"""GPU spec database loader: normalize_gpu_name() maps raw MLPerf names to canonical ids; enrich_df() joins spec columns onto a parsed MLPerf DataFrame."""

from __future__ import annotations

import functools
import logging
import re
import stat as _stat
import types
from pathlib import Path
from typing import Optional

import pandas as pd
import yaml

# Real spec files are ~10 KB; 1 MB cap blocks oversized files (mirrors mlperf_parser.py's _safe_read_text).
_MAX_SPEC_BYTES: int = 1 * 1024 * 1024  # 1 MB

log = logging.getLogger(__name__)

_DEFAULT_SPEC_PATH = Path(__file__).parent.parent.parent / "data" / "gpu_specs.yaml"

# Columns added by enrich_df, in output order.
SPEC_COLUMNS = [
    "canonical_gpu_id",
    "gpu_vendor",
    "gpu_architecture",
    "gpu_memory_type",
    "gpu_vram_gb",
    "gpu_hbm_bandwidth_tbps",
    "gpu_peak_fp32_tflops",
    "gpu_peak_bf16_tflops",
    "gpu_peak_fp16_tflops",
    "gpu_peak_fp8_tflops",
    "gpu_peak_fp6_tflops",
    "gpu_peak_fp4_tflops",
    "gpu_peak_int8_tops",
    "gpu_cu_sm_count",
    "gpu_l2_cache_mb",
    "gpu_tdp_w",
    "gpu_in_model_scope",
    "gpu_spec_confidence",
]

# Matches "(x8)", "(x16)", "(x32)", "(x87)" etc.
_COUNT_SUFFIX_RE = re.compile(r"\s*\(x\d+\)\s*$")
# Matches "(Power Cap 1000 W)" and similar parenthetical power notes
_POWER_SUFFIX_RE = re.compile(r"\s*\(Power Cap[^)]*\)\s*$", re.IGNORECASE)


@functools.lru_cache(maxsize=4)
def _build_spec_cache(spec_path_str: str) -> tuple[list, dict, dict]:
    """Build (specs, alias_index, id_map) once per unique path string; cached objects must not be mutated by callers."""
    specs  = load_specs(Path(spec_path_str))
    index  = _build_alias_index(specs)
    # MappingProxyType wraps id_map values so callers can't mutate the cached spec rows.
    id_map = {s["id"]: types.MappingProxyType(_spec_row(s)) for s in specs}
    return specs, index, id_map


@functools.lru_cache(maxsize=8)
def _load_raw(spec_path: Path | str = _DEFAULT_SPEC_PATH) -> dict:
    """Guarded, cached parse of the full gpu_specs.yaml dict (not just the 'gpus' list) — the one place this file is actually opened and yaml.safe_load'd. load_specs() and spec_db_version() both build on this so the same ~10 KB file isn't parsed twice for two different facts about it."""
    path = Path(spec_path)
    try:
        st = path.lstat()
    except OSError as exc:
        raise FileNotFoundError(f"GPU spec DB not found: {path}") from exc
    if _stat.S_ISLNK(st.st_mode):
        raise ValueError(f"GPU spec DB path is a symlink (refused): {path}")
    if st.st_size > _MAX_SPEC_BYTES:
        raise ValueError(
            f"GPU spec DB too large ({st.st_size} bytes > {_MAX_SPEC_BYTES}): {path}"
        )
    with path.open(encoding="utf-8") as f:
        data = yaml.safe_load(f)
    if not isinstance(data, dict) or "gpus" not in data:
        raise ValueError(
            f"GPU spec DB at {path} is missing required 'gpus' key "
            f"(got {type(data).__name__})"
        )
    return data


@functools.lru_cache(maxsize=8)
def load_specs(spec_path: Path | str = _DEFAULT_SPEC_PATH) -> list[dict]:
    """Load the GPU spec dict list from YAML; raises ValueError for symlinks, oversized files, or a missing 'gpus' key."""
    return _load_raw(spec_path)["gpus"]


@functools.lru_cache(maxsize=8)
def spec_db_version(spec_path: Path | str = _DEFAULT_SPEC_PATH) -> str:
    """The gpu_specs.yaml schema_version string (meta.gpu_spec_db_version)."""
    return str(_load_raw(spec_path).get("schema_version", "unknown"))


def _build_alias_index(specs: list[dict]) -> dict[str, dict]:
    """Return lowercase-alias → spec dict for fast lookup."""
    index: dict[str, dict] = {}
    for spec in specs:
        for alias in spec.get("aliases", []):
            key = alias.lower()
            if key in index and index[key]["id"] != spec["id"]:
                # Only warn when the alias maps to two *different* GPU ids (same-GPU dupes are harmless).
                log.warning(
                    "Conflicting alias %r maps to both %r and %r in GPU spec DB",
                    alias, index[key]["id"], spec["id"],
                )
            index[key] = spec
    return index


def _is_heterogeneous(raw_name: str) -> bool:
    """Return True for multi-GPU strings mixing different SKUs."""
    # "A and B" pattern (heterogeneous mix)
    if " and " in raw_name.lower():
        return True
    # "A (xN), B (xM)" pattern
    if "," in raw_name and re.search(r"\(x\d+\)", raw_name):
        return True
    return False


def _strip_suffixes(raw_name: str) -> str:
    """Remove parenthetical count and power suffixes from a GPU name."""
    name = _COUNT_SUFFIX_RE.sub("", raw_name)
    name = _POWER_SUFFIX_RE.sub("", name)
    return name.strip()


def normalize_gpu_name(
    raw_name: str | None,
    specs: list[dict],
    *,
    _index: dict[str, dict] | None = None,
) -> Optional[str]:
    """Map a raw MLPerf accelerator_model_name to a canonical GPU id, or None for null/N/A, heterogeneous multi-GPU, or unmatched names."""
    if not raw_name or raw_name.strip().upper() in {"N/A", "NA", "NONE", ""}:
        return None

    if _is_heterogeneous(raw_name):
        log.debug("Skipping heterogeneous GPU string: %r", raw_name)
        return None

    index = _index if _index is not None else _build_alias_index(specs)
    clean = _strip_suffixes(raw_name)

    match = index.get(clean.lower())
    if match is None:
        log.debug("No GPU spec match for: %r (stripped: %r)", raw_name, clean)
        return None

    return match["id"]


def _spec_row(spec: dict) -> dict:
    """Flatten one GPU spec dict into the SPEC_COLUMNS schema."""
    pt = spec.get("peak_tflops") or {}
    return {
        "canonical_gpu_id":        spec["id"],
        "gpu_vendor":              spec.get("vendor"),
        "gpu_architecture":        spec.get("architecture"),
        "gpu_memory_type":         spec.get("memory_type"),
        "gpu_vram_gb":             spec.get("vram_gb"),
        "gpu_hbm_bandwidth_tbps":  spec.get("hbm_bandwidth_tbps"),
        "gpu_peak_fp32_tflops":    pt.get("fp32"),
        "gpu_peak_bf16_tflops":    pt.get("bf16"),
        "gpu_peak_fp16_tflops":    pt.get("fp16"),
        "gpu_peak_fp8_tflops":     pt.get("fp8"),
        "gpu_peak_fp6_tflops":     pt.get("fp6"),
        "gpu_peak_fp4_tflops":     pt.get("fp4"),
        "gpu_peak_int8_tops": pt.get("int8"),
        # Unified key for AMD compute_units / NVIDIA streaming_multiprocessors, so features stay vendor-agnostic.
        "gpu_cu_sm_count": (
            spec.get("compute_units") or spec.get("streaming_multiprocessors")
        ),
        "gpu_l2_cache_mb": spec.get("l2_cache_mb"),
        "gpu_tdp_w": spec.get("tdp_w"),
        "gpu_in_model_scope": spec.get("in_model_scope"),
        "gpu_spec_confidence": spec.get("spec_confidence"),
    }


def enrich_df(
    df: pd.DataFrame,
    spec_path: Path | str = _DEFAULT_SPEC_PATH,
) -> pd.DataFrame:
    """Join GPU hardware spec columns onto a parsed MLPerf DataFrame by canonical_gpu_id; unmatched rows get NaN. Returns a new DataFrame."""
    specs, index, id_map = _build_spec_cache(str(Path(spec_path)))

    # Normalize once per unique gpu_name (not per row) — ~33 unique names across ~1223 rows.
    unique_names = {n for n in df["gpu_name"].dropna().unique()}
    name_to_id = {
        name: normalize_gpu_name(name, specs, _index=index)
        for name in unique_names
    }
    canonical_ids = df["gpu_name"].map(name_to_id)

    spec_df = pd.DataFrame(
        [id_map.get(cid, {}) for cid in canonical_ids],
        index=df.index,
    )

    # Ensure all expected columns exist even if spec_rows were all empty.
    for col in SPEC_COLUMNS:
        if col not in spec_df.columns:
            spec_df[col] = None

    matched = canonical_ids.notna().sum()
    unmatched = canonical_ids.isna().sum()
    log.info(
        "GPU spec enrichment: %d rows matched, %d unmatched (%.0f%%)",
        matched, unmatched, 100 * matched / max(len(df), 1),
    )
    if unmatched:
        unseen = set(df.loc[canonical_ids.isna(), "gpu_name"].dropna().unique())
        # %r escapes control chars in GPU names, guarding against log injection.
        for name in sorted(unseen):
            log.debug("Unmatched gpu_name: %r", name)

    result = pd.concat([df, spec_df[SPEC_COLUMNS]], axis=1)

    # Some submitters report total system VRAM (parser's vram_gb) instead of per-GPU capacity (spec DB's gpu_vram_gb); use gpu_vram_gb for the memory-fit constraint.
    if "vram_gb" in result.columns and "gpu_vram_gb" in result.columns:
        conflict = (
            result["vram_gb"].notna()
            & result["gpu_vram_gb"].notna()
            & (result["vram_gb"] != result["gpu_vram_gb"])
        )
        if conflict.any():
            n = conflict.sum()
            examples = (
                result.loc[conflict, ["gpu_name", "num_gpus", "vram_gb", "gpu_vram_gb"]]
                .drop_duplicates()
            )
            log.warning(
                "%d rows: vram_gb (parser) != gpu_vram_gb (spec DB). "
                "Some submitters report total system VRAM. "
                "Use gpu_vram_gb for the memory-fit constraint.",
                n,
            )
            # itertuples ~10x faster than iterrows; %r quotes gpu_name to prevent log injection.
            for row in examples.itertuples(index=False):
                log.warning(
                    "  vram conflict: gpu=%r  num_gpus=%s  reported=%s  spec=%s",
                    row.gpu_name, row.num_gpus, row.vram_gb, row.gpu_vram_gb,
                )

    return result