// AdamW-vs-PyTorch parity, step 1 of 2: build the tiny transformer with a fixed
// deterministic init, then run N steps of the hand-written AdamW on a FIXED
// (input, target) batch — recording the loss at each step and the final
// parameters. tests/adamw_parity.py rebuilds the identical model + torch.optim
// .AdamW with matched hyperparameters and compares the loss trajectory and final
// params within rtol. This is the rigorous correctness check for the optimizer.
//
// Run:  XTRAIN_ADAMW_DIR=/tmp/xtrain_adamw cargo test -p xtrain-train \
//         --test adamw_parity_dump -- --nocapture --ignored
// then: python3 crates/xtrain-train/tests/adamw_parity.py /tmp/xtrain_adamw
//
// Marked #[ignore] (fixture generator) and gated #![cfg(not(no_cuda))].
#![cfg(not(no_cuda))]

use std::fs;
use std::io::Write;
use std::path::PathBuf;

use xtrain_cuda::device;
use xtrain_model::{Config, TinyTransformer, ids_tensor, param_to_host};
use xtrain_optim::AdamW;
use xtrain_tensor::Device;

fn fill(n: usize, seed: u64, scale: f32) -> Vec<f32> {
    let mut state = seed
        .wrapping_mul(2862933555777941757)
        .wrapping_add(3037000493);
    (0..n)
        .map(|_| {
            state = state
                .wrapping_mul(6364136223846793005)
                .wrapping_add(1442695040888963407);
            (((state >> 33) as f32 / (1u64 << 31) as f32) - 0.5) * 2.0 * scale
        })
        .collect()
}

fn write_vec(dir: &PathBuf, name: &str, data: &[f32], shape: &[usize]) {
    let mut f = fs::File::create(dir.join(name)).unwrap();
    let shape_str: Vec<String> = shape.iter().map(|d| d.to_string()).collect();
    writeln!(f, "# shape {}", shape_str.join(",")).unwrap();
    for v in data {
        writeln!(f, "{v:.8e}").unwrap();
    }
}

const LR: f32 = 0.01;
const WD: f32 = 0.1;
// Kept short on purpose: AdamW correctness shows in the per-step loss trajectory
// and the parameter values *while the loss is still well-determined*. Run it long
// enough to memorise the tiny batch and the model enters a flat, overparameterised
// region where many weight configs give the same loss — there f32(GPU) vs the
// torch reference diverge per-weight (large *relative* error on tiny weights)
// while the loss stays identical. 10 steps keeps both signals sharp.
const N_STEPS: usize = 10;

#[test]
#[ignore = "fixture generator for AdamW PyTorch parity; run with --ignored"]
fn dump_adamw_trajectory() {
    assert!(device::device_count().unwrap() > 0, "no CUDA device");
    device::set_device(0).unwrap();
    let device = Device::Cuda(0);

    let dir = PathBuf::from(
        std::env::var("XTRAIN_ADAMW_DIR").unwrap_or_else(|_| "/tmp/xtrain_adamw".to_string()),
    );
    fs::create_dir_all(&dir).unwrap();

    let mut cfg = Config::tiny();
    cfg.vocab = 12;
    let ids: Vec<i32> = vec![3, 1, 4, 1, 5, 9, 2, 6];
    let targets: Vec<i32> = vec![1, 4, 1, 5, 9, 2, 6, 0];

    // Same deterministic init the parity dump uses (so the torch side can reuse it).
    let mut seed = 1u64;
    let model = TinyTransformer::new(cfg, device, |shape| {
        seed = seed.wrapping_add(1);
        let n: usize = shape.iter().product();
        if shape.len() == 1 {
            fill(n, seed, 0.02).iter().map(|v| v + 1.0).collect()
        } else {
            fill(n, seed, 0.08)
        }
    });

    // Dump config + ids + initial params (named for adamw_parity.py).
    {
        let mut f = fs::File::create(dir.join("config.txt")).unwrap();
        writeln!(f, "vocab {}", cfg.vocab).unwrap();
        writeln!(f, "dim {}", cfg.dim).unwrap();
        writeln!(f, "n_layers {}", cfg.n_layers).unwrap();
        writeln!(f, "n_heads {}", cfg.n_heads).unwrap();
        writeln!(f, "head_dim {}", cfg.head_dim).unwrap();
        writeln!(f, "ffn_hidden {}", cfg.ffn_hidden).unwrap();
        writeln!(f, "eps {:e}", cfg.eps).unwrap();
        writeln!(f, "rope_theta {:e}", cfg.rope_theta).unwrap();
        writeln!(f, "lr {LR:e}").unwrap();
        writeln!(f, "wd {WD:e}").unwrap();
        writeln!(f, "n_steps {N_STEPS}").unwrap();
        let mut g = fs::File::create(dir.join("ids.txt")).unwrap();
        for v in &ids {
            writeln!(g, "{v}").unwrap();
        }
        let mut g = fs::File::create(dir.join("targets.txt")).unwrap();
        for v in &targets {
            writeln!(g, "{v}").unwrap();
        }
    }

    let names = param_names(&cfg);
    let params = model.params();
    for (name, p) in names.iter().zip(&params) {
        let shape = p.value().shape().to_vec();
        write_vec(&dir, &format!("w0_{name}.txt"), &param_to_host(p), &shape);
    }

    // Train N steps of AdamW with a CONSTANT lr (no schedule) on the fixed batch.
    let ids_t = ids_tensor(&ids, device);
    let targets_t = ids_tensor(&targets, device);
    let mut opt = AdamW::new(LR, WD);
    let mut losses = Vec::with_capacity(N_STEPS);
    for _ in 0..N_STEPS {
        let loss = model.loss(&ids_t, &targets_t);
        losses.push(param_to_host(&loss)[0]);
        loss.backward();
        opt.step(LR, &params);
        for p in &params {
            p.zero_grad();
        }
    }

    {
        let mut f = fs::File::create(dir.join("losses.txt")).unwrap();
        for l in &losses {
            writeln!(f, "{l:.8e}").unwrap();
        }
    }
    for (name, p) in names.iter().zip(&params) {
        let shape = p.value().shape().to_vec();
        write_vec(&dir, &format!("wN_{name}.txt"), &param_to_host(p), &shape);
    }

    println!(
        "adamw parity: dumped to {} (loss {:.6e} → {:.6e} over {N_STEPS} steps)",
        dir.display(),
        losses.first().unwrap(),
        losses.last().unwrap()
    );
}

fn param_names(cfg: &Config) -> Vec<String> {
    let mut names = vec!["embed".to_string()];
    for l in 0..cfg.n_layers {
        for p in [
            "attn_norm",
            "wq",
            "wk",
            "wv",
            "q_norm",
            "k_norm",
            "wo",
            "ffn_norm",
            "w_gate",
            "w_up",
            "w_down",
        ] {
            names.push(format!("l{l}_{p}"));
        }
    }
    names.push("final_norm".to_string());
    names.push("lm_head".to_string());
    names
}