"""Static configuration for SpriteBench.

Holds repo paths, OpenRouter / budget constants, scoring weights, the judge
model, the verified candidate roster, the sampling plan, and the mock model
identifiers. config.py is the single source of truth for the candidate list;
discover_models.py only verifies against it.
"""

from __future__ import annotations

import os
from dataclasses import dataclass
from pathlib import Path

# --- Paths -----------------------------------------------------------------

ROOT: Path = Path(__file__).resolve().parent
RUNS: Path = ROOT / "runs"
RESULTS: Path = ROOT / "results"
LOGS: Path = ROOT / "logs"

# --- OpenRouter / budget ---------------------------------------------------

OPENROUTER_BASE = "https://openrouter.ai/api/v1"
BUDGET_USD = 25.0  # hard pause threshold; override via env SPRITEBENCH_BUDGET
MAX_PALETTE = 12
GEN_MAX_TOKENS = 8192
# Hidden thinking is disabled for ALL candidate generation calls (fairness:
# same setting every model, every round). Smoke testing showed hybrid
# reasoners (qwen3.6-27b) burn the entire max_tokens budget on reasoning and
# return empty text; the target app is interactive, so thinking-off is also
# the production configuration. None = provider default (judge keeps None).
CANDIDATE_REASONING: bool | None = False

# --- Judge -----------------------------------------------------------------

JUDGE_MODEL = "google/gemini-3.1-pro-preview"  # frontier vision, NOT a candidate
JUDGE_TEMP = 0.0
# Mean abs per-dim delta on duplicate judge pairs; above this -> run a 2nd
# judge pass and average.
CONSISTENCY_THRESHOLD = 1.5

# --- Scoring weights -------------------------------------------------------

WEIGHTS = {"validity": 0.30, "visual": 0.50, "repair": 0.10, "costlat": 0.10}


@dataclass(frozen=True)
class Candidate:
    """A model under test."""

    id: str  # exact OpenRouter id, or a local alias like "local/gemma-4-12b"
    params_b: float  # total params, billions (must be <= 32)
    vision: bool  # can accept images (tier 3 eligible)
    enabled: bool
    notes: str = ""
    # Custom OpenAI-compatible endpoint. None -> OpenRouter. When set, the
    # client sends requests there (no real API key, cost recorded as $0, one
    # request at a time) and omits OpenRouter-only payload fields.
    base_url: str | None = None
    api_model: str | None = None  # model name for the payload; defaults to id


# Verified against live OpenRouter API 2026-06-09. All three confirmed
# available, modality "text+image+video->text", ctx 262144:
#
#   | id                              | params_b | vision | pricing in/out per M | notes                                  |
#   |---------------------------------|----------|--------|----------------------|----------------------------------------|
#   | qwen/qwen3.6-27b                | 27       | True   | $0.289 / $2.40       | dense, prime suspect                   |
#   | google/gemma-4-31b-it:free      | 31       | True   | $0 / $0              | free tier - expect 429s, backoff handles |
#   | google/gemma-4-26b-a4b-it:free  | 26       | True   | $0 / $0              | MoE A4B, free tier                     |
#
# These three ONLY -- per user instruction (2026-06-09) no other candidates
# are added, even ones named in the mission.
#
# MiniCPM: NOT available on OpenRouter at all (verified live 2026-06-09). The
# mission's sponsor-prize MiniCPM candidate could therefore not be included;
# the final report must note this substitution/exclusion. The roster above is
# user-locked: do not add Nemotron, Mistral Small, tiny <=4B models, or any
# other candidate named in the mission -- the user explicitly froze the list
# to these three on 2026-06-09.
CANDIDATES: list[Candidate] = [
    Candidate(
        id="qwen/qwen3.6-27b",
        params_b=27,
        vision=True,
        enabled=True,
        notes="dense, prime suspect",
    ),
    Candidate(
        id="google/gemma-4-31b-it:free",
        params_b=31,
        vision=True,
        enabled=True,
        notes="free tier - expect 429s, backoff handles",
    ),
    Candidate(
        id="google/gemma-4-26b-a4b-it:free",
        params_b=26,
        vision=True,
        enabled=True,
        notes="MoE A4B, free tier",
    ),
    # User's own local server (added 2026-06-09 at user request): llama.cpp-
    # style OpenAI-compatible endpoint, 64K ctx, ~49 tok/s, all layers on GPU.
    # Text-only as served (no images) -> vision=False, sits out Tier-3 repair.
    # The model emits hidden reasoning that cannot be disabled via the API;
    # the answer arrives in message.content so parsing is unaffected, and the
    # latency metric honestly reflects the thinking time.
    Candidate(
        id="local/gemma-4-12b",
        params_b=12,
        vision=False,
        enabled=True,
        notes="user's local server; reasoning baked in; cost $0",
        base_url="http://localhost:8080/v1",
        api_model="gemma-4-12b",
    ),
]

# Sampling plan: 3 samples at temp 0.7, 1 at temp 0.2; fixed seeds for
# reproducibility where the API honors them.
SAMPLES: list[dict] = [
    {"sample": 0, "temp": 0.7, "seed": 11},
    {"sample": 1, "temp": 0.7, "seed": 22},
    {"sample": 2, "temp": 0.7, "seed": 33},
    {"sample": 3, "temp": 0.2, "seed": 44},
]

# Mock model identifiers used by MockClient for offline testing; all vision=True.
MOCK_MODELS = ["mock/good-model", "mock/flaky-model", "mock/bad-model"]


def enabled_candidates(mock: bool = False) -> list[Candidate]:
    """Return the candidate roster to run.

    When mock=True, returns the three MOCK_MODELS wrapped as Candidate objects
    (params_b=1, vision=True, enabled=True). Otherwise returns the enabled
    real candidates from CANDIDATES.
    """
    if mock:
        return [
            Candidate(id=model_id, params_b=1, vision=True, enabled=True)
            for model_id in MOCK_MODELS
        ]
    return [c for c in CANDIDATES if c.enabled]


def endpoint_for(model_id: str) -> tuple[str, str, bool]:
    """Resolve (base_url, payload_model_name, is_openrouter) for a model id.

    Candidates with a custom base_url route there; everything else (including
    the judge model) goes to OpenRouter.
    """
    for c in CANDIDATES:
        if c.id == model_id and c.base_url:
            return c.base_url, c.api_model or c.id, False
    return OPENROUTER_BASE, model_id, True


def budget_usd() -> float:
    """Effective budget: env SPRITEBENCH_BUDGET if set and numeric, else BUDGET_USD."""
    raw = os.environ.get("SPRITEBENCH_BUDGET")
    if raw is not None:
        try:
            return float(raw)
        except ValueError:
            pass
    return BUDGET_USD