # Use simplified report format without traces
                    if results:
                        self.runner.full_report += (
                            generate_simple_markdown_report(results) + "\n"
                        )
        finally:
            self.runner.write_final_report()

        if failed_variants:
            raise AssertionError(
                f"Benchmark failed for {self.model} with the following variants: "
                f"{', '.join(failed_variants)}"
            )


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