from __future__ import annotations

import os
from dataclasses import dataclass, field
from pathlib import Path

from dotenv import load_dotenv


BACKEND_ROOT = Path(__file__).resolve().parents[1]
DEFAULT_ENV_FILE = BACKEND_ROOT / ".env"


def _as_bool(value: str | None, default: bool) -> bool:
    if value is None:
        return default
    normalized = value.strip().lower()
    if normalized in {"1", "true", "yes", "on"}:
        return True
    if normalized in {"0", "false", "no", "off"}:
        return False
    raise ValueError(f"Invalid boolean configuration value: {value!r}")


def _as_int(name: str, value: str | None, default: int) -> int:
    if value is None:
        return default
    parsed = int(value)
    if parsed < 0:
        raise ValueError(f"{name} must be non-negative")
    return parsed


def _as_float(name: str, value: str | None, default: float) -> float:
    if value is None:
        return default
    parsed = float(value)
    if parsed <= 0:
        raise ValueError(f"{name} must be positive")
    return parsed


@dataclass(frozen=True, slots=True)
class Settings:
    """Runtime settings with the API key deliberately hidden from repr output."""

    llm_provider: str = "auto"
    openai_api_key: str | None = field(default=None, repr=False)
    openai_base_url: str | None = None
    openai_model: str = "gpt-4o-mini"
    openai_timeout_seconds: float = 30.0
    openai_max_retries: int = 2
    structured_output_retries: int = 1
    structured_output_mode: str = "json_schema"
    fallback_to_rules: bool = True

    @property
    def use_openai(self) -> bool:
        if self.llm_provider == "openai":
            if not self.openai_api_key:
                raise ValueError("OPENAI_API_KEY is required when LLM provider is openai")
            return True
        if self.llm_provider == "rule":
            return False
        if self.llm_provider != "auto":
            raise ValueError("RESUME_AGENT_LLM_PROVIDER must be auto, openai, or rule")
        return bool(self.openai_api_key)


def load_settings(env_file: str | Path | None = None) -> Settings:
    selected_file = Path(
        env_file or os.getenv("RESUME_AGENT_ENV_FILE", str(DEFAULT_ENV_FILE))
    )
    load_dotenv(selected_file, override=False)
    mode = os.getenv("OPENAI_STRUCTURED_OUTPUT_MODE", "json_schema").strip().lower()
    if mode not in {"json_schema", "json_object"}:
        raise ValueError(
            "OPENAI_STRUCTURED_OUTPUT_MODE must be json_schema or json_object"
        )
    provider = os.getenv("RESUME_AGENT_LLM_PROVIDER", "auto").strip().lower()
    settings = Settings(
        llm_provider=provider,
        openai_api_key=os.getenv("OPENAI_API_KEY") or None,
        openai_base_url=os.getenv("OPENAI_BASE_URL") or None,
        openai_model=os.getenv("OPENAI_MODEL", "gpt-4o-mini").strip(),
        openai_timeout_seconds=_as_float(
            "OPENAI_TIMEOUT_SECONDS", os.getenv("OPENAI_TIMEOUT_SECONDS"), 30.0
        ),
        openai_max_retries=_as_int(
            "OPENAI_MAX_RETRIES", os.getenv("OPENAI_MAX_RETRIES"), 2
        ),
        structured_output_retries=_as_int(
            "OPENAI_STRUCTURED_OUTPUT_RETRIES",
            os.getenv("OPENAI_STRUCTURED_OUTPUT_RETRIES"),
            1,
        ),
        structured_output_mode=mode,
        fallback_to_rules=_as_bool(
            os.getenv("RESUME_AGENT_LLM_FALLBACK_TO_RULES"), True
        ),
    )
    if not settings.openai_model:
        raise ValueError("OPENAI_MODEL cannot be blank")
    return settings