// jsonl_to_tlog — Convert pruned_memories.jsonl to tlog LMDB format
// Copyright 2026 Joseph Stone — All Rights Reserved
//
// Reads ConsolidatedExample JSONL, converts to TrainingSignal format,
// and writes to LMDB tlog:* keys for spf_transformer_train().
//
// Usage: cargo run --bin jsonl_to_tlog -- <input.jsonl>
//
// Output: LIVE/LMDB5/LMDB5.DB state DB (tlog:* keys)

use anyhow::Result;
use heed::types::*;
use heed::{Database, EnvOpenOptions};
use serde::{Deserialize, Serialize};
use std::fs::File;
use std::io::{BufRead, BufReader};
use std::path::Path;
use std::time::{SystemTime, UNIX_EPOCH};

// ============================================================================
// TrainingSignal — matches gate_training.rs EXACTLY
// ============================================================================

#[derive(Debug, Clone, Deserialize, Serialize)]
pub struct TrainingSignal {
    pub tool: String,
    pub source: String,
    pub allowed: bool,
    pub status: String,
    pub duration_ms: u64,
    pub timestamp: String,
    pub user_override: bool,
    pub false_positive: bool,
    pub recent_call_count: u32,
    pub preceding_tools: Vec<String>,
    #[serde(default)]
    pub evil_score: f32,
}

#[derive(Debug, Clone, Deserialize, Serialize)]
pub struct ConsolidatedExample {
    pub label: i32,
    pub weight: f32,
    pub tool: String,
    pub context: String,
    pub outcome: String,
    pub source_type: String,
    pub category: String,
    pub occurrence_count: u64,
    pub signal_strength: f64,
}

// ============================================================================
// Conversion: ConsolidatedExample → TrainingSignal
// ============================================================================

impl ConsolidatedExample {
    /// Convert to TrainingSignal for FLINT training
    fn to_training_signal(&self) -> TrainingSignal {
        // Map 20-level label to TrainingSignal fields
        let (allowed, user_override, false_positive) = match self.label {
            // Negative labels = blocked
            -10..=-1 => (false, false, self.label <= -3),
            // Positive labels = allowed
            1..=10 => (true, self.label >= 9, false),
            // Should never happen (NO ZERO)
            _ => (true, false, false),
        };

        // Map label to evil_score (higher negative = higher evil)
        let evil_score = if self.label < 0 {
            (-self.label as f32) * 0.07 // -10 → 0.7, -3 → 0.21
        } else {
            0.0
        };

        TrainingSignal {
            tool: self.tool.clone(),
            source: format!("memory:{}", self.category),
            allowed,
            status: if allowed { "allowed" } else { "blocked" }.to_string(),
            duration_ms: (self.signal_strength * 10.0) as u64, // scale signal to duration
            timestamp: format!("{}", SystemTime::now()
                .duration_since(UNIX_EPOCH)
                .unwrap()
                .as_millis()),
            user_override,
            false_positive,
            recent_call_count: self.occurrence_count.min(u32::MAX as u64) as u32,
            preceding_tools: vec![self.category.clone()],
            evil_score,
        }
    }
}

const MAX_DB_SIZE: usize = 1024 * 1024 * 1024; // 1GB

fn main() -> Result<()> {
    // Find input file
    let input_path = std::env::args().nth(1)
        .unwrap_or_else(|| "LIVE/TMP/stoneshell-brain/training_data/raw/pruned_memories.jsonl".to_string());

    let lmdb_path = "/data/data/com.termux/files/home/SPFsmartGATE/LIVE/LMDB5/LMDB5.DB";

    println!("[*] jsonl_to_tlog — JSONL to tlog LMDB converter");
    println!("[*] Input: {}", input_path);
    println!("[*] LMDB: {}", lmdb_path);

    // Open LMDB
    let env = unsafe {
        EnvOpenOptions::new()
            .map_size(MAX_DB_SIZE)
            .max_dbs(8)
            .open(Path::new(lmdb_path))?
    };

    let state_db: Database<Str, SerdeBincode<String>> = env.open_database(&env.read_txn()?, Some("state"))?
        .ok_or_else(|| anyhow::anyhow!("state sub-DB not found"))?;

    // Read JSONL
    let file = File::open(&input_path)?;
    let reader = BufReader::new(file);

    let mut count = 0;
    let mut error_count = 0;
    let now = SystemTime::now().duration_since(UNIX_EPOCH)?.as_millis() as u64;

    for line in reader.lines() {
        let line = line?;
        if line.trim().is_empty() { continue; }

        match serde_json::from_str::<ConsolidatedExample>(&line) {
            Ok(example) => {
                let signal = example.to_training_signal();
                let json = serde_json::to_string(&signal)?;

                // Write to LMDB state DB as tlog: timestamp
                let tlog_key = format!("tlog:{}", now + count);

                let mut wtxn = env.write_txn()?;
                state_db.put(&mut wtxn, &tlog_key, &json)?;
                wtxn.commit()?;

                count += 1;
                if count % 500 == 0 {
                    println!("  Converted: {} entries", count);
                }
            }
            Err(e) => {
                error_count += 1;
                if error_count <= 5 {
                    println!("  Error parsing line {}: {}", count + error_count, e);
                }
            }
        }
    }

    println!("\n[=] Conversion complete");
    println!("  Entries converted: {}", count);
    println!("  Errors: {}", error_count);
    println!("  tlog keys written: tlog:* in {}", lmdb_path);
    println!("\n  Next: Run 'spf_transformer_train()' to train FLINT on these signals");

    Ok(())
}