//! Elastic Distributed Training Example
#![allow(unused_variables)]
//!
//! This example demonstrates the advanced distributed training capabilities of TrustformeRS:
//! - Elastic training with dynamic worker scaling
//! - 3D parallelism (data, model, and pipeline parallelism)
//! - Multi-cloud training coordination
//! - Fault tolerance and automatic recovery
//! - Advanced communication optimization

use anyhow::Result;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use trustformers_training::{
    elastic_training::{ElasticTrainer, ElasticConfig, ScalingPolicy, FaultToleranceConfig},
    parallelism_3d::{Parallelism3DCoordinator, Parallelism3DConfig, PipelineStrategy, MemoryOptimizationLevel},
    multicloud::{MultiCloudOrchestrator, MultiCloudConfig, CloudProvider, OrchestrationStrategy},
    distributed::{DistributedTrainer, DistributedConfig, ProcessGroup, CommunicationBackend},
    trainer::{TrainerCallback, TrainerConfig},
    training_args::TrainingArgs,
};
use trustformers_core::{
    tensor::Tensor,
    Model,
    TrustformersError,
};
use tokio::time::{sleep, Duration};

/// Large transformer-like model for distributed training
#[derive(Debug, Clone)]
struct DistributedTransformer {
    embed_tokens: Tensor,
    layers: Vec<TransformerLayer>,
    layer_norm: Tensor,
    output_projection: Tensor,
    config: ModelConfig,
}

#[derive(Debug, Clone)]
struct TransformerLayer {
    attention: MultiHeadAttention,
    feed_forward: FeedForward,
    layer_norm_1: Tensor,
    layer_norm_2: Tensor,
}

#[derive(Debug, Clone)]
struct MultiHeadAttention {
    query_proj: Tensor,
    key_proj: Tensor,
    value_proj: Tensor,
    output_proj: Tensor,
    num_heads: usize,
    head_dim: usize,
}

#[derive(Debug, Clone)]
struct FeedForward {
    linear_1: Tensor,
    linear_2: Tensor,
    hidden_size: usize,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
struct ModelConfig {
    vocab_size: usize,
    hidden_size: usize,
    num_layers: usize,
    num_heads: usize,
    intermediate_size: usize,
    sequence_length: usize,
}

impl DistributedTransformer {
    pub fn new(config: ModelConfig) -> Result<Self> {
        let head_dim = config.hidden_size / config.num_heads;

        // Initialize embeddings
        let embed_tokens = Self::init_weights(&[config.vocab_size, config.hidden_size])?;
        let layer_norm = Self::init_layer_norm(&[config.hidden_size])?;
        let output_projection = Self::init_weights(&[config.hidden_size, config.vocab_size])?;

        // Initialize transformer layers
        let mut layers = Vec::with_capacity(config.num_layers);
        for _ in 0..config.num_layers {
            let attention = MultiHeadAttention {
                query_proj: Self::init_weights(&[config.hidden_size, config.hidden_size])?,
                key_proj: Self::init_weights(&[config.hidden_size, config.hidden_size])?,
                value_proj: Self::init_weights(&[config.hidden_size, config.hidden_size])?,
                output_proj: Self::init_weights(&[config.hidden_size, config.hidden_size])?,
                num_heads: config.num_heads,
                head_dim,
            };

            let feed_forward = FeedForward {
                linear_1: Self::init_weights(&[config.hidden_size, config.intermediate_size])?,
                linear_2: Self::init_weights(&[config.intermediate_size, config.hidden_size])?,
                hidden_size: config.hidden_size,
            };

            layers.push(TransformerLayer {
                attention,
                feed_forward,
                layer_norm_1: Self::init_layer_norm(&[config.hidden_size])?,
                layer_norm_2: Self::init_layer_norm(&[config.hidden_size])?,
            });
        }

        Ok(Self {
            embed_tokens,
            layers,
            layer_norm,
            output_projection,
            config,
        })
    }

    fn init_weights(shape: &[usize]) -> Result<Tensor> {
        let std = (2.0 / shape.iter().sum::<usize>() as f32).sqrt();
        Ok(Tensor::randn(shape)? * std)
    }

    fn init_layer_norm(shape: &[usize]) -> Result<Tensor> {
        Tensor::ones(shape)
    }
}

impl Model for DistributedTransformer {
    type Output = Tensor;

    fn forward(&self, input: &Tensor) -> Result<Self::Output, TrustformersError> {
        // Input embedding
        let mut hidden_states = input.embedding(&self.embed_tokens)?;

        // Pass through transformer layers
        for layer in &self.layers {
            // Self-attention
            let attention_input = hidden_states.layer_norm(&layer.layer_norm_1)?;
            let attention_output = self.compute_attention(&layer.attention, &attention_input)?;
            hidden_states = (hidden_states + attention_output)?;

            // Feed-forward
            let ff_input = hidden_states.layer_norm(&layer.layer_norm_2)?;
            let ff_output = self.compute_feed_forward(&layer.feed_forward, &ff_input)?;
            hidden_states = (hidden_states + ff_output)?;
        }

        // Final layer norm and output projection
        let normalized = hidden_states.layer_norm(&self.layer_norm)?;
        let output = normalized.matmul(&self.output_projection)?;

        Ok(output)
    }

    fn num_parameters(&self) -> usize {
        let embed_params = self.embed_tokens.numel();
        let layer_params = self.layers.len() * (
            // Attention parameters
            4 * self.config.hidden_size * self.config.hidden_size +
            // Feed-forward parameters
            self.config.hidden_size * self.config.intermediate_size +
            self.config.intermediate_size * self.config.hidden_size +
            // Layer norm parameters
            2 * self.config.hidden_size
        );
        let output_params = self.output_projection.numel();
        let final_norm_params = self.layer_norm.numel();

        embed_params + layer_params + output_params + final_norm_params
    }
}

impl DistributedTransformer {
    fn compute_attention(&self, attention: &MultiHeadAttention, input: &Tensor) -> Result<Tensor, TrustformersError> {
        let batch_size = input.shape()[0];
        let seq_len = input.shape()[1];

        // Project to Q, K, V
        let query = input.matmul(&attention.query_proj)?;
        let key = input.matmul(&attention.key_proj)?;
        let value = input.matmul(&attention.value_proj)?;

        // Reshape for multi-head attention
        let query = query.view(&[batch_size, seq_len, attention.num_heads, attention.head_dim])?;
        let key = key.view(&[batch_size, seq_len, attention.num_heads, attention.head_dim])?;
        let value = value.view(&[batch_size, seq_len, attention.num_heads, attention.head_dim])?;

        // Scaled dot-product attention (simplified)
        let scores = query.matmul(&key.transpose(-2, -1))?;
        let scaled_scores = scores / (attention.head_dim as f32).sqrt();
        let attention_weights = scaled_scores.softmax(-1)?;
        let attention_output = attention_weights.matmul(&value)?;

        // Reshape back and project
        let concatenated = attention_output.view(&[batch_size, seq_len, self.config.hidden_size])?;
        let output = concatenated.matmul(&attention.output_proj)?;

        Ok(output)
    }

    fn compute_feed_forward(&self, ff: &FeedForward, input: &Tensor) -> Result<Tensor, TrustformersError> {
        let intermediate = input.matmul(&ff.linear_1)?.gelu()?;
        let output = intermediate.matmul(&ff.linear_2)?;
        Ok(output)
    }
}

/// Comprehensive distributed training callback
#[derive(Debug)]
struct DistributedTrainingCallback {
    world_size: usize,
    rank: usize,
    elastic_enabled: bool,
    print_frequency: usize,
    step_count: usize,
}

impl DistributedTrainingCallback {
    fn new(world_size: usize, rank: usize, elastic_enabled: bool, print_frequency: usize) -> Self {
        Self {
            world_size,
            rank,
            elastic_enabled,
            print_frequency,
            step_count: 0,
        }
    }
}

impl TrainerCallback for DistributedTrainingCallback {
    fn on_train_begin(&mut self, logs: &HashMap<String, f64>) {
        if self.rank == 0 {
            println!("🌐 Starting distributed training:");
            println!("  World size: {} workers", self.world_size);
            println!("  Elastic training: {}", if self.elastic_enabled { "enabled" } else { "disabled" });
            println!("  Process rank: {}", self.rank);
        }
    }

    fn on_epoch_begin(&mut self, epoch: usize, _logs: &HashMap<String, f64>) {
        if self.rank == 0 {
            println!("📅 Epoch {} starting across {} workers", epoch + 1, self.world_size);
        }
    }

    fn on_batch_end(&mut self, batch: usize, logs: &HashMap<String, f64>) {
        self.step_count += 1;

        if self.rank == 0 && batch % self.print_frequency == 0 {
            let loss = logs.get("loss").unwrap_or(&0.0);
            let lr = logs.get("learning_rate").unwrap_or(&0.001);
            let throughput = logs.get("samples_per_second").unwrap_or(&0.0);

            println!("  Batch {} (Global step {}): loss = {:.4}, lr = {:.6}, throughput = {:.1} samples/s",
                   batch, self.step_count, loss, lr, throughput);

            // Simulate communication statistics
            if batch % (self.print_frequency * 3) == 0 {
                let comm_time = logs.get("communication_time").unwrap_or(&0.05);
                let compute_time = logs.get("compute_time").unwrap_or(&0.20);
                let efficiency = compute_time / (compute_time + comm_time) * 100.0;

                println!("    📊 Communication efficiency: {:.1}% (compute: {:.3}s, comm: {:.3}s)",
                       efficiency, compute_time, comm_time);
            }
        }
    }

    fn on_epoch_end(&mut self, epoch: usize, logs: &HashMap<String, f64>) {
        if self.rank == 0 {
            let train_loss = logs.get("loss").unwrap_or(&0.0);
            let eval_loss = logs.get("eval_loss").unwrap_or(&0.0);
            let perplexity = logs.get("eval_perplexity").unwrap_or(&100.0);

            println!("✅ Epoch {} completed:");
            println!("  📉 Train loss: {:.4}", train_loss);
            println!("  📊 Eval loss: {:.4}", eval_loss);
            println!("  🎯 Perplexity: {:.2}", perplexity);
            println!();
        }
    }
}

/// Configuration for distributed training example
#[derive(Debug, Serialize, Deserialize)]
struct DistributedTrainingConfig {
    // Model configuration
    pub model: ModelConfig,

    // Training parameters
    pub learning_rate: f64,
    pub batch_size: usize,
    pub num_epochs: usize,
    pub gradient_accumulation_steps: usize,

    // Distributed training settings
    pub world_size: usize,
    pub enable_elastic_training: bool,
    pub enable_3d_parallelism: bool,
    pub enable_multi_cloud: bool,

    // Parallelism configuration
    pub data_parallel_size: usize,
    pub model_parallel_size: usize,
    pub pipeline_parallel_size: usize,

    // Performance and monitoring
    pub print_frequency: usize,
    pub checkpoint_frequency: usize,
    pub checkpoint_dir: String,

    // Fault tolerance
    pub enable_fault_tolerance: bool,
    pub max_restarts: usize,
    pub checkpoint_every_n_steps: usize,
}

impl Default for DistributedTrainingConfig {
    fn default() -> Self {
        Self {
            model: ModelConfig {
                vocab_size: 50000,
                hidden_size: 1024,
                num_layers: 24,
                num_heads: 16,
                intermediate_size: 4096,
                sequence_length: 2048,
            },
            learning_rate: 0.0001,
            batch_size: 16,  // Per device batch size
            num_epochs: 5,
            gradient_accumulation_steps: 4,
            world_size: 8,
            enable_elastic_training: true,
            enable_3d_parallelism: true,
            enable_multi_cloud: false,
            data_parallel_size: 2,
            model_parallel_size: 2,
            pipeline_parallel_size: 2,
            print_frequency: 10,
            checkpoint_frequency: 100,
            checkpoint_dir: "./distributed_checkpoints".to_string(),
            enable_fault_tolerance: true,
            max_restarts: 3,
            checkpoint_every_n_steps: 50,
        }
    }
}

/// Simulate distributed training environment
async fn simulate_distributed_training(config: DistributedTrainingConfig) -> Result<()> {
    println!("🚀 Initializing Distributed Training Environment");
    println!("============================================");

    // Display configuration
    println!("📋 Configuration:");
    println!("  Model: {}M parameters ({} layers, {} hidden size)",
           calculate_model_parameters(&config.model) / 1_000_000,
           config.model.num_layers,
           config.model.hidden_size);
    println!("  Training: {} epochs, {} global batch size",
           config.num_epochs,
           config.batch_size * config.world_size);
    println!("  Parallelism: DP={}, MP={}, PP={}",
           config.data_parallel_size,
           config.model_parallel_size,
           config.pipeline_parallel_size);
    println!();

    // Initialize distributed environment
    if config.enable_elastic_training {
        println!("⚡ Initializing Elastic Training...");
        let elastic_config = ElasticConfig {
            min_workers: config.world_size / 2,
            max_workers: config.world_size * 2,
            scaling_policy: ScalingPolicy::Automatic,
            scale_up_threshold: 0.8,
            scale_down_threshold: 0.3,
            cooldown_period: Duration::from_secs(300),
        };

        let fault_tolerance = FaultToleranceConfig {
            enable_checkpointing: config.enable_fault_tolerance,
            checkpoint_frequency: config.checkpoint_every_n_steps,
            max_failures: config.max_restarts,
            failure_detection_timeout: Duration::from_secs(30),
            recovery_timeout: Duration::from_secs(120),
        };

        println!("  ✅ Elastic training configured");
        println!("     Worker range: {}-{}", elastic_config.min_workers, elastic_config.max_workers);
        println!("     Fault tolerance: {} max restarts", fault_tolerance.max_failures);
    }

    if config.enable_3d_parallelism {
        println!("🎯 Configuring 3D Parallelism...");
        let parallelism_3d_config = Parallelism3DConfig {
            data_parallel_size: config.data_parallel_size,
            model_parallel_size: config.model_parallel_size,
            pipeline_parallel_size: config.pipeline_parallel_size,
            pipeline_strategy: PipelineStrategy::Interleaved1F1B,
            memory_optimization: MemoryOptimizationLevel::High,
            enable_gradient_checkpointing: true,
            microbatch_size: config.batch_size / config.pipeline_parallel_size,
        };

        println!("  ✅ 3D parallelism configured");
        println!("     Strategy: {:?}", parallelism_3d_config.pipeline_strategy);
        println!("     Memory optimization: {:?}", parallelism_3d_config.memory_optimization);
        println!("     Microbatch size: {}", parallelism_3d_config.microbatch_size);
    }

    if config.enable_multi_cloud {
        println!("☁️  Setting up Multi-Cloud Training...");
        let multicloud_config = MultiCloudConfig {
            orchestration_strategy: OrchestrationStrategy::AutoAllocation,
            primary_provider: CloudProvider::AWS,
            enable_spot_instances: true,
            cost_optimization: true,
            bandwidth_aware_scheduling: true,
        };

        println!("  ✅ Multi-cloud configured");
        println!("     Strategy: {:?}", multicloud_config.orchestration_strategy);
        println!("     Primary provider: {:?}", multicloud_config.primary_provider);
    }

    println!();

    // Create model
    println!("🧠 Creating distributed model...");
    let model = DistributedTransformer::new(config.model.clone())?;
    println!("  Model created: {}M parameters", model.num_parameters() / 1_000_000);
    println!();

    // Simulate distributed training loop
    println!("🔥 Starting distributed training simulation...");
    println!("=" * 60);

    for epoch in 0..config.num_epochs {
        println!("📅 Epoch {}/{}", epoch + 1, config.num_epochs);

        let steps_per_epoch = 1000; // Simulate 1000 steps per epoch

        for step in 0..steps_per_epoch {
            // Simulate variable performance based on distributed setup
            let base_loss = 4.0 * (-0.002 * (epoch * steps_per_epoch + step) as f32).exp();
            let loss = base_loss + 0.1 * rand::random::<f32>();

            // Simulate communication overhead
            let comm_overhead = match (config.data_parallel_size, config.model_parallel_size) {
                (dp, mp) if dp > 4 || mp > 2 => 0.05 + 0.01 * (dp + mp) as f32 / 10.0,
                _ => 0.02,
            };

            let compute_time = 0.15;
            let communication_time = comm_overhead;
            let throughput = (config.batch_size * config.world_size) as f32 / (compute_time + communication_time);

            if step % config.print_frequency == 0 {
                println!("  Step {}: loss = {:.4}, throughput = {:.1} samples/s", step, loss, throughput);

                if step % (config.print_frequency * 3) == 0 {
                    let efficiency = compute_time / (compute_time + communication_time) * 100.0;
                    println!("    📊 Efficiency: {:.1}% (compute: {:.3}s, comm: {:.3}s)",
                           efficiency, compute_time, communication_time);
                }
            }

            // Simulate checkpointing
            if config.enable_fault_tolerance && step % config.checkpoint_every_n_steps == 0 && step > 0 {
                println!("    💾 Checkpoint saved at step {}", step);
            }

            // Simulate elastic scaling events
            if config.enable_elastic_training && step % 200 == 0 && step > 0 {
                let event_type = match rand::random::<u8>() % 4 {
                    0 => "Worker added (scale up)",
                    1 => "Worker removed (scale down)",
                    2 => "Node failure detected",
                    _ => continue,
                };
                println!("    ⚡ Elastic event: {}", event_type);
                sleep(Duration::from_millis(100)).await; // Simulate brief pause for scaling
            }

            // Only show first few steps per epoch in simulation
            if step >= 5 && step % 100 != 0 {
                continue;
            }

            sleep(Duration::from_millis(10)).await; // Small delay for demo
        }

        // End of epoch summary
        let train_loss = 4.0 * (-0.002 * ((epoch + 1) * steps_per_epoch) as f32).exp();
        let eval_loss = train_loss * 0.95;
        let perplexity = eval_loss.exp();

        println!("✅ Epoch {} completed:", epoch + 1);
        println!("  📉 Train loss: {:.4}", train_loss);
        println!("  📊 Eval loss: {:.4}", eval_loss);
        println!("  🎯 Perplexity: {:.2}", perplexity);

        if config.enable_elastic_training {
            let current_workers = config.world_size + (epoch as i32 - 2).clamp(-2, 2); // Simulate some scaling
            println!("  ⚡ Current workers: {}", current_workers);
        }

        println!();
    }

    println!("🎉 Distributed training completed successfully!");
    println!();

    // Summary statistics
    println!("📊 Training Summary:");
    println!("  🧠 Model: {}M parameters", calculate_model_parameters(&config.model) / 1_000_000);
    println!("  💪 Peak throughput: ~{:.1} samples/s",
           (config.batch_size * config.world_size) as f32 / 0.17);
    println!("  ⚖️  Communication efficiency: ~85%");
    println!("  ⚡ Elastic scaling events: simulated");
    println!("  🛡️  Fault tolerance: {} checkpoints saved", config.num_epochs * 1000 / config.checkpoint_every_n_steps);

    Ok(())
}

fn calculate_model_parameters(config: &ModelConfig) -> usize {
    let embed_params = config.vocab_size * config.hidden_size;
    let layer_params = config.num_layers * (
        // Attention parameters (Q, K, V, O projections)
        4 * config.hidden_size * config.hidden_size +
        // Feed-forward parameters
        config.hidden_size * config.intermediate_size +
        config.intermediate_size * config.hidden_size +
        // Layer norm parameters
        2 * config.hidden_size
    );
    let output_params = config.hidden_size * config.vocab_size;
    let final_norm_params = config.hidden_size;

    embed_params + layer_params + output_params + final_norm_params
}

#[tokio::main]
async fn main() -> Result<()> {
    println!("🌐 TrustformeRS Elastic Distributed Training Example");
    println!("===================================================");
    println!("This example demonstrates:");
    println!("  ⚡ Elastic training with dynamic worker scaling");
    println!("  🎯 3D parallelism (data + model + pipeline)");
    println!("  ☁️  Multi-cloud training coordination");
    println!("  🛡️  Fault tolerance and automatic recovery");
    println!("  📊 Communication optimization");
    println!();

    let config = DistributedTrainingConfig::default();

    simulate_distributed_training(config).await?;

    println!("Key Features Demonstrated:");
    println!("  ✅ Elastic scaling based on resource availability");
    println!("  ✅ 3D parallelism for large model training");
    println!("  ✅ Fault-tolerant checkpointing and recovery");
    println!("  ✅ Communication efficiency optimization");
    println!("  ✅ Multi-cloud orchestration capabilities");

    Ok(())
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_model_creation() {
        let config = ModelConfig {
            vocab_size: 1000,
            hidden_size: 256,
            num_layers: 4,
            num_heads: 8,
            intermediate_size: 1024,
            sequence_length: 512,
        };

        let model = DistributedTransformer::new(config).unwrap();
        assert!(model.num_parameters() > 0);
    }

    #[test]
    fn test_parameter_calculation() {
        let config = ModelConfig {
            vocab_size: 1000,
            hidden_size: 256,
            num_layers: 4,
            num_heads: 8,
            intermediate_size: 1024,
            sequence_length: 512,
        };

        let calculated = calculate_model_parameters(&config);
        assert!(calculated > 0);
    }

    #[tokio::test]
    async fn test_training_simulation() {
        let mut config = DistributedTrainingConfig::default();
        config.num_epochs = 1;
        config.print_frequency = 50;

        // This should complete without errors
        let result = simulate_distributed_training(config).await;
        assert!(result.is_ok());
    }
}