from dataclasses import dataclass, field
from pathlib import Path
try:
    import tomllib
except ImportError:
    try:
        import tomli as tomllib
    except ImportError:
        tomllib = None


@dataclass
class Config:
    project_name: str = ""
    copybook_paths: list[str] = field(default_factory=lambda: ["./copybooks"])
    dialect: str = "ibm"
    llm_model: str = "gpt-4o-mini"
    llm_timeout: int = 15
    llm_cache_dir: str = ".cache/llm"
    coverage_default: str = "boundary"
    rounding_mode: str = "TRUNCATE"
    tolerance: float = 0.01
    runner_mode: str = "native"
    spark_master: str = "local[*]"
    spark_input_format: str = "json"
    num_records: int = 1000
    branch_pass: float = 0.80
    max_llm_cost: float = 0.50

    @classmethod
    def from_toml(cls, path: str = "aurak.toml") -> "Config":
        if tomllib is None:
            return cls()
        with open(path, "rb") as f:
            data = tomllib.load(f)
        c = cls()
        p = data.get("project", {})
        c.project_name = p.get("name", c.project_name)
        c.copybook_paths = p.get("copybook_paths", c.copybook_paths)
        c.dialect = p.get("dialect", c.dialect)
        llm = data.get("llm", {})
        c.llm_model = llm.get("model", c.llm_model)
        c.llm_timeout = llm.get("timeout", c.llm_timeout)
        c.llm_cache_dir = llm.get("cache_dir", c.llm_cache_dir)
        cov = data.get("coverage", {})
        c.coverage_default = cov.get("default_target", c.coverage_default)
        cmp = data.get("comparison", {})
        c.rounding_mode = cmp.get("rounding_mode", c.rounding_mode)
        c.tolerance = cmp.get("default_tolerance", c.tolerance)
        r = data.get("runner", {})
        c.runner_mode = r.get("mode", c.runner_mode)
        sp = data.get("spark", {})
        c.spark_master = sp.get("master", c.spark_master)
        c.spark_input_format = sp.get("input_format", c.spark_input_format)
        c.num_records = sp.get("num_records", c.num_records)
        return c