import fcntl
import os
import tempfile
from datetime import UTC, datetime
from pathlib import Path
from typing import Literal

from pydantic import BaseModel

DECISIONS_FILE = "review_decisions.jsonl"
DECISIONS_LOCK_FILE = ".review_decisions.lock"

# 22 tipi di the reference implementation (verified leggendo session/decisions.py) + 1 nuovo (D15):
# `decision_retracted` per il rollback a granularità step (ritira una decisione
# senza cancellarne la riga dal log di audit; effective_decisions la onora).
DecisionType = Literal[
    "concept_clarified",
    "question_rewritten",
    "table_promoted",
    "table_excluded",
    "column_promoted",
    "column_excluded",
    "column_corrected",
    "join_modified",
    "evidence_accepted",
    "evidence_rejected",
    "ambiguity_open",
    "memory_rejected",
    "cte_approved",
    "cte_corrected",
    "cte_rejected",
    "sql_revised",
    "sql_approved",
    "sql_rejected",
    "phase_approved",
    "phase_auto_approved",
    "phase_reopened",
    "phase_skipped",
    "datamart_requested",
    "datamart_declined",
    # F8: promozione memorie riusabili al gate reviewer_memory_promote. subject =
    # subject della decisione originale, detail = "seq:<decision_seq>" (usato da
    # declined_promotion_seqs per non riproporre i candidati rifiutati).
    "memory_promoted",
    "memory_promotion_declined",
    "memory_summary_reviewed",
    # D15: marker di ritrazione. subject = "phase:N", retracts = decision_seq ritirata.
    # Resta nel log di audit (append-only); effective_decisions() la esclude dalla vista.
    "decision_retracted",
    # D14a: valore citato nella domanda ancorato a una o piu' colonne. subject =
    # "phase:4", detail = il valore (es. "ablazione"), rationale = la/e colonna/e scelta/e
    # dal reviewer (aggregate_lsh_multi le espone tutte senza collassare al miglior match).
    "value_grounded",
    # D14b: formula di concetto approvata/rifiutata dal reviewer. subject = "phase:4",
    # detail = il concetto (es. "fascia pediatrica"), rationale = la/e colonna/e o il motivo.
    # La ricerca nelle Formula Evidence pubblicate restituisce i candidati; queste decisioni
    # registrano la scelta della proposta nella sessione.
    "concept_formula_approved",
    "concept_formula_rejected",
]


class DecisionRecord(BaseModel):
    seq: int
    ts: datetime
    type: DecisionType
    subject: str
    detail: str = ""
    rationale: str = ""
    # D15: se type == "decision_retracted", indica quale seq viene ritirata.
    retracts: int | None = None
    # D15: la fase corrente al momento della scrittura (high-water-mark). Permette a
    # effective_decisions() di marcare stale le decisioni il cui subject NON e' "phase:N"
    # (es. cte_approved usa il nome del CTE) dopo un rollback. None per record storici
    # (pre-fix) o costruiti a mano: in quel caso si ricade sulla logica basata sul subject.
    phase: int | None = None


class DecisionInput(BaseModel):
    type: DecisionType
    subject: str
    detail: str = ""
    rationale: str = ""
    retracts: int | None = None


def list_decisions(session_dir: Path) -> list[DecisionRecord]:
    path = session_dir / DECISIONS_FILE
    if not path.exists():
        return []
    return [
        DecisionRecord.model_validate_json(line)
        for line in path.read_text().splitlines()
        if line.strip()
    ]


def append_decision(
    session_dir: Path,
    *,
    type: str,
    subject: str,
    detail: str = "",
    rationale: str = "",
    retracts: int | None = None,
) -> DecisionRecord:
    return append_decisions(
        session_dir,
        [{
            "type": type,
            "subject": subject,
            "detail": detail,
            "rationale": rationale,
            "retracts": retracts,
        }],
    )[0]


def append_decisions(
    session_dir: Path,
    decisions: list[DecisionInput | dict],
) -> list[DecisionRecord]:
    """Validate and append a decision set through one atomic file replacement."""
    inputs = [DecisionInput.model_validate(decision) for decision in decisions]
    if not inputs:
        return []

    session_dir.mkdir(parents=True, exist_ok=True)
    lock_path = session_dir / DECISIONS_LOCK_FILE
    with lock_path.open("a+") as lock:
        fcntl.flock(lock, fcntl.LOCK_EX)
        try:
            return _append_decisions_locked(session_dir, inputs)
        finally:
            fcntl.flock(lock, fcntl.LOCK_UN)


def _append_decisions_locked(
    session_dir: Path,
    inputs: list[DecisionInput],
) -> list[DecisionRecord]:
    """Append while the caller holds the session's cross-process ledger lock."""

    # Fase corrente PRIMA dell'append (high-water-mark D15). Import lazy: phase.py
    # importa decisions.py (ciclo). Per i marker di fase (subject "phase:N") il valore
    # e' ridondante col subject; per le decisioni sostanziali con subject "a nome"
    # (cte_approved, ...) e' l'unico modo per filtrarle dopo un reopen.
    from tht.phase import current_phase

    existing = list_decisions(session_dir)
    phase = current_phase(session_dir)
    records = [
        DecisionRecord(
            seq=len(existing) + index,
            ts=datetime.now(UTC),
            phase=phase,
            **decision.model_dump(),
        )
        for index, decision in enumerate(inputs, start=1)
    ]
    path = session_dir / DECISIONS_FILE
    path.parent.mkdir(parents=True, exist_ok=True)
    previous = path.read_text() if path.exists() else ""
    separator = "" if not previous or previous.endswith("\n") else "\n"
    content = previous + separator + "".join(record.model_dump_json() + "\n" for record in records)

    fd, temporary_name = tempfile.mkstemp(prefix=f".{DECISIONS_FILE}.", dir=path.parent)
    temporary = Path(temporary_name)
    try:
        with os.fdopen(fd, "w") as handle:
            handle.write(content)
            handle.flush()
            os.fsync(handle.fileno())
        os.replace(temporary, path)
    finally:
        temporary.unlink(missing_ok=True)
    return records