"""csv 输入输出层.

case 输入 csv 列 (表头, 大小写不敏感, 缺省列取默认值):
    case_id, batch_a, batch_b, m, n, k,
    dtype_a, dtype_b, dtype_c, trans_a, trans_b, has_bias, out_nd, deterministic_level

  建模边界说明: trans_a/trans_b (转置对 dValue/排布的影响) 与 has_bias (bias 读写增量)
  本期不建模, 仅透传记录; out_nd=False 会禁用 StreamK (进入条件 4).

方案输入 csv (评估模式): ImplPlan 全部字段, 见 ImplPlan.csv_fields().

输出 csv:
  - 方案推荐模式: case 列 + plan_* 列 + 时延评估列 + feasible/bound_type/advice
  - 方案评估模式: 同上, plan 来自用户输入, feasible/violations 反映约束校验
"""

from __future__ import annotations

import csv
import sys
from pathlib import Path

from .models import BmmCase, ImplPlan, EvalResult


_TRUE = {"1", "true", "yes", "y", "是"}


def _to_bool(v, default=False):
    if v is None or str(v).strip() == "":
        return default
    return str(v).strip().lower() in _TRUE


def _to_int(v, default=0):
    if v is None or str(v).strip() == "":
        return default
    return int(float(v))


def _to_str(v, default=""):
    if v is None:
        return default
    v = str(v).strip()
    return v if v else default


def load_cases(path: str | Path) -> list:
    """读取 case csv -> list[BmmCase]."""
    cases = []
    with open(path, newline="", encoding="utf-8-sig") as f:
        for i, row in enumerate(csv.DictReader(f)):
            r = {k.strip().lower(): v for k, v in row.items() if k}
            try:
                cases.append(BmmCase(
                    case_id=_to_str(r.get("case_id"), f"case_{i}"),
                    batch_a=_to_int(r.get("batch_a"), 1),
                    batch_b=_to_int(r.get("batch_b"), 1),
                    m=_to_int(r.get("m"), 1),
                    n=_to_int(r.get("n"), 1),
                    k=_to_int(r.get("k"), 1),
                    dtype_a=_to_str(r.get("dtype_a"), "bf16"),
                    dtype_b=_to_str(r.get("dtype_b"), "bf16"),
                    dtype_c=_to_str(r.get("dtype_c"), "bf16"),
                    trans_a=_to_bool(r.get("trans_a")),
                    trans_b=_to_bool(r.get("trans_b")),
                    has_bias=_to_bool(r.get("has_bias")),
                    out_nd=_to_bool(r.get("out_nd"), True),
                    deterministic_level=_to_int(r.get("deterministic_level"), 0),
                ))
            except ValueError as e:
                raise ValueError(f"{path} 第{i+2}行解析失败: {e}") from e
    # issue#8: A/B dtype 不一致告警 (注释承诺的 warning 落地)
    for c in cases:
        if c.dtype_a.strip().lower() != c.dtype_b.strip().lower():
            print(f"[warn] case {c.case_id}: A/B dtype 不一致 "
                  f"({c.dtype_a} vs {c.dtype_b}), 数据量按较大者建模, "
                  f"混精度对 dValue/带宽的影响未精确建模", file=sys.stderr)
    return cases


def load_plans(path: str | Path) -> list:
    """读取方案 csv -> list[ImplPlan] (按 case_id 与 case 关联)."""
    plans = []
    with open(path, newline="", encoding="utf-8-sig") as f:
        for row in csv.DictReader(f):
            r = {k.strip(): v for k, v in row.items() if k}
            plans.append(ImplPlan.from_row(r))
    return plans


def save_results(path: str | Path, results: list) -> None:
    """list[EvalResult] -> csv."""
    if not results:
        return
    rows = [r.to_row() for r in results]
    fields = list(rows[0].keys())
    with open(path, "w", newline="", encoding="utf-8-sig") as f:
        w = csv.DictWriter(f, fieldnames=fields)
        w.writeheader()
        w.writerows(rows)


def save_plans(path: str | Path, plans: list) -> None:
    """list[ImplPlan] -> csv (标准结构体列)."""
    if not plans:
        return
    with open(path, "w", newline="", encoding="utf-8-sig") as f:
        w = csv.DictWriter(f, fieldnames=ImplPlan.csv_fields())
        w.writeheader()
        for p in plans:
            w.writerow(p.to_row())