"""MI35x Nightly performance benchmark for DeepSeek-R1-MXFP4 model.

This test benchmarks the DeepSeek-R1-MXFP4 quantized model on MI35x with 8 GPUs.

The model path can be configured via DEEPSEEK_R1_MXFP4_MODEL_PATH environment variable.

Registry: nightly-perf-8-gpu-mi35x-deepseek-r1-mxfp4 suite

Example usage:
    DEEPSEEK_R1_MXFP4_MODEL_PATH=/data2/models/amd-DeepSeek-R1-MXFP4-Preview python -m pytest test_deepseek_r1_mxfp4_perf_mi35x.py -v
"""

import os

# Set HF cache to /data2/models/ for MI35x so HF models download there
os.environ.setdefault("HF_HOME", "/data2/models/huggingface")
os.environ.setdefault("HF_HUB_CACHE", "/data2/models/huggingface/hub")
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-R1-MXFP4 benchmark on MI35x (~300 min)
register_amd_ci(
    est_time=18000, suite="nightly-perf-8-gpu-mi35x-deepseek-r1-mxfp4", 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", "MI35x")
    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 configuration for MI35x DeepSeek-R1-MXFP4
# Priority: 1) env var, 2) local path, 3) HuggingFace model ID
DEEPSEEK_R1_MXFP4_LOCAL_PATH = "/data2/models/amd-DeepSeek-R1-MXFP4-Preview"
DEEPSEEK_R1_MXFP4_HF_MODEL_ID = "amd/DeepSeek-R1-MXFP4-Preview"
PROFILE_DIR = "performance_profiles_deepseek_r1_mxfp4_mi35x"


def get_model_path() -> str:
    """Get effective model path: env var > local path > HF model ID."""
    # Check env var first
    env_path = os.environ.get("DEEPSEEK_R1_MXFP4_MODEL_PATH")
    if env_path:
        return env_path
    # Check local path
    if os.path.exists(DEEPSEEK_R1_MXFP4_LOCAL_PATH):
        return DEEPSEEK_R1_MXFP4_LOCAL_PATH
    # Fall back to HF model ID
    return DEEPSEEK_R1_MXFP4_HF_MODEL_ID


class TestDeepseekR1MXFP4PerfMI35x(unittest.TestCase):
    """MI35x Nightly performance benchmark for DeepSeek-R1-MXFP4 model.

    Tests the DeepSeek-R1-MXFP4 quantized model on TP=8 with DP=8.
    Uses local path if available, otherwise downloads from HuggingFace.
    """

    @classmethod
    def setUpClass(cls):
        cls.model = get_model_path()
        print(f"Using model path: {cls.model}")
        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-R1-MXFP4 on MI35x
        # Only run basic variant for perf (DP/TC/MTP covered in accuracy tests)
        cls.variants = [
            {
                "name": "basic",
                "other_args": [
                    "--trust-remote-code",
                    "--tp",
                    "8",
                    "--chunked-prefill-size",
                    "131072",
                    "--disable-radix-cache",
                    "--mem-fraction-static",
                    "0.85",
                ],
            },
        ]

        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 = []

        # For local paths, check if exists. HF model IDs will download automatically.
        is_local_path = self.model.startswith("/")
        if is_local_path and not os.path.exists(self.model):
            print(f"\n⏭️ SKIPPING: Local model not found at {self.model}")
            self.runner.full_report += (
                f"\n⏭️ Test skipped: Local model not found at {self.model}\n"
            )
            self.runner.write_final_report()
            return

        # Log model source
        if is_local_path:
            print(f"📁 Using local model: {self.model}")
        else:
            print(
                f"📥 Using HuggingFace model: {self.model} (will download if not cached)"
            )

        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()
