from __future__ import annotations

import json
import os
import tomllib
from dataclasses import asdict, dataclass
from pathlib import Path


DEFAULT_VECTOR_STORE = "json-local"


@dataclass(frozen=True)
class ModelConfig:
    ranker: str | None = None
    hunter: str | None = None
    verifier: str | None = None
    patcher: str | None = None


@dataclass(frozen=True)
class EmbeddingConfig:
    model: str | None = None
    store: str = DEFAULT_VECTOR_STORE


@dataclass(frozen=True)
class SandboxConfig:
    network: bool = False
    workspace_readonly: bool = True
    memory_mb: int = 2048
    timeout_seconds: int = 300
    pids_limit: int = 512


@dataclass(frozen=True)
class DynamicConfig:
    enabled: bool = False
    network_scope: tuple[str, ...] = ()


@dataclass(frozen=True)
class EvidenceConfig:
    minimum_report_verified: str = "static_corroboration"
    minimum_exploit: str = "crash_reproduced"
    minimum_patch: str = "root_cause_explained"


@dataclass(frozen=True)
class ResolvedConfig:
    models: ModelConfig = ModelConfig()
    embeddings: EmbeddingConfig = EmbeddingConfig()
    sandbox: SandboxConfig = SandboxConfig()
    dynamic: DynamicConfig = DynamicConfig()
    evidence: EvidenceConfig = EvidenceConfig()
    runs_dir: str = ".openultrasast/runs"


def load_config(config_path: Path | None = None) -> ResolvedConfig:
    data: dict[str, object] = {}
    if config_path is not None and config_path.exists():
        with config_path.open("rb") as handle:
            data = tomllib.load(handle)

    return ResolvedConfig(
        models=_load_models(data.get("models", {})),
        embeddings=_load_embeddings(data.get("embeddings", {})),
        sandbox=_load_sandbox(data.get("sandbox", {})),
        dynamic=_load_dynamic(data.get("dynamic", {})),
        evidence=_load_evidence(data.get("evidence", {})),
        runs_dir=os.environ.get("OPENULTRASAST_RUNS_DIR", ".openultrasast/runs"),
    )


def write_resolved_config(config: ResolvedConfig, path: Path) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    path.write_text(json.dumps(asdict(config), indent=2, sort_keys=True) + "\n")


def _section(value: object) -> dict[str, object]:
    return value if isinstance(value, dict) else {}


def _string(value: object) -> str | None:
    return value if isinstance(value, str) and value else None


def _load_models(value: object) -> ModelConfig:
    data = _section(value)
    return ModelConfig(
        ranker=_string(data.get("ranker")),
        hunter=_string(data.get("hunter")),
        verifier=_string(data.get("verifier")),
        patcher=_string(data.get("patcher")),
    )


def _load_embeddings(value: object) -> EmbeddingConfig:
    data = _section(value)
    return EmbeddingConfig(model=_string(data.get("model")), store=_string(data.get("store")) or DEFAULT_VECTOR_STORE)


def _load_sandbox(value: object) -> SandboxConfig:
    data = _section(value)
    return SandboxConfig(
        network=bool(data.get("network", False)),
        workspace_readonly=bool(data.get("workspace_readonly", True)),
        memory_mb=int(data.get("memory_mb", 2048)),
        timeout_seconds=int(data.get("timeout_seconds", 300)),
        pids_limit=int(data.get("pids_limit", 512)),
    )


def _load_dynamic(value: object) -> DynamicConfig:
    data = _section(value)
    scope = data.get("network_scope", [])
    if not isinstance(scope, list):
        scope = []
    return DynamicConfig(enabled=bool(data.get("enabled", False)), network_scope=tuple(str(item) for item in scope))


def _load_evidence(value: object) -> EvidenceConfig:
    data = _section(value)
    return EvidenceConfig(
        minimum_report_verified=str(data.get("minimum_report_verified", "static_corroboration")),
        minimum_exploit=str(data.get("minimum_exploit", "crash_reproduced")),
        minimum_patch=str(data.get("minimum_patch", "root_cause_explained")),
    )