"""Configuration management for STT daemon."""

from dataclasses import dataclass, field
import logging
import tomllib
from pathlib import Path
from typing import Any

logger = logging.getLogger(__name__)

KNOWN_SECTIONS = {
    "audio",
    "vad",
    "whisper",
    "output",
    "sink",
    "daemon",
    "events",
    "assistant",
    "audit",
    "llm",
    "triage",
    "speaker",
    "brief",
    "debug",
}


@dataclass
class AudioConfig:
    """Audio capture configuration."""
    backend: str = "parec"
    device: str = "VirtualMicSink.monitor"
    sample_rate: int = 16000
    channels: int = 1
    frame_ms: int = 30
    gain: float = 1.0


@dataclass
class VadConfig:
    """Voice Activity Detection configuration."""
    aggressiveness: int = 2
    pre_roll_ms: int = 400
    speech_start_ms: int = 90
    silence_ms: int = 700
    min_utterance_ms: int = 300
    max_utterance_s: float = 30.0
    chunk_pause_ms: int = 350
    soft_max_s: float = 10.0
    rms_floor: float = 0.008
    trailing_trim_ms: int = 200


@dataclass
class WhisperConfig:
    """Whisper transcription configuration."""
    backend: str = "faster-whisper"
    model: str = "small"
    device: str = "cpu"
    compute_type: str = "int8"
    language: str | None = None
    beam_size: int = 1
    vad_filter: bool = False
    cpu_threads: int = 0
    no_speech_prob_max: float = 0.6
    avg_logprob_min: float = -1.0
    language_strategy: str = "static"
    sticky_redetect_logprob: float = -0.8


@dataclass
class OutputConfig:
    """Output configuration."""
    stdout: bool = True
    jsonl: bool = False
    session_dir: str = "transcripts"
    save_audio: bool = False
    session_gap_min: float = 5.0


@dataclass
class DaemonConfig:
    """Daemon runtime configuration."""
    queue_size: int = 50
    backlog_warn_at: int = 5


@dataclass
class EventsConfig:
    """Publish-only event bus configuration."""
    socket: str = ""
    heartbeat_ms: int = 1000
    ring_size: int = 200


@dataclass
class AssistantConfig:
    """Assistant (controller) configuration."""
    control_socket: str = ""
    stall_timeout_ms: int = 3000
    worker_stall_ms: int = 15000
    command_arm_timeout_s: float = 10.0
    chat_idle_timeout_s: float = 300.0
    chat_exit_phrases: list[str] = field(
        default_factory=lambda: ["end conversation", "that's all", "stop listening"]
    )
    require_viewer: bool = True
    turn_gap_s: float = 1.5


@dataclass
class AuditConfig:
    """Action audit configuration (daily JSONL, like transcripts)."""
    enabled: bool = True
    dir: str = "troubleshoot/audit"


@dataclass
class LlmConfig:
    """llama.cpp (OpenAI-compatible) configuration."""
    enabled: bool = True
    endpoint: str = "http://localhost:9931/v1/chat/completions"
    model: str = ""
    api_key_env: str = ""
    session_id: str = ""
    session_header: str = ""
    user_agent: str = ""
    reasoning_effort: str = ""
    extra_body: dict[str, Any] = field(default_factory=dict)
    temperature: float = 0.7
    max_tokens: int = 1024
    timeout_s: float = 120.0
    cache_prompt: bool = True
    chat_slot_id: int | None = None
    command_slot_id: int | None = None
    max_context_tokens: int = 131072
    compaction_safety_margin: int = 256
    compaction_enabled: bool = True
    compaction_trigger_ratio: float = 0.8
    compaction_keep_turns: int = 6
    compaction_max_tokens: int = 512
    compaction_timeout_s: float = 30.0
    compaction_input_tokens: int = 8192
    compaction_system_prompt: str = (
        "You compress a conversation. Preserve names, numbers, decisions, user "
        "preferences, and unresolved questions. Omit pleasantries. Output only the summary."
    )
    brief_compaction_system_prompt: str = (
        "You compress a triage briefing conversation. Preserve every opportunity, blocker, "
        "owner, responsibility, decision, deadline, and open question; drop pleasantries. "
        "Output only the summary."
    )
    triage_system_prompt: str = (
        "You maintain a living brief of a business conversation. The caller sends "
        "CURRENT LEDGER plus CURRENT BRIEF plus NEW UTTERANCES. Reply with exactly "
        "two sections in this order: NEW FACTS: first, then BRIEF:. Facts go first "
        "so a truncated reply still preserves extraction (truncation safety). In "
        "NEW FACTS:, emit one fact per line as '- <fact>', or the single line "
        "'none' when the new utterances add no new facts. Never repeat facts "
        "already listed in CURRENT LEDGER. In BRIEF:, return the UPDATED brief as "
        "plain text only, with no markdown fences and no JSON. Open with a Summary "
        "section: a rolling overview of the conversation so far, one to a few "
        "sentences for a short session and growing to a short paragraph or two as "
        "the session extends. Preserve prior content unless the new utterances "
         "contradict it; merge duplicates; prune stale items; keep owners, "
        "responsibilities, and deadlines explicit. Use short sections: Summary, "
        "Opportunities, Blockers/Risks, Responsibilities, Decisions, Action Items, "
        "Deadlines, Open Questions. Never silently delete Action Items, Open "
        "Questions, or Decisions lines. When an action completes or a question "
        "is answered, do NOT drop the line — move it to Decisions as "
        "'Done: <original> — resolved: <what happened>' (or keep it in place "
        "with a 'Done (...)' suffix) AND list the resolution as a NEW FACTS "
        "bullet. Prune only exact duplicates, never resolved history. "
        "Never emit a 'Prior Details:' section. Output "
        "the two sections directly; never emit tool calls, function-call syntax, "
        "or special wrapper tokens."
    )
    inference_queue_size: int = 8
    trim_block_turns: int = 4
    max_tool_iterations: int = 3
    system_prompt_chat: str = (
        "You are a concise voice assistant. Reply in 1-3 sentences."
    )
    system_prompt_command: str = (
        "You translate a spoken request into a single tool call when one applies."
    )

    @property
    def prompt_budget(self) -> int:
        """Prompt tokens available: the context window minus generation and slack."""
        return self.max_context_tokens - self.max_tokens - self.compaction_safety_margin


@dataclass
class TriageConfig:
    """Laya triage-panel configuration (in-process lane, multilingual pinned)."""
    enabled: bool = False
    questions_file: str = "questions.json"
    window: int = 500
    state_char_budget: int = 3000
    periodic_s: float = 30.0
    predict_max_tokens: int = 1024
    confidence_field: str = "answer_confidence"
    escalate_threshold: float = 0.6
    choice_min_confidence: float = 0.85
    extract_enabled: bool = True
    extract_max_tokens: int = 3072
    extract_timeout_s: float = 30.0
    extract_temperature: float = 0.2
    brief_slot_id: int | None = None
    brief_gate_enabled: bool = True
    brief_gate_threshold: float = 0.5
    brief_max_chars: int = 4000
    brief_pending_char_budget: int = 3000
    ledger_max_facts: int = 200
    fact_max_chars: int = 240
    device: str = ""
    preload: list[str] = field(default_factory=lambda: ["multilingual"])
    default_checkpoint: str = "multilingual"
    dump_io: bool = False


@dataclass
class SpeakerConfig:
    """Speaker-identification configuration (sherpa-onnx embedding lane)."""
    enabled: bool = False
    model_path: str | None = None
    provider: str = "cpu"
    threshold_id: float = 0.60
    threshold_new: float = 0.45
    min_enroll_s: float = 2.0
    rms_floor: float = 0.0
    margin: float = 0.04
    mint_cooldown_s: float = 60.0
    confirm_s: float = 12.0
    max_prototypes: int = 8
    provisional_ttl_h: float = 24.0
    store_dir: str = "speakers"
    join_timeout_s: float = 2.0


@dataclass
class BriefConfig:
    """Durable brief document configuration (markdown snapshot written to disk)."""
    write: bool = False


@dataclass
class DebugConfig:
    """Debug signal-log configuration (JSONL diagnostics, gitignored)."""
    enabled: bool = False
    dir: str = "troubleshoot/debug"
    heartbeat: bool = True
    max_bytes_per_day: int = 50000000


@dataclass
class SinkConfig:
    """Virtual audio sink configuration."""
    sink_name: str = "VirtualMicSink"
    remap_source_name: str = "VirtualMic"
    loopback_latency_msec: int = 1
    monitor_output: bool = False


@dataclass
class Config:
    """Complete daemon configuration."""
    audio: AudioConfig = field(default_factory=AudioConfig)
    vad: VadConfig = field(default_factory=VadConfig)
    whisper: WhisperConfig = field(default_factory=WhisperConfig)
    output: OutputConfig = field(default_factory=OutputConfig)
    sink: SinkConfig = field(default_factory=SinkConfig)
    daemon: DaemonConfig = field(default_factory=DaemonConfig)
    events: EventsConfig = field(default_factory=EventsConfig)
    assistant: AssistantConfig = field(default_factory=AssistantConfig)
    audit: AuditConfig = field(default_factory=AuditConfig)
    llm: LlmConfig = field(default_factory=LlmConfig)
    triage: TriageConfig = field(default_factory=TriageConfig)
    speaker: SpeakerConfig = field(default_factory=SpeakerConfig)
    brief: BriefConfig = field(default_factory=BriefConfig)
    debug: DebugConfig = field(default_factory=DebugConfig)


def _optional_int(value: Any) -> int | None:
    """Coerce an optional TOML value to int, degrading to None on bad input.

    TOML has no null, so an absent key is "unset" and reaches this helper as
    ``None``. ``0`` is a valid slot id and must never be mistaken for unset.
    """
    if value is None:
        return None
    try:
        return int(value)
    except (TypeError, ValueError):
        return None


def load_config(path: str | None = None) -> Config:
    """Load configuration from TOML file, with defaults.

    Args:
        path: Path to config.toml. If None, looks for config.toml in cwd,
              then uses all defaults.

    Returns:
        Config with loaded values overlayed on defaults.
    """
    cfg = Config()

    # Try to find and load config file
    config_path = None
    if path:
        config_path = Path(path)
    else:
        default_path = Path("config.toml")
        if default_path.exists():
            config_path = default_path

    if config_path and config_path.exists():
        with open(config_path, "rb") as f:
            data = tomllib.load(f)

        unknown = set(data) - KNOWN_SECTIONS
        if unknown:
            logger.warning(
                "Ignoring unknown config sections: %s", ", ".join(sorted(unknown))
            )

        # Audio section
        if "audio" in data:
            audio_data = data["audio"]
            cfg.audio = AudioConfig(
                backend=audio_data.get("backend", cfg.audio.backend),
                device=audio_data.get("device", cfg.audio.device),
                sample_rate=audio_data.get("sample_rate", cfg.audio.sample_rate),
                channels=audio_data.get("channels", cfg.audio.channels),
                frame_ms=audio_data.get("frame_ms", cfg.audio.frame_ms),
                gain=audio_data.get("gain", cfg.audio.gain),
            )

        # VAD section
        if "vad" in data:
            vad_data = data["vad"]
            cfg.vad = VadConfig(
                aggressiveness=vad_data.get("aggressiveness", cfg.vad.aggressiveness),
                pre_roll_ms=vad_data.get("pre_roll_ms", cfg.vad.pre_roll_ms),
                speech_start_ms=vad_data.get("speech_start_ms", cfg.vad.speech_start_ms),
                silence_ms=vad_data.get("silence_ms", cfg.vad.silence_ms),
                min_utterance_ms=vad_data.get("min_utterance_ms", cfg.vad.min_utterance_ms),
                max_utterance_s=vad_data.get("max_utterance_s", cfg.vad.max_utterance_s),
                chunk_pause_ms=vad_data.get("chunk_pause_ms", cfg.vad.chunk_pause_ms),
                soft_max_s=vad_data.get("soft_max_s", cfg.vad.soft_max_s),
                rms_floor=vad_data.get("rms_floor", cfg.vad.rms_floor),
                trailing_trim_ms=vad_data.get("trailing_trim_ms", cfg.vad.trailing_trim_ms),
            )

        # Whisper section
        if "whisper" in data:
            whisper_data = data["whisper"]
            backend = whisper_data.get("backend", cfg.whisper.backend)
            backend = backend.strip().lower().replace("_", "-")
            if backend not in ("faster-whisper", "pywhispercpp"):
                logger.warning(
                    "Unknown whisper backend %r; falling back to 'faster-whisper'", backend
                )
                backend = "faster-whisper"
            language = whisper_data.get("language", cfg.whisper.language)
            # Normalize language: "", "auto", "none" -> None
            if language in ("", "auto", "none"):
                language = None
            strategy = whisper_data.get("language_strategy", cfg.whisper.language_strategy)
            if strategy not in ("static", "sticky"):
                logger.warning(
                    "Unknown language_strategy %r; falling back to 'static'", strategy
                )
                strategy = "static"
            cfg.whisper = WhisperConfig(
                backend=backend,
                model=whisper_data.get("model", cfg.whisper.model),
                device=whisper_data.get("device", cfg.whisper.device),
                compute_type=whisper_data.get("compute_type", cfg.whisper.compute_type),
                language=language,
                beam_size=whisper_data.get("beam_size", cfg.whisper.beam_size),
                vad_filter=whisper_data.get("vad_filter", cfg.whisper.vad_filter),
                cpu_threads=whisper_data.get("cpu_threads", cfg.whisper.cpu_threads),
                no_speech_prob_max=whisper_data.get(
                    "no_speech_prob_max", cfg.whisper.no_speech_prob_max
                ),
                avg_logprob_min=whisper_data.get(
                    "avg_logprob_min", cfg.whisper.avg_logprob_min
                ),
                language_strategy=strategy,
                sticky_redetect_logprob=whisper_data.get(
                    "sticky_redetect_logprob", cfg.whisper.sticky_redetect_logprob
                ),
            )

        # Output section
        if "output" in data:
            output_data = data["output"]
            cfg.output = OutputConfig(
                stdout=output_data.get("stdout", cfg.output.stdout),
                jsonl=output_data.get("jsonl", cfg.output.jsonl),
                session_dir=output_data.get("session_dir", cfg.output.session_dir),
                save_audio=output_data.get("save_audio", cfg.output.save_audio),
                session_gap_min=output_data.get(
                    "session_gap_min", cfg.output.session_gap_min
                ),
            )

        # Sink section
        if "sink" in data:
            sink_data = data["sink"]
            cfg.sink = SinkConfig(
                sink_name=sink_data.get("sink_name", cfg.sink.sink_name),
                remap_source_name=sink_data.get(
                    "remap_source_name", cfg.sink.remap_source_name
                ),
                loopback_latency_msec=sink_data.get(
                    "loopback_latency_msec", cfg.sink.loopback_latency_msec
                ),
                monitor_output=sink_data.get("monitor_output", cfg.sink.monitor_output),
            )

        # Daemon section
        if "daemon" in data:
            daemon_data = data["daemon"]
            cfg.daemon = DaemonConfig(
                queue_size=daemon_data.get("queue_size", cfg.daemon.queue_size),
                backlog_warn_at=daemon_data.get(
                    "backlog_warn_at", cfg.daemon.backlog_warn_at
                ),
            )

        # Events section
        if "events" in data:
            events_data = data["events"]
            cfg.events = EventsConfig(
                socket=events_data.get("socket", cfg.events.socket),
                heartbeat_ms=events_data.get("heartbeat_ms", cfg.events.heartbeat_ms),
                ring_size=events_data.get("ring_size", cfg.events.ring_size),
            )

        # Assistant section
        if "assistant" in data:
            assistant_data = data["assistant"]
            cfg.assistant = AssistantConfig(
                control_socket=assistant_data.get(
                    "control_socket", cfg.assistant.control_socket
                ),
                stall_timeout_ms=assistant_data.get(
                    "stall_timeout_ms", cfg.assistant.stall_timeout_ms
                ),
                worker_stall_ms=assistant_data.get(
                    "worker_stall_ms", cfg.assistant.worker_stall_ms
                ),
                command_arm_timeout_s=assistant_data.get(
                    "command_arm_timeout_s", cfg.assistant.command_arm_timeout_s
                ),
                chat_idle_timeout_s=assistant_data.get(
                    "chat_idle_timeout_s", cfg.assistant.chat_idle_timeout_s
                ),
                chat_exit_phrases=list(
                    assistant_data.get(
                        "chat_exit_phrases", cfg.assistant.chat_exit_phrases
                    )
                ),
                require_viewer=assistant_data.get(
                    "require_viewer", cfg.assistant.require_viewer
                ),
                turn_gap_s=assistant_data.get("turn_gap_s", cfg.assistant.turn_gap_s),
            )

        # Audit section
        if "audit" in data:
            audit_data = data["audit"]
            cfg.audit = AuditConfig(
                enabled=audit_data.get("enabled", cfg.audit.enabled),
                dir=audit_data.get("dir", cfg.audit.dir),
            )

        # LLM section
        if "llm" in data:
            llm_data = data["llm"]
            if "max_prompt_tokens" in llm_data:
                logger.warning(
                    "llm.max_prompt_tokens is deprecated and ignored; the prompt budget is "
                    "derived as max_context_tokens - max_tokens - compaction_safety_margin"
                )
            cfg.llm = LlmConfig(
                enabled=llm_data.get("enabled", cfg.llm.enabled),
                endpoint=llm_data.get("endpoint", cfg.llm.endpoint),
                model=llm_data.get("model", cfg.llm.model),
                api_key_env=llm_data.get("api_key_env", cfg.llm.api_key_env),
                session_id=llm_data.get("session_id", cfg.llm.session_id),
                session_header=llm_data.get("session_header", cfg.llm.session_header),
                user_agent=llm_data.get("user_agent", cfg.llm.user_agent),
                reasoning_effort=llm_data.get(
                    "reasoning_effort", cfg.llm.reasoning_effort
                ),
                extra_body=dict(llm_data.get("extra_body", cfg.llm.extra_body)),
                temperature=llm_data.get("temperature", cfg.llm.temperature),
                max_tokens=llm_data.get("max_tokens", cfg.llm.max_tokens),
                timeout_s=llm_data.get("timeout_s", cfg.llm.timeout_s),
                cache_prompt=llm_data.get("cache_prompt", cfg.llm.cache_prompt),
                chat_slot_id=_optional_int(llm_data.get("chat_slot_id")),
                command_slot_id=_optional_int(llm_data.get("command_slot_id")),
                max_context_tokens=llm_data.get(
                    "max_context_tokens", cfg.llm.max_context_tokens
                ),
                compaction_safety_margin=llm_data.get(
                    "compaction_safety_margin", cfg.llm.compaction_safety_margin
                ),
                compaction_enabled=llm_data.get(
                    "compaction_enabled", cfg.llm.compaction_enabled
                ),
                compaction_trigger_ratio=llm_data.get(
                    "compaction_trigger_ratio", cfg.llm.compaction_trigger_ratio
                ),
                compaction_keep_turns=llm_data.get(
                    "compaction_keep_turns", cfg.llm.compaction_keep_turns
                ),
                compaction_max_tokens=llm_data.get(
                    "compaction_max_tokens", cfg.llm.compaction_max_tokens
                ),
                compaction_timeout_s=llm_data.get(
                    "compaction_timeout_s", cfg.llm.compaction_timeout_s
                ),
                compaction_input_tokens=llm_data.get(
                    "compaction_input_tokens", cfg.llm.compaction_input_tokens
                ),
                compaction_system_prompt=llm_data.get(
                    "compaction_system_prompt", cfg.llm.compaction_system_prompt
                ),
                brief_compaction_system_prompt=llm_data.get(
                    "brief_compaction_system_prompt", cfg.llm.brief_compaction_system_prompt
                ),
                triage_system_prompt=llm_data.get(
                    "triage_system_prompt", cfg.llm.triage_system_prompt
                ),
                inference_queue_size=llm_data.get(
                    "inference_queue_size", cfg.llm.inference_queue_size
                ),
                trim_block_turns=llm_data.get(
                    "trim_block_turns", cfg.llm.trim_block_turns
                ),
                max_tool_iterations=llm_data.get(
                    "max_tool_iterations", cfg.llm.max_tool_iterations
                ),
                system_prompt_chat=llm_data.get(
                    "system_prompt_chat", cfg.llm.system_prompt_chat
                ),
                system_prompt_command=llm_data.get(
                    "system_prompt_command", cfg.llm.system_prompt_command
                ),
            )

        # Triage section
        if "triage" in data:
            triage_data = data["triage"]
            cfg.triage = TriageConfig(
                enabled=triage_data.get("enabled", cfg.triage.enabled),
                questions_file=triage_data.get("questions_file", cfg.triage.questions_file),
                window=triage_data.get("window", cfg.triage.window),
                state_char_budget=triage_data.get(
                    "state_char_budget", cfg.triage.state_char_budget
                ),
                periodic_s=triage_data.get("periodic_s", cfg.triage.periodic_s),
                predict_max_tokens=triage_data.get(
                    "predict_max_tokens", cfg.triage.predict_max_tokens
                ),
                confidence_field=triage_data.get(
                    "confidence_field", cfg.triage.confidence_field
                ),
                escalate_threshold=triage_data.get(
                    "escalate_threshold", cfg.triage.escalate_threshold
                ),
                choice_min_confidence=triage_data.get(
                    "choice_min_confidence", cfg.triage.choice_min_confidence
                ),
                extract_enabled=triage_data.get(
                    "extract_enabled", cfg.triage.extract_enabled
                ),
                extract_max_tokens=triage_data.get(
                    "extract_max_tokens", cfg.triage.extract_max_tokens
                ),
                extract_timeout_s=triage_data.get(
                    "extract_timeout_s", cfg.triage.extract_timeout_s
                ),
                extract_temperature=triage_data.get(
                    "extract_temperature", cfg.triage.extract_temperature
                ),
                brief_slot_id=_optional_int(triage_data.get("brief_slot_id")),
                brief_gate_enabled=triage_data.get(
                    "brief_gate_enabled", cfg.triage.brief_gate_enabled
                ),
                brief_gate_threshold=triage_data.get(
                    "brief_gate_threshold", cfg.triage.brief_gate_threshold
                ),
                brief_max_chars=triage_data.get(
                    "brief_max_chars", cfg.triage.brief_max_chars
                ),
                brief_pending_char_budget=triage_data.get(
                    "brief_pending_char_budget", cfg.triage.brief_pending_char_budget
                ),
                ledger_max_facts=triage_data.get(
                    "ledger_max_facts", cfg.triage.ledger_max_facts
                ),
                fact_max_chars=triage_data.get(
                    "fact_max_chars", cfg.triage.fact_max_chars
                ),
                device=triage_data.get("device", cfg.triage.device),
                preload=list(triage_data.get("preload", cfg.triage.preload)),
                default_checkpoint=triage_data.get(
                    "default_checkpoint", cfg.triage.default_checkpoint
                ),
                dump_io=triage_data.get("dump_io", cfg.triage.dump_io),
            )

        # Speaker section
        if "speaker" in data:
            speaker_data = data["speaker"]
            provider = speaker_data.get("provider", cfg.speaker.provider)
            provider = provider.strip().lower()
            if provider not in ("cpu", "cuda"):
                logger.warning(
                    "Unknown speaker provider %r; falling back to 'cpu'", provider
                )
                provider = "cpu"
            cfg.speaker = SpeakerConfig(
                enabled=speaker_data.get("enabled", cfg.speaker.enabled),
                model_path=speaker_data.get("model_path", cfg.speaker.model_path),
                provider=provider,
                threshold_id=speaker_data.get("threshold_id", cfg.speaker.threshold_id),
                threshold_new=speaker_data.get("threshold_new", cfg.speaker.threshold_new),
                min_enroll_s=speaker_data.get("min_enroll_s", cfg.speaker.min_enroll_s),
                rms_floor=speaker_data.get("rms_floor", cfg.speaker.rms_floor),
                margin=speaker_data.get("margin", cfg.speaker.margin),
                mint_cooldown_s=speaker_data.get(
                    "mint_cooldown_s", cfg.speaker.mint_cooldown_s
                ),
                confirm_s=speaker_data.get("confirm_s", cfg.speaker.confirm_s),
                max_prototypes=speaker_data.get("max_prototypes", cfg.speaker.max_prototypes),
                provisional_ttl_h=speaker_data.get(
                    "provisional_ttl_h", cfg.speaker.provisional_ttl_h
                ),
                store_dir=speaker_data.get("store_dir", cfg.speaker.store_dir),
                join_timeout_s=speaker_data.get("join_timeout_s", cfg.speaker.join_timeout_s),
            )

        # Brief section
        if "brief" in data:
            brief_data = data["brief"]
            cfg.brief = BriefConfig(
                write=brief_data.get("write", cfg.brief.write),
            )

        # Debug section
        if "debug" in data:
            debug_data = data["debug"]
            cfg.debug = DebugConfig(
                enabled=debug_data.get("enabled", cfg.debug.enabled),
                dir=debug_data.get("dir", cfg.debug.dir),
                heartbeat=debug_data.get("heartbeat", cfg.debug.heartbeat),
                max_bytes_per_day=debug_data.get(
                    "max_bytes_per_day", cfg.debug.max_bytes_per_day
                ),
            )

    return cfg