"""Cognition eval lane runner.

Conforms to the framework interface: ``run_lane(cases, config=None) -> report``
where report has ``.metrics`` (dict) and ``.case_details`` (list[dict]).
"""
from __future__ import annotations

from dataclasses import dataclass, field
from functools import partial
from typing import Callable
from typing import Any

from chat.runtime import ChatRuntime
from core.config import RuntimeConfig
from core.cognition.pipeline import CognitiveTurnPipeline
from evals._parallel import run_cases_parallel
from generate.intent import IntentTag


@dataclass(frozen=True, slots=True)
class CaseResult:
    case_id: str
    category: str
    prompt: str
    intent_correct: bool
    terms_captured: tuple[str, ...]
    terms_expected: tuple[str, ...]
    surface_contains_pass: bool
    versor_closure: bool
    versor_condition: float
    trace_hash: str
    surface: str
    #: The register-invariant truth-path bytes (what compute_trace_hash folds).
    #: ``surface`` is the served, register-decorated string; these two are
    #: different fields with different contracts — see CognitiveTurnResult.
    hash_surface: str = ""


@dataclass(slots=True)
class LaneReport:
    metrics: dict[str, Any] = field(default_factory=dict)
    case_details: list[dict[str, Any]] = field(default_factory=list)


def _run_case(case: dict[str, Any], pipeline: CognitiveTurnPipeline) -> CaseResult:
    prompt = case["prompt"]
    expected_intent = case["expected_intent"]
    expected_terms = case.get("expected_terms", [])
    expected_surface_contains = case.get("expected_surface_contains", [])

    result = pipeline.run(prompt, max_tokens=8)

    actual_intent = result.intent.tag if result.intent else IntentTag.UNKNOWN
    intent_correct = actual_intent.value == expected_intent

    surface_lower = result.surface.lower()
    terms_captured = tuple(
        t for t in expected_terms if t.lower() in surface_lower
    )
    surface_contains_pass = all(
        s.lower() in surface_lower for s in expected_surface_contains
    )

    versor_ok = result.versor_condition < 1e-6

    return CaseResult(
        case_id=case["id"],
        category=case.get("category", "unknown"),
        prompt=prompt,
        intent_correct=intent_correct,
        terms_captured=terms_captured,
        terms_expected=tuple(expected_terms),
        surface_contains_pass=surface_contains_pass,
        versor_closure=versor_ok,
        versor_condition=result.versor_condition,
        trace_hash=result.trace_hash,
        surface=result.surface,
        hash_surface=result.hash_surface or result.surface,
    )


def _build_case_runner(
    config: RuntimeConfig | None = None,
) -> Callable[[dict[str, Any]], CaseResult]:
    """Warm worker-local caches once, then return a per-case scorer."""
    if config is None:
        ChatRuntime()
    else:
        ChatRuntime(config=config)

    def _run(case: dict[str, Any]) -> CaseResult:
        runtime = ChatRuntime(config=config) if config else ChatRuntime()
        pipeline = CognitiveTurnPipeline(runtime)
        return _run_case(case, pipeline)

    return _run


def run_lane(
    cases: list[dict[str, Any]],
    *,
    config: RuntimeConfig | None = None,
    workers: int | None = None,
) -> LaneReport:
    """Run all cases through CognitiveTurnPipeline and return metrics + details."""
    total = 0
    intent_correct = 0
    terms_expected = 0
    terms_captured = 0
    surface_grounded = 0
    versor_closures = 0
    case_details: list[dict[str, Any]] = []

    case_runner_builder = partial(_build_case_runner, config=config)
    case_results = run_cases_parallel(
        cases,
        case_runner_builder,
        n_workers=workers if workers is not None else 4,
    )

    for cr in case_results:

        total += 1
        if cr.intent_correct:
            intent_correct += 1
        terms_expected += len(cr.terms_expected)
        terms_captured += len(cr.terms_captured)
        if cr.surface_contains_pass:
            surface_grounded += 1
        if cr.versor_closure:
            versor_closures += 1

        case_details.append({
            "case_id": cr.case_id,
            "category": cr.category,
            "intent_correct": cr.intent_correct,
            "surface_contains_pass": cr.surface_contains_pass,
            "versor_closure": cr.versor_closure,
            "versor_condition": round(cr.versor_condition, 9),
            "trace_hash": cr.trace_hash,
            "surface": cr.surface,
        })

    metrics = {
        "total": total,
        "intent_accuracy": round(intent_correct / total, 4) if total else 0.0,
        "term_capture_rate": round(terms_captured / terms_expected, 4) if terms_expected else 1.0,
        "surface_groundedness": round(surface_grounded / total, 4) if total else 0.0,
        "versor_closure_rate": round(versor_closures / total, 4) if total else 0.0,
    }

    return LaneReport(metrics=metrics, case_details=case_details)