"""semif-serve HTTP layer. Contract: semif-serve.contract.md."""
from __future__ import annotations

import hmac
import itertools
import math
import threading
from typing import Any, Literal

from fastapi import FastAPI, Request
from fastapi.concurrency import run_in_threadpool
from fastapi.responses import JSONResponse
from pydantic import BaseModel, ConfigDict, ValidationError

from .config import SEMIF_COMMIT, Settings
from .errors import OutOfMemory

OPEN_PATHS = frozenset({"/health"})
State = str | dict[str, Any] | list[Any]


class Option(BaseModel):
    model_config = ConfigDict(extra="ignore")
    id: str
    description: str


class Decision(BaseModel):
    model_config = ConfigDict(extra="ignore")
    id: str
    question: str
    options: list[Option]
    orderings: Literal["none", "rotations", "all"] = "none"

    def row(self, state: State) -> dict:
        """The SemIf row shape: exactly id, state, question, options."""
        return {"id": self.id, "state": state, "question": self.question,
                "options": [o.model_dump() for o in self.options]}


class DecideBody(Decision):
    state: State
    workload: str | None = None


class SharedBody(BaseModel):
    model_config = ConfigDict(extra="ignore")
    state: State
    decisions: list[Decision]
    workload: str | None = None


class ApiError(Exception):
    def __init__(self, status: int, code: str, message: str):
        super().__init__(message)
        self.status, self.code, self.message = status, code, message


def error(status: int, code: str, message: str) -> JSONResponse:
    return JSONResponse(status_code=status, content={"error": {"code": code, "message": message}})


def _first_error(exc: ValidationError) -> str:
    first = exc.errors()[0]
    where = ".".join(str(p) for p in first.get("loc", ())) or "body"
    return f"{where}: {first.get('msg', 'invalid')}"


MAX_OPTIONS_FOR_ALL = 4


def ordering_perms(decision: Decision) -> list[tuple[int, ...]]:
    """Index permutations of the caller's options, the caller's own order first."""
    n = len(decision.options)
    if decision.orderings == "rotations":
        return [tuple((start + k) % n for k in range(n)) for start in range(n)]
    if n > MAX_OPTIONS_FOR_ALL:
        raise ApiError(422, "invalid_request",
                       f"orderings 'all' allows at most {MAX_OPTIONS_FOR_ALL} options ({n} given); use 'rotations'")
    return list(itertools.permutations(range(n)))


def expanded_rows(decision: Decision, state: State, perms: list[tuple[int, ...]]) -> list[dict]:
    base = decision.row(state)
    return [{**base, "id": f"{decision.id}#o{k}", "options": [base["options"][i] for i in perm]}
            for k, perm in enumerate(perms)]


def combine(decision: Decision, results: list[dict]) -> dict:
    """Average per-ordering log-softmax by option id; the native results ride along unchanged."""
    option_ids = [o.id for o in decision.options]
    logp: dict[str, list[float]] = {i: [] for i in option_ids}
    probs: dict[str, list[float]] = {i: [] for i in option_ids}
    tops = []
    for result in results:
        logits = result["option_logits"]
        top = max(logits)
        lse = top + math.log(sum(math.exp(x - top) for x in logits))
        for oid, x, p in zip(result["option_ids"], logits, result["probabilities"]):
            logp[oid].append(x - lse)
            probs[oid].append(p)
        tops.append(result["option_ids"][logits.index(top)])
    means = [sum(logp[i]) / len(logp[i]) for i in option_ids]
    peak = max(means)
    weights = [math.exp(m - peak) for m in means]
    combined_p = [w / sum(weights) for w in weights]
    winner = option_ids[combined_p.index(max(combined_p))]
    return {
        "id": decision.id,
        "option_ids": option_ids,
        "combined": {
            "method": decision.orderings,
            "orderings": len(results),
            "probabilities": combined_p,
            "top": winner,
            "agreement": tops.count(winner) / len(tops),
            "spread": {i: [min(probs[i]), max(probs[i])] for i in option_ids},
        },
        "orderings": results,
    }


async def read_limited(stream, declared: str | None, limit: int) -> bytes:
    """Read a request body, refusing it once it would exceed `limit` bytes. The check runs BEFORE a
    chunk is kept, and nothing after the crossing chunk is read (bug hunt C2). A declared length is
    trusted only as ASCII digits: `"²".isdigit()` is True but `int("²")` raises (S3)."""
    too_large = ApiError(413, "request_too_large", f"request body exceeds {limit} bytes")
    if declared is not None and declared.isascii() and declared.isdigit() and int(declared) > limit:
        raise too_large
    body = bytearray()
    async for chunk in stream:
        if len(body) + len(chunk) > limit:
            raise too_large
        body.extend(chunk)
    return bytes(body)


def calibrated_view(result: dict, workload: str, temperature: float) -> dict:
    """softmax(option_logits / T): the native fields are left exactly as SemIf returned them (INV-1)."""
    scaled = [x / temperature for x in result["option_logits"]]
    top = max(scaled)
    weights = [math.exp(x - top) for x in scaled]
    total = sum(weights)
    return {"workload": workload, "temperature": temperature, "probabilities": [w / total for w in weights]}


def create_app(settings: Settings, engine: Any) -> FastAPI:
    app = FastAPI(title="semif-serve")
    expected = f"Bearer {settings.api_token}".encode()
    inference = threading.Lock()   # INV-2: one scorer call at a time, off the event loop
    in_progress = 0                 # POSTs admitted and not yet answered (bug hunt C6)

    def admit():
        nonlocal in_progress
        if in_progress >= settings.max_queue:
            raise ApiError(429, "busy", f"{in_progress} requests already in progress (limit {settings.max_queue})")
        in_progress += 1

    def leave():
        nonlocal in_progress
        in_progress -= 1

    def build(fn, *args):
        """Response construction from a scorer result (calibration, averaging) maps its failures to
        the 500 envelope too, instead of escaping as a bare 500 (bug hunt C3)."""
        try:
            return fn(*args)
        except ApiError:
            raise
        except Exception as exc:  # noqa: BLE001
            raise ApiError(500, "scoring_failed", f"building the response failed: {type(exc).__name__}: {exc}") from exc

    def locked(fn, *args):
        with inference:
            return fn(*args)

    @app.middleware("http")
    async def require_bearer(request: Request, call_next):
        if request.url.path not in OPEN_PATHS:
            supplied = request.headers.get("authorization", "").encode()
            if not hmac.compare_digest(supplied, expected):   # INV-6
                return error(401, "unauthorized", "missing or wrong bearer token")
        return await call_next(request)

    @app.exception_handler(ApiError)
    async def api_error(_request: Request, exc: ApiError):
        return error(exc.status, exc.code, exc.message)

    async def parse(request: Request, model: type[BaseModel]):
        try:
            return model.model_validate_json(
                await read_limited(request.stream(), request.headers.get("content-length"), settings.max_body_bytes))
        except ValidationError as exc:
            raise ApiError(422, "invalid_request", _first_error(exc)) from exc

    async def score(fn, *args):
        """Run one scorer call in a worker thread under the lock; map its failures to contract codes."""
        try:
            return await run_in_threadpool(locked, fn, *args)
        except ValueError as exc:            # SemIf validation, token limit, tokenisation
            raise ApiError(422, "invalid_request", str(exc)) from exc
        except OutOfMemory as exc:
            raise ApiError(503, "out_of_memory", str(exc)) from exc
        except Exception as exc:             # noqa: BLE001 — any other scorer failure
            raise ApiError(500, "scoring_failed", f"{type(exc).__name__}: {exc}") from exc

    def temperature_for(workload: str | None) -> float | None:
        if workload is None:
            return None
        if workload not in settings.calibration:
            raise ApiError(422, "invalid_request", f"unknown workload {workload!r}")
        return settings.calibration[workload]

    def with_calibration(result: dict, workload: str | None, temperature: float | None) -> dict:
        if temperature is None:
            return result
        return {**result, "calibrated": calibrated_view(result, workload, temperature)}

    @app.get("/health")
    async def health():
        return {"status": "ok", "semif_commit": SEMIF_COMMIT, "model": engine.health(),
                "vram_cap_gib": settings.vram_cap_gib, "max_tokens": settings.max_tokens,
                "max_decisions": settings.max_decisions, "workloads": sorted(settings.calibration)}

    async def score_batch(decisions: list[Decision], state: State, workload: str | None) -> tuple[list[dict], dict]:
        """One engine.shared call for every row of every decision; results in request order."""
        plan = []            # (decision, perms or None, row count)
        rows: list[dict] = []
        for d in decisions:
            if d.orderings == "none":
                plan.append((d, None, 1))
                rows.append(d.row(state))
            else:
                if workload is not None:
                    raise ApiError(422, "invalid_request",
                                   "workload calibration is not available together with orderings")
                perms = ordering_perms(d)
                plan.append((d, perms, len(perms)))
                rows.extend(expanded_rows(d, state, perms))
        if not 1 <= len(rows) <= settings.max_decisions:
            raise ApiError(422, "invalid_request",
                           f"this request expands to {len(rows)} scored rows; the limit is 1..{settings.max_decisions}")
        temperature = temperature_for(workload)
        results, timing = await score(engine.shared, rows)
        out, cursor = [], 0
        for d, perms, count in plan:
            chunk = results[cursor:cursor + count]
            cursor += count
            out.append(build(with_calibration, chunk[0], workload, temperature) if perms is None
                       else build(combine, d, chunk))
        return out, timing

    @app.post("/decide")
    async def decide(request: Request):
        admit()
        try:
            body = await parse(request, DecideBody)
            if body.orderings != "none":
                results, _timing = await score_batch([body], body.state, body.workload)
                return results[0]
            temperature = temperature_for(body.workload)
            result = await score(engine.direct, body.row(body.state))
            return build(with_calibration, result, body.workload, temperature)
        finally:
            leave()

    @app.post("/decide/shared")
    async def decide_shared(request: Request):
        admit()
        try:
            body = await parse(request, SharedBody)
            if not 1 <= len(body.decisions) <= settings.max_decisions:
                raise ApiError(422, "invalid_request",
                               f"decisions must hold 1..{settings.max_decisions} entries, got {len(body.decisions)}")
            results, timing = await score_batch(body.decisions, body.state, body.workload)
            return {"results": results, "timing": timing}
        finally:
            leave()

    return app