"""Typed savings ledger (Fase 1, F1): realized cost savings — deterministic, fail-fast.

MAF-repo-local: imports ``Verdict`` / ``ProposalFeatures`` from ``verdicts`` (which is MAF-bound),
so it CANNOT live in the framework-neutral ``shared/`` subtree (contrast ``dimension.py``, which is
neutral). All amounts are integer *øre* (1 NOK = 100 øre): integer sums are exact and
order-independent, so the goal boundary (Step 8) is deterministic and the on-disk form is
byte-stable — a float NOK amount would make both non-deterministic (float associativity).

The ledger is the accumulated record of realized savings a portfolio run stops against (Step 8).
Accumulation dedups on the DIMENSION-FREE key ``(project_id, candidate_identity)`` (C1), so the same
underlying saving counted under two dimensions contributes ONCE. Step 5 formalizes the two-part key
(the full ``(project_id, dimension, candidate_identity)`` is kept only for storage + cross-dimension
overlap flagging) and the ``_candidate_identity`` helper.
"""

from __future__ import annotations

import hashlib
import json
from decimal import ROUND_HALF_UP, Decimal
from pathlib import Path

from pydantic import BaseModel, Field

from portfolio_optimiser.verdicts import _APPROVED_DECISIONS, ProposalFeatures, Verdict


class RealizationRefused(RuntimeError):
    """Fail-closed gate (SC5, mirrors ``verdicts.PromotionRefused``): a non-approved verdict was
    offered for realization. Nothing is written — only human/persona-approved savings enter the
    ledger, never raw agent output (self-contamination)."""


class LedgerEntry(BaseModel):
    """One realized saving, linked to the approving verdict."""

    project_id: str
    dimension: str
    candidate_identity: str
    amount_ore: int = Field(ge=0)  # integer øre — exact, order-independent sums
    verdict_id: str  # link to the approving verdict
    provenance: str  # lightweight who/experiment/when string


def to_ore(nok: float) -> int:
    """The framework's ONE NOK -> integer-øre conversion (Kø-(p)).

    Via ``Decimal`` to avoid binary-float error: ``12345.67`` NOK -> ``1234567`` øre exactly
    (a raw ``float * 100`` would drift to ``...66.9999``). Half øre round HALF UP.

    **Apply this PER money amount, then sum the integers — never sum floats and convert the
    total.** The two orders disagree (three ``60000.005`` NOK lines are ``18000003`` øre
    quantized first, ``18000001`` summed first), and each cost line is itself a real amount, so
    the per-line value is the one that exists. Integer addition is also associative, which keeps
    every total order-independent under the D-D wave model. ``run.py``'s goal baselines import
    THIS function rather than re-implementing it: two copies of a money conversion drift, and a
    drifted copy would put the two sides of a goal comparison on different scales."""
    return int((Decimal(str(nok)) * 100).quantize(Decimal("1"), rounding=ROUND_HALF_UP))


def stamp(*, approver: str, experiment: str, timestamp: str) -> str:
    """A lightweight who/experiment/when provenance string for a ledger entry. ``timestamp`` is a
    required keyword — no wall-clock default — so a stamped entry is deterministic and its provenance
    reproducible (mirrors ``promote_verdict``). Deliberately NOT ``ProvenanceStamp``, which is
    MAF-bound and requires ``citations >= 1`` — a ledger entry has no text span to cite."""
    return f"godkjent av {approver}; eksperiment {experiment}; {timestamp}"


def _candidate_identity(
    *, affected_codes: frozenset[str], measure_type: str, amount_ore: int
) -> str:
    """Stable content-hash identity for a realized candidate — a canonical-JSON sha256 over
    ``sorted(affected_codes) + measure_type + amount_ore`` (mirrors ``verdicts._mint_id``'s canonical
    form). Integer *øre* is IN the identity, so it is (a) deterministic — no ``30000`` vs ``30000.0``
    divergence after a JSON round-trip — AND (b) collision-free: two genuinely distinct realizations
    with the same codes+measure but a different amount stay SEPARATE (a magnitude-free identity would
    under-report by merging them). Named module-level so Step-5 tests and Step-6 ``realize`` construct
    entries with the same identity."""
    canonical = json.dumps(
        {
            "affected_codes": sorted(affected_codes),
            "measure_type": measure_type,
            "amount_ore": amount_ore,
        },
        sort_keys=True,
        separators=(",", ":"),
    )
    return hashlib.sha256(canonical.encode("utf-8")).hexdigest()[:16]


class SavingsLedger(BaseModel):
    """A typed store of realized ``LedgerEntry`` records — the two-part key (C1).

    ``add_realized`` stores on the FULL key ``(project_id, dimension, candidate_identity)`` (so the
    same candidate under two dimensions is kept for overlap flagging); the totals dedup on the
    dimension-free key ``(project_id, candidate_identity)`` so that underlying saving is counted
    ONCE. Totals sum integer øre and are therefore order-independent; the sorted iteration in
    ``save`` is only for byte-deterministic serialization, not for sum correctness.
    """

    entries: list[LedgerEntry] = Field(default_factory=list)

    @staticmethod
    def _sum_key(entry: LedgerEntry) -> tuple[str, str]:
        """The dimension-FREE key: sum/dedup on this so the same candidate realized under two
        dimensions is counted ONCE (C1). ``candidate_identity`` embeds the amount, so two entries
        sharing this key share the amount — summing one representative is well-defined."""
        return (entry.project_id, entry.candidate_identity)

    @staticmethod
    def _storage_key(entry: LedgerEntry) -> tuple[str, str, str]:
        """The FULL key: storage + overlap-flagging ONLY, never the sum. The same candidate under a
        different dimension is a distinct full key, so both are stored (the overlap can be flagged)."""
        return (entry.project_id, entry.dimension, entry.candidate_identity)

    def add_realized(self, entry: LedgerEntry) -> bool:
        """Store ``entry`` unless its FULL key is already present. The same candidate under a
        DIFFERENT dimension is a distinct full key -> both are stored (so ``overlaps`` can flag it),
        while the totals still count the underlying saving ONCE (dimension-free key). Returns ``True``
        if stored, ``False`` if it was an exact (full-key) duplicate."""
        seen = {self._storage_key(e) for e in self.entries}
        if self._storage_key(entry) in seen:
            return False
        self.entries.append(entry)
        return True

    def _dedup_amount(self, entries: list[LedgerEntry]) -> int:
        """Sum ``amount_ore`` over UNIQUE dimension-free keys — each underlying candidate counted
        once, so a cross-dimension overlap is never double-summed (SC4)."""
        seen: set[tuple[str, str]] = set()
        total = 0
        for e in entries:
            key = self._sum_key(e)
            if key in seen:
                continue
            seen.add(key)
            total += e.amount_ore
        return total

    def per_project_total(self, project_id: str) -> int:
        """Total realized øre for one project — dimension-free-deduped, order-independent."""
        return self._dedup_amount([e for e in self.entries if e.project_id == project_id])

    def portfolio_total(self) -> int:
        """Total realized øre across the portfolio — dimension-free-deduped, order-independent."""
        return self._dedup_amount(self.entries)

    def overlaps(self) -> list[tuple[str, str]]:
        """The dimension-free keys registered under MORE THAN ONE dimension — flagged, never
        double-summed. Returns the sorted ``(project_id, candidate_identity)`` keys whose stored
        entries span >1 dimension."""
        dims: dict[tuple[str, str], set[str]] = {}
        for e in self.entries:
            dims.setdefault(self._sum_key(e), set()).add(e.dimension)
        return sorted(key for key, ds in dims.items() if len(ds) > 1)

    def save(self, path: str) -> None:
        """Serialize deterministically: entries sorted by their full key, then JSON with
        ``sort_keys=True, indent=2`` (mirrors ``verdicts.py``'s deterministic on-disk form). Same
        entries, any insertion order -> byte-identical output."""
        ordered = sorted(
            self.entries,
            key=lambda e: (e.project_id, e.candidate_identity, e.dimension),
        )
        payload = [e.model_dump() for e in ordered]
        Path(path).write_text(json.dumps(payload, sort_keys=True, indent=2), encoding="utf-8")

    @classmethod
    def load(cls, path: str) -> SavingsLedger:
        """Fail-fast load (mirrors ``okf.load_ir_projection``'s required-input semantics): a missing
        file raises ``FileNotFoundError``; a non-array top-level or non-object row raises
        ``ValueError`` (a valid-JSON ``{}`` must NOT masquerade as an empty zero-savings ledger, and
        a bare scalar / non-object row must NOT leak an uncaught ``TypeError``); a malformed object
        row raises ``pydantic.ValidationError``. Contrast the tolerant verdict inbox
        (``load_verdicts_from_dir``), which SKIPS bad files — the ledger is authoritative input, not
        an out-of-band drop folder."""
        p = Path(path)
        if not p.is_file():
            raise FileNotFoundError(f"savings ledger not found: {path!r}")
        rows = json.loads(p.read_text(encoding="utf-8"))
        if not isinstance(rows, list):
            raise ValueError(
                f"savings ledger must be a JSON array of entries, got {type(rows).__name__}: {path!r}"
            )
        entries = []
        for row in rows:
            if not isinstance(row, dict):
                raise ValueError(
                    f"savings ledger entry must be a JSON object, got {type(row).__name__}: {path!r}"
                )
            entries.append(LedgerEntry(**row))
        return cls(entries=entries)


def realize(
    ledger: SavingsLedger,
    features: ProposalFeatures,
    verdict: Verdict,
    *,
    project_id: str,
    dimension: str,
    approver: str,
    experiment: str,
    timestamp: str,
) -> LedgerEntry:
    """Realize an APPROVED candidate into ``ledger`` and return the entry (SC5).

    FAIL-CLOSED: a verdict whose ``decision`` is not an approval raises ``RealizationRefused`` and
    writes NOTHING — only human/persona-approved savings enter the ledger (mirrors
    ``promote_verdict``). The approval set is the PROMOTION set ``{approved,
    approved_with_adjustment}``, NOT the run-path binary ``FeedbackContract`` (H6).

    Deliberately NOT wired into ``run_project`` (role split C3): the system READS context; the
    expert/persona realizes out of band — mirroring how ``promote_verdict`` is never called in the
    run path (self-contamination guard).

    ``project_id`` and ``dimension`` are required keywords: a ``LedgerEntry`` is scoped to a project
    and a dimension, and neither ``features`` nor ``verdict`` carries them.

    NOK->øre conversion goes through ``to_ore``, the framework's ONE conversion (Kø-(p)).
    ``timestamp`` is a required keyword (no wall-clock default), so the entry is deterministic."""
    if verdict.decision not in _APPROVED_DECISIONS:
        raise RealizationRefused(
            f"refusing to realize a non-approved verdict (decision={verdict.decision!r}); "
            "only human/persona-approved savings enter the ledger (SC5)"
        )
    amount_ore = to_ore(features.claimed_saving_nok)
    entry = LedgerEntry(
        project_id=project_id,
        dimension=dimension,
        candidate_identity=_candidate_identity(
            affected_codes=features.affected_codes,
            measure_type=features.measure_type,
            amount_ore=amount_ore,
        ),
        amount_ore=amount_ore,
        verdict_id=verdict.id,
        provenance=stamp(approver=approver, experiment=experiment, timestamp=timestamp),
    )
    ledger.add_realized(entry)
    return entry