#!/usr/bin/env python3
  """Benchmark local translation models with products_analyzed.csv."""
  
  from __future__ import annotations
  
  import argparse

  import concurrent.futures

  import copy
  import csv
  import json
  import math
  import platform
  import resource
  import statistics
  import subprocess
  import sys
  import time
  from datetime import datetime
  from pathlib import Path

  from typing import Any, Dict, Iterable, List, Sequence

  
  import torch
  import transformers
  
  PROJECT_ROOT = Path(__file__).resolve().parent.parent
  if str(PROJECT_ROOT) not in sys.path:
      sys.path.insert(0, str(PROJECT_ROOT))
  
  from config.services_config import get_translation_config  # noqa: E402
  from translation.service import TranslationService  # noqa: E402
  from translation.settings import get_translation_capability  # noqa: E402
  
  

  DEFAULT_BATCH_SIZES = [1, 4, 8, 16, 32, 64]
  DEFAULT_CONCURRENCIES = [1, 2, 4, 8, 16, 64]
  

  SCENARIOS: List[Dict[str, str]] = [
      {
          "name": "nllb-200-distilled-600m zh->en",
          "model": "nllb-200-distilled-600m",
          "source_lang": "zh",
          "target_lang": "en",
          "column": "title_cn",
          "scene": "sku_name",
      },
      {
          "name": "nllb-200-distilled-600m en->zh",
          "model": "nllb-200-distilled-600m",
          "source_lang": "en",
          "target_lang": "zh",
          "column": "title",
          "scene": "sku_name",
      },
      {
          "name": "opus-mt-zh-en zh->en",
          "model": "opus-mt-zh-en",
          "source_lang": "zh",
          "target_lang": "en",
          "column": "title_cn",
          "scene": "sku_name",
      },
      {
          "name": "opus-mt-en-zh en->zh",
          "model": "opus-mt-en-zh",
          "source_lang": "en",
          "target_lang": "zh",
          "column": "title",
          "scene": "sku_name",
      },
  ]
  
  
  def parse_args() -> argparse.Namespace:
      parser = argparse.ArgumentParser(description="Benchmark local translation models")
      parser.add_argument("--csv-path", default="products_analyzed.csv", help="Benchmark dataset CSV path")

      parser.add_argument("--limit", type=int, default=0, help="Limit rows for baseline or single-case run; 0 means all")

      parser.add_argument("--output-dir", default="", help="Directory for JSON/Markdown reports")
      parser.add_argument("--single", action="store_true", help="Run a single scenario in-process")
      parser.add_argument("--model", default="", help="Model name for --single mode")
      parser.add_argument("--source-lang", default="", help="Source language for --single mode")
      parser.add_argument("--target-lang", default="", help="Target language for --single mode")
      parser.add_argument("--column", default="", help="CSV column to benchmark for --single mode")
      parser.add_argument("--scene", default="sku_name", help="Scene passed to translation service")
      parser.add_argument("--batch-size", type=int, default=0, help="Override configured batch size")
      parser.add_argument("--device-override", default="", help="Override configured device, for example cpu or cuda")
      parser.add_argument("--torch-dtype-override", default="", help="Override configured torch dtype, for example float32 or float16")

      parser.add_argument("--max-new-tokens", type=int, default=0, help="Override configured max_new_tokens")
      parser.add_argument("--num-beams", type=int, default=0, help="Override configured num_beams")
      parser.add_argument("--attn-implementation", default="", help="Override attention implementation, for example sdpa")

      parser.add_argument("--warmup-batches", type=int, default=1, help="Warmup batches before measuring")

      parser.add_argument("--disable-cache", action="store_true", help="Disable translation cache during benchmarks")
      parser.add_argument(
          "--suite",
          choices=["baseline", "extended"],
          default="baseline",
          help="baseline keeps the previous all-scenarios summary; extended adds batch/concurrency/matrix sweeps",
      )
      parser.add_argument(
          "--batch-size-list",
          default="",
          help="Comma-separated batch sizes for extended suite; default 1,4,8,16,32,64",
      )
      parser.add_argument(
          "--concurrency-list",
          default="",
          help="Comma-separated concurrency levels for extended suite; default 1,2,4,8,16,64",
      )
      parser.add_argument(
          "--serial-items-per-case",
          type=int,
          default=512,
          help="Items per batch-size case in extended suite",
      )
      parser.add_argument(
          "--concurrency-requests-per-case",
          type=int,
          default=128,
          help="Requests per concurrency or matrix case in extended suite",
      )
      parser.add_argument(
          "--concurrency-batch-size",
          type=int,
          default=1,
          help="Batch size used by the dedicated concurrency sweep",
      )
      parser.add_argument(
          "--max-batch-concurrency-product",
          type=int,
          default=128,
          help="Skip matrix cases where batch_size * concurrency exceeds this value; 0 disables the limit",
      )

      return parser.parse_args()
  
  

  def parse_csv_ints(raw: str, fallback: Sequence[int]) -> List[int]:
      if not raw.strip():
          return list(fallback)
      values: List[int] = []
      for item in raw.split(","):
          stripped = item.strip()
          if not stripped:
              continue
          value = int(stripped)
          if value <= 0:
              raise ValueError(f"Expected positive integer, got {value}")
          values.append(value)
      if not values:
          raise ValueError("Parsed empty integer list")
      return values
  
  

  def load_texts(csv_path: Path, column: str, limit: int) -> List[str]:
      texts: List[str] = []
      with csv_path.open("r", encoding="utf-8") as handle:
          reader = csv.DictReader(handle)
          for row in reader:
              value = (row.get(column) or "").strip()
              if value:
                  texts.append(value)
              if limit > 0 and len(texts) >= limit:
                  break
      if not texts:
          raise ValueError(f"No non-empty texts found in column '{column}' from {csv_path}")
      return texts
  
  

  def batched(values: Sequence[str], batch_size: int) -> Iterable[List[str]]:

      for start in range(0, len(values), batch_size):

          yield list(values[start:start + batch_size])

  
  
  def percentile(values: List[float], p: float) -> float:
      if not values:
          return 0.0
      ordered = sorted(values)
      if len(values) == 1:
          return float(ordered[0])
      idx = (len(ordered) - 1) * p
      lower = math.floor(idx)
      upper = math.ceil(idx)
      if lower == upper:
          return float(ordered[lower])
      return float(ordered[lower] + (ordered[upper] - ordered[lower]) * (idx - lower))
  
  
  def resolve_output_dir(output_dir: str) -> Path:
      if output_dir:
          path = Path(output_dir)
      else:
          path = PROJECT_ROOT / "perf_reports" / datetime.now().strftime("%Y%m%d") / "translation_local_models"
      path.mkdir(parents=True, exist_ok=True)
      return path
  
  
  def build_environment_info() -> Dict[str, Any]:
      gpu_name = None
      gpu_total_mem_gb = None
      if torch.cuda.is_available():
          gpu_name = torch.cuda.get_device_name(0)
          props = torch.cuda.get_device_properties(0)
          gpu_total_mem_gb = round(props.total_memory / (1024 ** 3), 2)
      return {
          "python": platform.python_version(),
          "torch": torch.__version__,
          "transformers": transformers.__version__,
          "cuda_available": torch.cuda.is_available(),
          "gpu_name": gpu_name,
          "gpu_total_mem_gb": gpu_total_mem_gb,
          "platform": platform.platform(),
      }
  
  

  def scenario_from_args(args: argparse.Namespace) -> Dict[str, str]:
      return {
          "name": f"{args.model} {args.source_lang}->{args.target_lang}",
          "model": args.model,
          "source_lang": args.source_lang,
          "target_lang": args.target_lang,
          "column": args.column,
          "scene": args.scene,
      }
  
  
  def build_config_and_capability(
      args: argparse.Namespace,
      *,
      batch_size_override: int | None = None,
  ) -> tuple[Dict[str, Any], Dict[str, Any]]:

      config = copy.deepcopy(get_translation_config())

      for name, cfg in config["capabilities"].items():
          cfg["enabled"] = name == args.model
      config["default_model"] = args.model

      capability = get_translation_capability(config, args.model, require_enabled=False)
      if args.device_override:
          capability["device"] = args.device_override
      if args.torch_dtype_override:
          capability["torch_dtype"] = args.torch_dtype_override

      if batch_size_override is not None:
          capability["batch_size"] = batch_size_override
      elif args.batch_size:

          capability["batch_size"] = args.batch_size

      if args.max_new_tokens:
          capability["max_new_tokens"] = args.max_new_tokens
      if args.num_beams:
          capability["num_beams"] = args.num_beams
      if args.attn_implementation:
          capability["attn_implementation"] = args.attn_implementation

      if args.disable_cache:
          capability["use_cache"] = False

      config["capabilities"][args.model] = capability

      return config, capability

  
  def ensure_cuda_stats_reset() -> None:

      if torch.cuda.is_available():
          torch.cuda.empty_cache()
          torch.cuda.reset_peak_memory_stats()
  

  def build_memory_metrics() -> Dict[str, Any]:
      peak_gpu_mem_gb = None
      peak_gpu_reserved_gb = None
      if torch.cuda.is_available():
          peak_gpu_mem_gb = round(torch.cuda.max_memory_allocated() / (1024 ** 3), 3)
          peak_gpu_reserved_gb = round(torch.cuda.max_memory_reserved() / (1024 ** 3), 3)
      max_rss_mb = round(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024, 2)
      return {
          "max_rss_mb": max_rss_mb,
          "peak_gpu_memory_gb": peak_gpu_mem_gb,
          "peak_gpu_reserved_gb": peak_gpu_reserved_gb,
      }
  
  
  def make_request_payload(batch: Sequence[str]) -> str | List[str]:
      if len(batch) == 1:
          return batch[0]
      return list(batch)
  
  
  def benchmark_serial_case(
      *,
      service: TranslationService,
      backend: Any,
      scenario: Dict[str, str],
      capability: Dict[str, Any],
      texts: List[str],
      batch_size: int,
      warmup_batches: int,
  ) -> Dict[str, Any]:
      backend.batch_size = batch_size
      measured_batches = list(batched(texts, batch_size))
      warmup_count = min(max(warmup_batches, 0), len(measured_batches))
  
      for batch in measured_batches[:warmup_count]:

          service.translate(

              text=make_request_payload(batch),
              source_lang=scenario["source_lang"],
              target_lang=scenario["target_lang"],
              model=scenario["model"],
              scene=scenario["scene"],

          )
  
      batch_latencies_ms: List[float] = []
      success_count = 0
      failure_count = 0
      output_chars = 0
      total_input_chars = sum(len(text) for text in texts)

  
      start = time.perf_counter()
      for batch in measured_batches:
          batch_start = time.perf_counter()
          outputs = service.translate(

              text=make_request_payload(batch),
              source_lang=scenario["source_lang"],
              target_lang=scenario["target_lang"],
              model=scenario["model"],
              scene=scenario["scene"],

          )
          elapsed_ms = (time.perf_counter() - batch_start) * 1000
          batch_latencies_ms.append(elapsed_ms)
  

          if isinstance(outputs, list):
              result_items = outputs
          else:
              result_items = [outputs]
          for item in result_items:

              if item is None:
                  failure_count += 1
              else:
                  success_count += 1
                  output_chars += len(item)
      translate_seconds = time.perf_counter() - start

      total_items = len(texts)
      memory = build_memory_metrics()

      return {
          "mode": "serial_batch",
          "batch_size": batch_size,
          "concurrency": 1,
          "rows": total_items,
          "requests": len(measured_batches),
          "input_chars": total_input_chars,
          "load_seconds": 0.0,
          "translate_seconds": round(translate_seconds, 4),
          "total_seconds": round(translate_seconds, 4),
          "batch_count": len(batch_latencies_ms),
          "request_latency_p50_ms": round(percentile(batch_latencies_ms, 0.50), 2),
          "request_latency_p95_ms": round(percentile(batch_latencies_ms, 0.95), 2),
          "request_latency_max_ms": round(max(batch_latencies_ms), 2),
          "avg_request_latency_ms": round(statistics.fmean(batch_latencies_ms), 2),
          "avg_item_latency_ms": round((translate_seconds / total_items) * 1000, 3),
          "requests_per_second": round(len(measured_batches) / translate_seconds, 2),
          "items_per_second": round(total_items / translate_seconds, 2),
          "input_chars_per_second": round(total_input_chars / translate_seconds, 2),
          "output_chars_per_second": round(output_chars / translate_seconds, 2),
          "success_count": success_count,
          "failure_count": failure_count,
          "success_rate": round(success_count / total_items, 6),
          "device": str(getattr(backend, "device", capability.get("device", "unknown"))),
          "torch_dtype": str(getattr(backend, "torch_dtype", capability.get("torch_dtype", "unknown"))),
          "configured_batch_size": int(capability.get("batch_size") or batch_size),
          "used_batch_size": batch_size,
          "warmup_batches": warmup_count,
          **memory,
      }

  
  def benchmark_concurrency_case(
      *,
      service: TranslationService,
      backend: Any,
      scenario: Dict[str, str],
      capability: Dict[str, Any],
      texts: List[str],
      batch_size: int,
      concurrency: int,
      requests_per_case: int,
      warmup_batches: int,
  ) -> Dict[str, Any]:
      backend.batch_size = batch_size
      required_items = batch_size * requests_per_case
      case_texts = texts[:required_items]
      request_batches = list(batched(case_texts, batch_size))
      if not request_batches:
          raise ValueError("No request batches prepared for concurrency benchmark")
      warmup_count = min(max(warmup_batches, 0), len(request_batches))
  
      for batch in request_batches[:warmup_count]:
          service.translate(
              text=make_request_payload(batch),
              source_lang=scenario["source_lang"],
              target_lang=scenario["target_lang"],
              model=scenario["model"],
              scene=scenario["scene"],
          )
  
      request_latencies_ms: List[float] = []
      success_count = 0
      failure_count = 0
      output_chars = 0
      total_input_chars = sum(len(text) for text in case_texts)
  
      def worker(batch: List[str]) -> tuple[float, int, int, int]:
          started = time.perf_counter()
          outputs = service.translate(
              text=make_request_payload(batch),
              source_lang=scenario["source_lang"],
              target_lang=scenario["target_lang"],
              model=scenario["model"],
              scene=scenario["scene"],
          )
          elapsed_ms = (time.perf_counter() - started) * 1000
          if isinstance(outputs, list):
              result_items = outputs
          else:
              result_items = [outputs]
          local_success = 0
          local_failure = 0
          local_output_chars = 0
          for item in result_items:
              if item is None:
                  local_failure += 1
              else:
                  local_success += 1
                  local_output_chars += len(item)
          return elapsed_ms, local_success, local_failure, local_output_chars
  
      wall_start = time.perf_counter()
      with concurrent.futures.ThreadPoolExecutor(max_workers=concurrency) as executor:
          futures = [executor.submit(worker, batch) for batch in request_batches]
          for future in concurrent.futures.as_completed(futures):
              latency_ms, local_success, local_failure, local_output_chars = future.result()
              request_latencies_ms.append(latency_ms)
              success_count += local_success
              failure_count += local_failure
              output_chars += local_output_chars
      wall_seconds = time.perf_counter() - wall_start
      total_items = len(case_texts)
      memory = build_memory_metrics()

  
      return {

          "mode": "concurrency",
          "batch_size": batch_size,
          "concurrency": concurrency,
          "rows": total_items,
          "requests": len(request_batches),
          "input_chars": total_input_chars,
          "load_seconds": 0.0,
          "translate_seconds": round(wall_seconds, 4),
          "total_seconds": round(wall_seconds, 4),
          "batch_count": len(request_latencies_ms),
          "request_latency_p50_ms": round(percentile(request_latencies_ms, 0.50), 2),
          "request_latency_p95_ms": round(percentile(request_latencies_ms, 0.95), 2),
          "request_latency_max_ms": round(max(request_latencies_ms), 2),
          "avg_request_latency_ms": round(statistics.fmean(request_latencies_ms), 2),
          "avg_item_latency_ms": round((wall_seconds / total_items) * 1000, 3),
          "requests_per_second": round(len(request_batches) / wall_seconds, 2),
          "items_per_second": round(total_items / wall_seconds, 2),
          "input_chars_per_second": round(total_input_chars / wall_seconds, 2),
          "output_chars_per_second": round(output_chars / wall_seconds, 2),
          "success_count": success_count,
          "failure_count": failure_count,
          "success_rate": round(success_count / total_items, 6),
          "device": str(getattr(backend, "device", capability.get("device", "unknown"))),
          "torch_dtype": str(getattr(backend, "torch_dtype", capability.get("torch_dtype", "unknown"))),
          "configured_batch_size": int(capability.get("batch_size") or batch_size),
          "used_batch_size": batch_size,
          "warmup_batches": warmup_count,
          **memory,
      }
  
  
  def benchmark_single_scenario(args: argparse.Namespace) -> Dict[str, Any]:
      csv_path = (PROJECT_ROOT / args.csv_path).resolve() if not Path(args.csv_path).is_absolute() else Path(args.csv_path)
      scenario = scenario_from_args(args)
      config, capability = build_config_and_capability(args)
      configured_batch_size = int(capability.get("batch_size") or 1)
      batch_size = configured_batch_size
      texts = load_texts(csv_path, args.column, args.limit)
  
      ensure_cuda_stats_reset()
      load_start = time.perf_counter()
      service = TranslationService(config)
      backend = service.get_backend(args.model)
      load_seconds = time.perf_counter() - load_start
  
      runtime = benchmark_serial_case(
          service=service,
          backend=backend,
          scenario=scenario,
          capability=capability,
          texts=texts,
          batch_size=batch_size,
          warmup_batches=args.warmup_batches,
      )
      runtime["load_seconds"] = round(load_seconds, 4)
      runtime["total_seconds"] = round(runtime["load_seconds"] + runtime["translate_seconds"], 4)
  
      return {
          "scenario": scenario,
          "dataset": {
              "csv_path": str(csv_path),
              "rows": len(texts),
              "input_chars": sum(len(text) for text in texts),

          },

          "runtime": runtime,
      }
  
  
  def benchmark_extended_scenario(args: argparse.Namespace) -> Dict[str, Any]:
      csv_path = (PROJECT_ROOT / args.csv_path).resolve() if not Path(args.csv_path).is_absolute() else Path(args.csv_path)
      scenario = scenario_from_args(args)
      batch_sizes = parse_csv_ints(args.batch_size_list, DEFAULT_BATCH_SIZES)
      concurrencies = parse_csv_ints(args.concurrency_list, DEFAULT_CONCURRENCIES)
      largest_batch = max(batch_sizes + [args.concurrency_batch_size])
      largest_concurrency = max(concurrencies)
      max_product = args.max_batch_concurrency_product
      required_items = max(
          args.limit or 0,
          max(args.serial_items_per_case, largest_batch),
          args.concurrency_requests_per_case * args.concurrency_batch_size,
          largest_batch * args.concurrency_requests_per_case,
      )
      texts = load_texts(csv_path, args.column, required_items)
      config, capability = build_config_and_capability(args)
  
      ensure_cuda_stats_reset()
      load_start = time.perf_counter()
      service = TranslationService(config)
      backend = service.get_backend(args.model)
      load_seconds = time.perf_counter() - load_start
  
      batch_sweep: List[Dict[str, Any]] = []
      concurrency_sweep: List[Dict[str, Any]] = []
      matrix_results: List[Dict[str, Any]] = []
  
      for batch_size in batch_sizes:
          case_texts = texts[: max(batch_size, args.serial_items_per_case)]
          batch_sweep.append(
              benchmark_serial_case(
                  service=service,
                  backend=backend,
                  scenario=scenario,
                  capability=capability,
                  texts=case_texts,
                  batch_size=batch_size,
                  warmup_batches=args.warmup_batches,
              )
          )
  
      for concurrency in concurrencies:
          concurrency_sweep.append(
              benchmark_concurrency_case(
                  service=service,
                  backend=backend,
                  scenario=scenario,
                  capability=capability,
                  texts=texts,
                  batch_size=args.concurrency_batch_size,
                  concurrency=concurrency,
                  requests_per_case=args.concurrency_requests_per_case,
                  warmup_batches=args.warmup_batches,
              )
          )
  
      for batch_size in batch_sizes:
          for concurrency in concurrencies:
              if max_product > 0 and batch_size * concurrency > max_product:
                  continue
              matrix_results.append(
                  benchmark_concurrency_case(
                      service=service,
                      backend=backend,
                      scenario=scenario,
                      capability=capability,
                      texts=texts,
                      batch_size=batch_size,
                      concurrency=concurrency,
                      requests_per_case=args.concurrency_requests_per_case,
                      warmup_batches=args.warmup_batches,
                  )
              )
  
      for collection in (batch_sweep, concurrency_sweep, matrix_results):
          for idx, item in enumerate(collection):
              item["load_seconds"] = round(load_seconds if idx == 0 else 0.0, 4)
              item["total_seconds"] = round(item["load_seconds"] + item["translate_seconds"], 4)
  
      return {
          "scenario": scenario,

          "dataset": {
              "csv_path": str(csv_path),

              "rows_loaded": len(texts),
          },
          "config": {
              "batch_sizes": batch_sizes,
              "concurrencies": concurrencies,
              "serial_items_per_case": args.serial_items_per_case,
              "concurrency_requests_per_case": args.concurrency_requests_per_case,
              "concurrency_batch_size": args.concurrency_batch_size,
              "max_batch_concurrency_product": max_product,
              "cache_disabled": bool(args.disable_cache),

          },

          "runtime_defaults": {

              "device": str(getattr(backend, "device", capability.get("device", "unknown"))),
              "torch_dtype": str(getattr(backend, "torch_dtype", capability.get("torch_dtype", "unknown"))),

              "configured_batch_size": int(capability.get("batch_size") or 1),

              "load_seconds": round(load_seconds, 4),

          },

          "batch_sweep": batch_sweep,
          "concurrency_sweep": concurrency_sweep,
          "matrix": matrix_results,

      }
  
  
  def run_all_scenarios(args: argparse.Namespace) -> Dict[str, Any]:
      report = {
          "generated_at": datetime.now().isoformat(timespec="seconds"),

          "suite": args.suite,

          "environment": build_environment_info(),
          "scenarios": [],
      }
  
      for scenario in SCENARIOS:
          cmd = [
              sys.executable,
              str(Path(__file__).resolve()),
              "--single",
              "--csv-path",
              args.csv_path,
              "--model",
              scenario["model"],
              "--source-lang",
              scenario["source_lang"],
              "--target-lang",
              scenario["target_lang"],
              "--column",
              scenario["column"],
              "--scene",
              scenario["scene"],
              "--warmup-batches",
              str(args.warmup_batches),

              "--suite",
              args.suite,
              "--serial-items-per-case",
              str(args.serial_items_per_case),
              "--concurrency-requests-per-case",
              str(args.concurrency_requests_per_case),
              "--concurrency-batch-size",
              str(args.concurrency_batch_size),
              "--max-batch-concurrency-product",
              str(args.max_batch_concurrency_product),

          ]
          if args.limit:
              cmd.extend(["--limit", str(args.limit)])
          if args.batch_size:
              cmd.extend(["--batch-size", str(args.batch_size)])

          if args.batch_size_list:
              cmd.extend(["--batch-size-list", args.batch_size_list])
          if args.concurrency_list:
              cmd.extend(["--concurrency-list", args.concurrency_list])

          if args.device_override:
              cmd.extend(["--device-override", args.device_override])
          if args.torch_dtype_override:
              cmd.extend(["--torch-dtype-override", args.torch_dtype_override])

          if args.max_new_tokens:
              cmd.extend(["--max-new-tokens", str(args.max_new_tokens)])
          if args.num_beams:
              cmd.extend(["--num-beams", str(args.num_beams)])
          if args.attn_implementation:
              cmd.extend(["--attn-implementation", args.attn_implementation])

          if args.disable_cache:
              cmd.append("--disable-cache")

  
          completed = subprocess.run(cmd, capture_output=True, text=True, check=True)
          result_line = ""
          for line in reversed(completed.stdout.splitlines()):
              if line.startswith("JSON_RESULT="):
                  result_line = line
                  break
          if not result_line:
              raise RuntimeError(f"Scenario output missing JSON_RESULT marker:\n{completed.stdout}\n{completed.stderr}")
          payload = json.loads(result_line.split("=", 1)[1])
          payload["scenario"]["name"] = scenario["name"]
          report["scenarios"].append(payload)
  
      return report
  
  

  def render_baseline_markdown_report(report: Dict[str, Any]) -> str:

      lines = [
          "# Local Translation Model Benchmark",
          "",
          f"- Generated at: `{report['generated_at']}`",

          f"- Suite: `{report['suite']}`",

          f"- Python: `{report['environment']['python']}`",
          f"- Torch: `{report['environment']['torch']}`",
          f"- Transformers: `{report['environment']['transformers']}`",
          f"- CUDA: `{report['environment']['cuda_available']}`",
      ]
      if report["environment"]["gpu_name"]:
          lines.append(f"- GPU: `{report['environment']['gpu_name']}` ({report['environment']['gpu_total_mem_gb']} GiB)")
      lines.extend(
          [
              "",

              "| Scenario | Items/s | Avg item ms | Req p50 ms | Req p95 ms | Load s | Peak GPU GiB | Success |",

              "|---|---:|---:|---:|---:|---:|---:|---:|",
          ]
      )
      for item in report["scenarios"]:
          runtime = item["runtime"]
          lines.append(

              "| {name} | {items_per_second} | {avg_item_latency_ms} | {request_latency_p50_ms} | {request_latency_p95_ms} | {load_seconds} | {peak_gpu_memory_gb} | {success_rate} |".format(

                  name=item["scenario"]["name"],
                  items_per_second=runtime["items_per_second"],
                  avg_item_latency_ms=runtime["avg_item_latency_ms"],

                  request_latency_p50_ms=runtime["request_latency_p50_ms"],
                  request_latency_p95_ms=runtime["request_latency_p95_ms"],

                  load_seconds=runtime["load_seconds"],
                  peak_gpu_memory_gb=runtime["peak_gpu_memory_gb"],
                  success_rate=runtime["success_rate"],
              )
          )
  
      lines.append("")
      for item in report["scenarios"]:
          runtime = item["runtime"]
          dataset = item["dataset"]
          lines.extend(
              [
                  f"## {item['scenario']['name']}",
                  "",
                  f"- Dataset rows: `{dataset['rows']}` from column `{item['scenario']['column']}`",
                  f"- Direction: `{item['scenario']['source_lang']} -> {item['scenario']['target_lang']}`",
                  f"- Batch size: configured `{runtime['configured_batch_size']}`, used `{runtime['used_batch_size']}`",
                  f"- Load time: `{runtime['load_seconds']} s`",
                  f"- Translate time: `{runtime['translate_seconds']} s`",
                  f"- Throughput: `{runtime['items_per_second']} items/s`, `{runtime['input_chars_per_second']} input chars/s`",

                  f"- Latency: avg item `{runtime['avg_item_latency_ms']} ms`, req p50 `{runtime['request_latency_p50_ms']} ms`, req p95 `{runtime['request_latency_p95_ms']} ms`, req max `{runtime['request_latency_max_ms']} ms`",

                  f"- Memory: max RSS `{runtime['max_rss_mb']} MB`, peak GPU allocated `{runtime['peak_gpu_memory_gb']} GiB`, peak GPU reserved `{runtime['peak_gpu_reserved_gb']} GiB`",
                  f"- Success: `{runtime['success_count']}/{dataset['rows']}`",
                  "",
              ]
          )
      return "\n".join(lines)
  
  

  def render_case_table(
      title: str,
      rows: Sequence[Dict[str, Any]],
      *,
      include_batch: bool,
      include_concurrency: bool,
  ) -> List[str]:
      headers = ["Rows", "Requests", "Items/s", "Req/s", "Avg req ms", "Req p50 ms", "Req p95 ms", "Peak GPU GiB"]
      prefix_headers: List[str] = []
      if include_batch:
          prefix_headers.append("Batch")
      if include_concurrency:
          prefix_headers.append("Concurrency")
      headers = prefix_headers + headers
      lines = [f"### {title}", ""]
      lines.append("| " + " | ".join(headers) + " |")
      lines.append("|" + "|".join(["---:"] * len(headers)) + "|")
      for item in rows:
          values: List[str] = []
          if include_batch:
              values.append(str(item["batch_size"]))
          if include_concurrency:
              values.append(str(item["concurrency"]))
          values.extend(
              [
                  str(item["rows"]),
                  str(item["requests"]),
                  str(item["items_per_second"]),
                  str(item["requests_per_second"]),
                  str(item["avg_request_latency_ms"]),
                  str(item["request_latency_p50_ms"]),
                  str(item["request_latency_p95_ms"]),
                  str(item["peak_gpu_memory_gb"]),
              ]
          )
          lines.append("| " + " | ".join(values) + " |")
      lines.append("")
      return lines
  
  
  def render_extended_markdown_report(report: Dict[str, Any]) -> str:
      lines = [
          "# Local Translation Model Extended Benchmark",
          "",
          f"- Generated at: `{report['generated_at']}`",
          f"- Suite: `{report['suite']}`",
          f"- Python: `{report['environment']['python']}`",
          f"- Torch: `{report['environment']['torch']}`",
          f"- Transformers: `{report['environment']['transformers']}`",
          f"- CUDA: `{report['environment']['cuda_available']}`",
      ]
      if report["environment"]["gpu_name"]:
          lines.append(f"- GPU: `{report['environment']['gpu_name']}` ({report['environment']['gpu_total_mem_gb']} GiB)")
  
      lines.extend(
          [
              "",
              "## Reading Guide",
              "",
              "- `batch_sweep`: single stream only (`concurrency=1`), used to compare bulk translation efficiency across batch sizes.",
              "- `concurrency_sweep`: fixed request batch size, used to compare online request latency and throughput as concurrency rises.",
              "- `matrix`: combined `batch_size x concurrency` runs, filtered by `batch_size * concurrency <= limit` when configured.",
              "",
          ]
      )
  
      for item in report["scenarios"]:
          lines.extend(
              [
                  f"## {item['scenario']['name']}",
                  "",
                  f"- Direction: `{item['scenario']['source_lang']} -> {item['scenario']['target_lang']}`",
                  f"- Column: `{item['scenario']['column']}`",
                  f"- Loaded rows: `{item['dataset']['rows_loaded']}`",
                  f"- Load time: `{item['runtime_defaults']['load_seconds']} s`",
                  f"- Device: `{item['runtime_defaults']['device']}`",
                  f"- DType: `{item['runtime_defaults']['torch_dtype']}`",
                  f"- Cache disabled: `{item['config']['cache_disabled']}`",
                  "",
              ]
          )
          lines.extend(render_case_table("Batch Sweep (`concurrency=1`)", item["batch_sweep"], include_batch=True, include_concurrency=False))
          lines.extend(
              render_case_table(
                  f"Concurrency Sweep (`batch_size={item['config']['concurrency_batch_size']}`)",
                  item["concurrency_sweep"],
                  include_batch=False,
                  include_concurrency=True,
              )
          )
          lines.extend(render_case_table("Batch x Concurrency Matrix", item["matrix"], include_batch=True, include_concurrency=True))
      return "\n".join(lines)
  
  
  def render_markdown_report(report: Dict[str, Any]) -> str:
      if report["suite"] == "extended":
          return render_extended_markdown_report(report)
      return render_baseline_markdown_report(report)
  
  

  def main() -> None:
      args = parse_args()
      if args.single:

          if args.suite == "extended":
              result = benchmark_extended_scenario(args)
          else:
              result = benchmark_single_scenario(args)

          print("JSON_RESULT=" + json.dumps(result, ensure_ascii=False))
          return
  
      report = run_all_scenarios(args)
      output_dir = resolve_output_dir(args.output_dir)
      timestamp = datetime.now().strftime("%H%M%S")

      suffix = "extended" if args.suite == "extended" else "baseline"
      json_path = output_dir / f"translation_local_models_{suffix}_{timestamp}.json"
      md_path = output_dir / f"translation_local_models_{suffix}_{timestamp}.md"

      json_path.write_text(json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8")
      md_path.write_text(render_markdown_report(report), encoding="utf-8")
  
      print(f"JSON report: {json_path}")
      print(f"Markdown report: {md_path}")
      for item in report["scenarios"]:

          if args.suite == "extended":
              best_batch = max(item["batch_sweep"], key=lambda x: x["items_per_second"])
              best_concurrency = max(item["concurrency_sweep"], key=lambda x: x["items_per_second"])
              print(
                  f"{item['scenario']['name']}: "
                  f"best_batch={best_batch['batch_size']} ({best_batch['items_per_second']} items/s) | "
                  f"best_concurrency={best_concurrency['concurrency']} ({best_concurrency['items_per_second']} items/s @ batch={best_concurrency['batch_size']})"
              )
          else:
              runtime = item["runtime"]
              print(
                  f"{item['scenario']['name']}: "
                  f"{runtime['items_per_second']} items/s | "
                  f"avg_item={runtime['avg_item_latency_ms']} ms | "
                  f"p95_req={runtime['request_latency_p95_ms']} ms | "
                  f"load={runtime['load_seconds']} s"
              )

  
  
  if __name__ == "__main__":
      main()