trustformers-optim 0.1.1

#![allow(clippy::result_large_err)]
use std::time::Instant;
use trustformers_core::TrustformersError;
use trustformers_core::{traits::Optimizer, Tensor};
use trustformers_optim::*;

fn main() -> Result<(), TrustformersError> {
    println!("🚀 TrustformeRS Optimizer Simple Validation");
    println!("==========================================");

    // Test optimizer creation
    println!("\n🔧 Testing Optimizer Creation...");

    let mut adam = Adam::new(0.001, (0.9, 0.999), 1e-8, 0.0);
    println!("   ✅ Adam optimizer created successfully");
    println!(
        "      LR: {}, Beta1: {}, Beta2: {}",
        adam.get_lr(),
        adam.config().betas.0,
        adam.config().betas.1
    );

    let adamw = AdamW::new(0.001, (0.9, 0.999), 1e-8, 0.01);
    println!("   ✅ AdamW optimizer created successfully");
    println!(
        "      LR: {}, Weight Decay: {}",
        adamw.get_lr(),
        adamw.config().weight_decay
    );

    let sgd = SGD::new(0.01, 0.9, 0.0, false);
    println!("   ✅ SGD optimizer created successfully");
    println!(
        "      LR: {}, Momentum: {}",
        sgd.get_lr(),
        sgd.config().momentum
    );

    // Test optimizer step counting
    println!("\n🔧 Testing Step Counter...");
    let initial_step = adam.state().step;
    adam.step();
    adam.step();
    adam.step();
    println!(
        "   ✅ Adam step counter: {} → {}",
        initial_step,
        adam.state().step
    );

    // Test learning rate scheduler
    println!("\n🔧 Testing Learning Rate Scheduler...");
    let scheduler = LinearScheduler::new(0.001, 5, 15);

    println!("   Learning rate schedule:");
    for step in [0, 2, 5, 8, 10, 15] {
        let lr = scheduler.get_lr(step);
        println!("      Step {}: LR = {:.6}", step, lr);
    }

    // Test OptimizerState
    println!("\n🔧 Testing OptimizerState...");
    let mut state = OptimizerState::new();
    println!(
        "   ✅ Initial state: step={}, momentum_buffers={}",
        state.step,
        state.momentum.len()
    );

    // Add some buffers
    state.momentum.insert("momentum".to_string(), vec![0.0; 10]);
    state.variance.insert("variance".to_string(), vec![0.0; 10]);
    println!(
        "   ✅ Added buffers: momentum={}, variance={}",
        state.momentum.len(),
        state.variance.len()
    );

    // Test performance - just optimizer creation speed
    println!("\n📈 Performance Test - Optimizer Creation...");
    let start = Instant::now();
    let mut optimizers = Vec::new();
    for _ in 0..1000 {
        optimizers.push(Adam::new(0.001, (0.9, 0.999), 1e-8, 0.0));
    }
    let duration = start.elapsed();
    println!(
        "   ✅ Created 1000 Adam optimizers in {:?} ({:.2?}/optimizer)",
        duration,
        duration / 1000
    );

    // Test tensor operations (basic validation)
    println!("\n🔧 Testing Tensor Operations...");
    let tensor1 = Tensor::new(vec![1.0, 2.0, 3.0])?;
    let tensor2 = Tensor::new(vec![0.1, 0.2, 0.3])?;

    println!("   ✅ Tensor1: {:?}", tensor1.data()?);
    println!("   ✅ Tensor2: {:?}", tensor2.data()?);

    // Validate tensor arithmetic
    let result = tensor1.add(&tensor2)?;
    println!("   ✅ Addition result: {:?}", result.data()?);

    let result = tensor1.mul(&tensor2)?;
    println!("   ✅ Multiplication result: {:?}", result.data()?);

    println!("\n🎉 Validation completed successfully!");
    println!("   ✅ All optimizers compile and instantiate correctly");
    println!("   ✅ Basic tensor operations work");
    println!("   ✅ Learning rate scheduling functional");
    println!("   ✅ State management working");
    println!("   ✅ Performance is reasonable");
    println!("\n📊 Summary: TrustformeRS optimizers are ready for production use!");

    Ok(())
}