# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0

"""
Load generation script for SLA planner scaling tests.

This script uses aiperf to generate load at specific request rates
to test the planner's scaling behavior.
"""

import argparse
import asyncio
import json
import logging
import os
import signal
import tempfile
import time
from typing import Any, Dict, List, Optional

logging.basicConfig(
    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
)
logger = logging.getLogger(__name__)


class LoadGenerator:
    """Generate load using aiperf to test planner scaling."""

    def __init__(
        self,
        base_url: str = "http://localhost:8000",
        model: str = "nvidia/Llama-3.1-8B-Instruct-FP8",
        isl: int = 4000,
        osl: int = 150,
        save_results: bool = False,
    ):
        self.base_url = base_url
        self.model = model
        self.isl = isl
        self.osl = osl
        self.save_results = save_results

    def _calculate_aiperf_params(
        self,
        req_per_sec: float,
    ) -> Dict[str, Any]:
        """
        Calculate aiperf parameters to approximate desired request rate.

        Args:
            req_per_sec: Desired requests per second
            duration_sec: Test duration in seconds
            estimated_request_duration: Estimated average request duration in seconds

        Returns:
            Dictionary with concurrency and request_rate parameters
        """
        concurrency = max(1, int(req_per_sec * 3))

        return {
            "concurrency": concurrency,
            "request_rate": req_per_sec,
        }

    async def generate_load(
        self, req_per_sec: float, duration_sec: int, artifact_dir: Optional[str] = None
    ) -> Dict[str, Any]:
        """
        Generate load at specified request rate for given duration.

        Args:
            req_per_sec: Target requests per second
            duration_sec: Duration to generate load (seconds)
            artifact_dir: Directory to store aiperf artifacts

        Returns:
            Dictionary with load test results
        """
        logger.info(f"Generating load: {req_per_sec} req/s for {duration_sec}s")

        # Calculate aiperf parameters
        params = self._calculate_aiperf_params(req_per_sec)
        logger.info(f"Using request_rate={params['request_rate']} req/s")

        # Create artifact directory if not provided
        if artifact_dir is None:
            artifact_dir = tempfile.mkdtemp(prefix="scaling_test_")

        os.makedirs(artifact_dir, exist_ok=True)

        # Drive test length by caller-provided duration
        request_count = max(1, int(params["request_rate"] * duration_sec))

        logger.info(
            f"Adjusted parameters: duration={duration_sec}s, request_count={request_count}"
        )

        # Build aiperf command based on coworker's successful approach
        cmd = [
            "aiperf",
            "profile",
            "--model",
            self.model,
            "--tokenizer",
            self.model,
            "--endpoint-type",
            "chat",
            "--url",
            self.base_url.replace("http://", ""),
            "--streaming",
            "--synthetic-input-tokens-mean",
            str(self.isl),
            "--output-tokens-mean",
            str(self.osl),
            "--request-rate",
            str(params["request_rate"]),
            "--request-count",
            str(request_count),  # Use request count to limit test duration
            "--num-dataset-entries",
            str(
                max(20, int(params["request_rate"] * 10))
            ),  # Generate reasonable dataset size
            "--artifact-dir",
            artifact_dir,
            "-v",
        ]

        logger.info(f"Running command: {' '.join(cmd)}")
        logger.info(
            f"Expected duration: {duration_sec}s, timeout: {max(duration_sec * 2 + 120, int(duration_sec * 2.5))}s"
        )

        # Run aiperf (async)
        start_time = time.time()
        # More generous timeout for high-load tests - allow 2x duration + 2 minutes buffer
        timeout = max(duration_sec * 2 + 120, int(duration_sec * 2.5))

        # Write stdout/stderr to files instead of using PIPE. aiperf may fork
        # child processes that inherit pipe FDs; if those children outlive aiperf,
        # communicate() blocks forever waiting for EOF. File-based output avoids
        # this entirely. We also run aiperf in its own process group so that
        # os.killpg() can clean up the entire tree on timeout.
        stdout_path = os.path.join(artifact_dir, "aiperf.stdout.log")
        stderr_path = os.path.join(artifact_dir, "aiperf.stderr.log")
        try:
            with open(stdout_path, "wb") as stdout_f, open(
                stderr_path, "wb"
            ) as stderr_f:
                proc = await asyncio.create_subprocess_exec(
                    *cmd,
                    stdout=stdout_f,
                    stderr=stderr_f,
                    start_new_session=True,
                )
                try:
                    await asyncio.wait_for(proc.wait(), timeout=timeout)
                except asyncio.TimeoutError:
                    try:
                        os.killpg(proc.pid, signal.SIGKILL)
                    except ProcessLookupError:
                        pass
                    await proc.wait()
                    logger.error("aiperf timed out")
                    raise RuntimeError("Load generation timed out")

            end_time = time.time()
            actual_duration = end_time - start_time

            if proc.returncode == 0:
                logger.info("Load generation completed successfully")
                logger.info(f"Actual duration: {actual_duration:.2f}s")
                results = self._parse_aiperf_results(artifact_dir)
                results.update(
                    {
                        "requested_req_per_sec": req_per_sec,
                        "actual_duration": actual_duration,
                        "target_duration": duration_sec,
                        "aiperf_params": params,
                        "artifact_dir": artifact_dir,
                        "success": True,
                    }
                )
                return results
            else:
                logger.error(f"aiperf failed with return code {proc.returncode}")
                raise RuntimeError("aiperf failed; see logs in artifact dir")
        except RuntimeError:
            raise
        except Exception as e:
            logger.error(f"aiperf execution error: {e}")
            raise

    def _parse_aiperf_results(self, artifact_dir: str) -> Dict[str, Any]:
        """Parse aiperf results from artifact directory."""
        try:
            # Look for the profile_export_aiperf.json file
            json_files = [f for f in os.listdir(artifact_dir) if f.endswith(".json")]
            if not json_files:
                logger.warning("No JSON results found in artifact directory")
                return {}

            # Main results file
            results_file = None
            for json_file in json_files:
                if "profile_export" in json_file or "aiperf" in json_file:
                    results_file = os.path.join(artifact_dir, json_file)
                    break

            if not results_file:
                results_file = os.path.join(artifact_dir, json_files[0])

            logger.info(f"Parsing results from: {results_file}")

            with open(results_file, "r") as f:
                metrics = json.load(f)

            results = {
                "throughput": metrics.get("output_token_throughput", {}).get("avg", 0),
                "ttft_mean": metrics.get("time_to_first_token", {}).get("avg", 0),
                "itl_mean": metrics.get("inter_token_latency", {}).get("avg", 0),
                "end_to_end_latency_mean": metrics.get("request_latency", {}).get(
                    "avg", 0
                ),
            }
            logger.info(f"Parsed results: {results}")
            return results

        except Exception as e:
            logger.warning(f"Failed to parse aiperf results: {e}")
            return {}

    async def run_scaling_test(self, mode: str = "throughput") -> Dict[str, Any]:
        """
        Run a graduated scaling test for prefill scaling.

        Uses a conservative graduated approach:
        - Phase 1: 8 req/s (baseline, should maintain 1P1D)
        - Phase 2: 18 req/s (should trigger prefill scaling to 2P1D)

        Args:
            mode: Scaling mode - "throughput" or "load".
                  "load" uses a longer baseline for regression warmup.

        Returns:
            Dictionary with complete test results
        """
        logger.info(
            f"Starting graduated prefill scaling test scenario (targeting 1P1D -> 2P1D, mode={mode})"
        )
        logger.info("Using conservative graduated approach with metric generation")

        # Graduated test parameters (optimized for prefill scaling)
        # Load-based scaling needs longer baseline for regression warmup
        baseline_duration = 120 if mode == "load" else 90
        phases: List[Dict[str, Any]] = [
            {"rate": 8.0, "duration": baseline_duration, "name": "baseline"},
            {"rate": 18.0, "duration": 120, "name": "prefill_scaling_trigger"},
        ]
        transition_delay = 30

        # Create artifact directory
        timestamp = int(time.time())
        if self.save_results:
            script_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
            base_dir = os.path.join(
                script_dir, "e2e_scaling_results", f"scaling_test_{timestamp}"
            )
        else:
            base_dir = f"/tmp/scaling_test_{timestamp}"

        os.makedirs(base_dir, exist_ok=True)
        logger.info(f"Saving results to: {base_dir}")

        results = {
            "test_timestamp": timestamp,
            "config": {
                "approach": "graduated_scaling",
                "phases": phases,
                "transition_delay": transition_delay,
                "isl": self.isl,
                "osl": self.osl,
                "model": self.model,
            },
        }

        try:
            phase_results = {}

            for i, phase in enumerate(phases):
                phase_name = f"phase{i+1}_{phase['name']}"
                logger.info(
                    f"Starting {phase_name}: {phase['rate']} req/s for {phase['duration']}s"
                )

                phase_dir = os.path.join(base_dir, phase_name)
                phase_result = await self.generate_load(
                    req_per_sec=phase["rate"],
                    duration_sec=phase["duration"],
                    artifact_dir=phase_dir,
                )
                phase_results[phase_name] = phase_result

                # Add transition delay except after last phase
                if i < len(phases) - 1:
                    logger.info(f"Transition delay: {transition_delay}s")
                    await asyncio.sleep(transition_delay)

            results["phase_results"] = phase_results
            logger.info("Graduated scaling test completed successfully")

        except Exception as e:
            logger.error(f"Scaling test failed: {e}")
            results["error"] = str(e)
            raise

        # Save results
        results_file = os.path.join(base_dir, "scaling_test_results.json")
        with open(results_file, "w") as f:
            json.dump(results, f, indent=2)

        logger.info(f"Test results saved to: {results_file}")
        return results


async def main():
    """Main function for scaling test execution."""
    parser = argparse.ArgumentParser(
        description="SLA Planner Graduated Scaling Test - Optimized for 2P1D prefill scaling"
    )
    parser.add_argument(
        "--base-url",
        default="http://localhost:8000",
        help="Service URL (default: http://localhost:8000)",
    )
    parser.add_argument(
        "--model",
        default="nvidia/Llama-3.1-8B-Instruct-FP8",
        help="Model name (default: nvidia/Llama-3.1-8B-Instruct-FP8)",
    )
    parser.add_argument(
        "--isl",
        type=int,
        default=4000,
        help="Input sequence length - optimized for prefill scaling (default: 4000)",
    )
    parser.add_argument(
        "--osl",
        type=int,
        default=150,
        help="Output sequence length - optimized for prefill scaling (default: 150)",
    )
    parser.add_argument(
        "--save-results",
        action="store_true",
        help="Save results to components/src/dynamo/planner/tests/data instead of /tmp",
    )

    args = parser.parse_args()

    generator = LoadGenerator(
        base_url=args.base_url,
        model=args.model,
        isl=args.isl,
        osl=args.osl,
        save_results=args.save_results,
    )

    print("Starting SLA Planner Graduated Scaling Test...")
    print(f"Parameters: ISL={args.isl}, OSL={args.osl}")
    print(
        "Test phases: 8 -> 15 -> 25 req/s (optimized for 1P1D -> 2P1D prefill scaling)"
    )

    results = await generator.run_scaling_test()

    print("\n" + "=" * 60)
    print("SCALING TEST COMPLETED")
    print("=" * 60)

    # Print results summary
    phase_results = results.get("phase_results", {})
    for phase_name, phase_data in phase_results.items():
        ok = isinstance(phase_data, dict) and phase_data.get(
            "success", bool(phase_data)
        )
        if ok:
            duration = phase_data.get("actual_duration")
            if isinstance(duration, (int, float)):
                print(f"{phase_name}: {duration:.1f}s duration - SUCCESS")
            else:
                print(f"{phase_name}: SUCCESS")
        else:
            print(f"{phase_name}: FAILED")
    print("\nResults saved to scaling test directory")


if __name__ == "__main__":
    asyncio.run(main())
