"""Deterministic, blocking hybrid validator (B1) — the obligatory, non-optional gate.

Pure module: **NO** ``agent_framework`` import (D7-portable core). Three stages over the
typed IR (``ir.py``):

1. **Pydantic IR** invariants already ran at construction (``ir.SavingsProposal``).
2. **PuLP solver-in-the-loop** — a real CBC solve bounds the maximum feasible saving (R2).
   CBC ships in PuLP's wheel; if it is genuinely absent the step **escalates**
   (``CbcUnavailable``) — no silent LP-relaxation fallback.
3. **Monte Carlo** — stdlib ``random`` (seeded ``_MC_SEED``) + ``statistics.quantiles`` over
   uncertain unit-costs give P10/P50/P90 of the feasible saving.

The structural block (stage 4) returns a ``Rejection`` that is a *different type* from
``ValidatedProposal`` and carries no percentiles, so it can never be consumed as validated.

Promoted verbatim from ``spikes/c_validator.py``. The one deliberate change vs the spike:
``self_repair`` no longer borrows the harness ``Budget`` (that pulls ``agent_framework`` via
``spikes/_harness``) — it loops directly on ``max_attempts``; the token-budget bound is
layered on by the Step 10 generate loop, keeping THIS module pure.
"""

from __future__ import annotations

import random
import statistics
import warnings
from collections.abc import Callable
from contextlib import contextmanager
from dataclasses import dataclass

import pulp

from portfolio_optimiser.ir import AffectedItem, SavingsProposal
from portfolio_optimiser.reference_domain import Project

MAX_SAVING_FRACTION = 0.30
"""Policy cap: at most 30% of an affected item's cost is realistically recoverable as a
saving. The LP bounds the feasible saving by this fraction."""

_MC_SAMPLES = 512
_MC_SEED = 20260624


class CbcUnavailable(RuntimeError):
    """PuLP's bundled CBC solver is not available — escalate (no silent fallback)."""


@contextmanager
def _quiet_pulp():
    """Silence PuLP 3.x's ``PULP_CBC_CMD`` DeprecationWarning. The bundled CBC is only
    reachable via ``PULP_CBC_CMD``; PuLP 4.0 will require ``pip install pulp[cbc]`` +
    COIN_CMD (a migration note). The warning is cosmetic here."""
    with warnings.catch_warnings():
        warnings.simplefilter("ignore", DeprecationWarning)
        yield


@dataclass(frozen=True)
class ValidatedProposal:
    """A proposal that passed every stage. Carries the Monte Carlo percentiles."""

    proposal: SavingsProposal
    p10: float
    p50: float
    p90: float
    nominal_feasible: float


@dataclass(frozen=True)
class Rejection:
    """A structurally-blocked proposal. Distinct type, no percentiles — it can never be
    consumed as a ``ValidatedProposal``."""

    proposal: SavingsProposal
    reason: str


def _solve_max_feasible(items: list[AffectedItem], fraction: float) -> float:
    """Real CBC solve: maximize total saving subject to a per-item upper bound and a
    global fraction cap. Raises ``CbcUnavailable`` if CBC is genuinely missing."""
    with _quiet_pulp():
        solver = pulp.PULP_CBC_CMD(msg=False)
        if not solver.available():
            raise CbcUnavailable("PuLP's bundled CBC solver is not available on this platform")
        prob = pulp.LpProblem("max_feasible_saving", pulp.LpMaximize)
        xs = [pulp.LpVariable(f"x_{i}", lowBound=0, upBound=it.total) for i, it in enumerate(items)]
        prob += pulp.lpSum(xs)
        prob += pulp.lpSum(xs) <= fraction * sum(it.total for it in items)
        status = prob.solve(solver)
    if pulp.LpStatus[status] != "Optimal":
        raise CbcUnavailable(
            f"CBC did not reach an optimal solution (status={pulp.LpStatus[status]})"
        )
    return float(pulp.value(prob.objective))


def _monte_carlo(
    proposal: SavingsProposal, *, fraction: float = MAX_SAVING_FRACTION
) -> tuple[float, float, float]:
    """Vary uncertain unit-costs (seeded) and return (P10, P50, P90) of the feasible
    saving. Uses the LP's closed-form optimum (= fraction x sum of sampled totals), which
    is exact here, so we do NOT spawn a CBC subprocess per sample (D6)."""
    rng = random.Random(_MC_SEED)
    feasibles: list[float] = []
    for _ in range(_MC_SAMPLES):
        total = 0.0
        for item in proposal.affected_items:
            rng_range = proposal.assumptions.get(item.code)
            unit_cost = rng.uniform(*rng_range) if rng_range else item.unit_cost
            total += item.quantity * unit_cost
        feasibles.append(fraction * total)
    deciles = statistics.quantiles(feasibles, n=10, method="inclusive")
    return deciles[0], deciles[4], deciles[8]  # P10, P50, P90


def validate_proposal(proposal: SavingsProposal) -> ValidatedProposal | Rejection:
    """Deterministic blocking validation. Returns a ``ValidatedProposal`` only when the
    claim is feasible; otherwise a ``Rejection`` that cannot be consumed as validated."""
    # Stage 1 (Pydantic) already ran at construction. Stage 2: real CBC solve.
    nominal = _solve_max_feasible(proposal.affected_items, MAX_SAVING_FRACTION)
    # Stage 3: Monte Carlo percentiles of the feasible saving.
    p10, p50, p90 = _monte_carlo(proposal)
    # Stage 4: structural block — a claim above the optimistic feasible (P90) is out of range.
    if proposal.claimed_saving_nok > p90:
        return Rejection(
            proposal=proposal,
            reason=f"claimed saving {proposal.claimed_saving_nok:.0f} exceeds P90 feasible {p90:.0f}",
        )
    return ValidatedProposal(proposal=proposal, p10=p10, p50=p50, p90=p90, nominal_feasible=nominal)


def self_repair(
    generate: Callable[[int], SavingsProposal],
    *,
    max_attempts: int = 3,
) -> ValidatedProposal | Rejection:
    """Call ``generate(attempt)`` and validate; retry on rejection up to ``max_attempts``,
    then hard-stop and return the last rejection. Attempts-bounded — never loops forever
    (B4). The token-budget bound is layered on by the Step 10 generate loop, not here (this
    module stays pure: no ``agent_framework`` import)."""
    if max_attempts <= 0:
        raise ValueError(f"max_attempts must be positive, got {max_attempts}")
    last: Rejection | None = None
    for attempt in range(1, max_attempts + 1):
        result = validate_proposal(generate(attempt))
        if isinstance(result, ValidatedProposal):
            return result
        last = result
    assert last is not None
    return last


def proposal_for(
    project: Project,
    codes: list[str],
    *,
    claimed_saving_nok: float,
    measure: str = "Reduce scope on selected cost codes",
    assumptions: dict[str, tuple[float, float]] | None = None,
) -> SavingsProposal:
    """Build a ``SavingsProposal`` from a real reference project's cost items (helper)."""
    items = [
        AffectedItem(code=ci.code, quantity=ci.quantity, unit_cost=ci.unit_cost)
        for ci in project.cost_items
        if ci.code in codes
    ]
    return SavingsProposal(
        project_id=project.id,
        measure=measure,
        affected_items=items,
        claimed_saving_nok=claimed_saving_nok,
        assumptions=assumptions or {},
    )