from __future__ import annotations

from dataclasses import dataclass
from typing import Literal

Provider = Literal["k1412", "deepseek"]


@dataclass(frozen=True, slots=True)
class ModelSpec:
    public_id: str
    display_name: str
    provider: Provider
    provider_model: str
    thinking_enabled: bool
    thinking_label: str
    thinking_adjustable: bool
    reasoning_effort: str | None
    max_output_tokens: int
    max_iterations: int
    context_char_budget: int


MODEL_SPECS: dict[str, ModelSpec] = {
    "luna": ModelSpec(
        public_id="luna",
        display_name="Luna",
        provider="k1412",
        provider_model="ChatGPT-5.6:Luna",
        thinking_enabled=True,
        thinking_label="开启（不分档）",
        thinking_adjustable=False,
        reasoning_effort=None,
        max_output_tokens=4_096,
        max_iterations=8,
        context_char_budget=120_000,
    ),
    "terra": ModelSpec(
        public_id="terra",
        display_name="Terra",
        provider="k1412",
        provider_model="ChatGPT-5.6:Terra",
        thinking_enabled=True,
        thinking_label="开启（不分档）",
        thinking_adjustable=False,
        reasoning_effort=None,
        max_output_tokens=4_096,
        max_iterations=16,
        context_char_budget=240_000,
    ),
    "sol": ModelSpec(
        public_id="sol",
        display_name="Sol",
        provider="k1412",
        provider_model="ChatGPT-5.6:Sol",
        thinking_enabled=True,
        thinking_label="开启（不分档）",
        thinking_adjustable=False,
        reasoning_effort=None,
        max_output_tokens=4_096,
        max_iterations=24,
        context_char_budget=400_000,
    ),
    "deepseek-v4-pro": ModelSpec(
        public_id="deepseek-v4-pro",
        display_name="DeepSeek V4 Pro",
        provider="deepseek",
        provider_model="deepseek-v4-pro",
        thinking_enabled=True,
        thinking_label="极高",
        thinking_adjustable=False,
        reasoning_effort="max",
        max_output_tokens=16_384,
        max_iterations=32,
        context_char_budget=800_000,
    ),
}


def get_model_spec(model_id: str) -> ModelSpec:
    try:
        return MODEL_SPECS[model_id]
    except KeyError as exc:
        raise ValueError(f"Unsupported model: {model_id}") from exc


def openai_model_list() -> dict:
    return {
        "object": "list",
        "data": [
            {
                "id": spec.public_id,
                "object": "model",
                "owned_by": "k1412-agent",
                "name": spec.display_name,
                "k1412": {
                    "thinking_enabled": spec.thinking_enabled,
                    "thinking_label": spec.thinking_label,
                    "thinking_adjustable": spec.thinking_adjustable,
                },
            }
            for spec in MODEL_SPECS.values()
        ],
    }