//! Transformer model benchmarks
//!
//! Benchmarks for transformer architectures including:
//! - BERT (encoder-only)
//! - GPT (decoder-only)
//! - T5 (encoder-decoder)
//! - Different model sizes (base, large, XL)
//! - Sequence length scaling
//! - Batch size optimization
//! - Attention mechanism performance

use crate::{BenchmarkConfig, BenchmarkMeasurement, time_benchmark_async};
use anyhow::{Context, Result};
use std::collections::HashMap;
use tracing::info;

// Placeholder types for testing
type TestModel = String;
type TestAttentionLayer = String;
type TestMultiheadAttention = String;

/// Transformer model benchmarks
pub struct TransformerBenchmarks;

/// Model configuration for benchmarks
#[derive(Debug, Clone)]
pub struct ModelConfig {
    pub model_type: ModelType,
    pub hidden_size: usize,
    pub num_layers: usize,
    pub num_heads: usize,
    pub sequence_length: usize,
    pub vocab_size: usize,
}

#[derive(Debug, Clone)]
pub enum ModelType {
    Bert,
    Gpt,
    T5,
}

impl TransformerBenchmarks {
    /// Create new transformer benchmarks
    pub fn new() -> Self {
        Self
    }

    /// Run all transformer benchmarks
    pub async fn run_all_benchmarks(
        &self,
        config: &BenchmarkConfig,
    ) -> Result<HashMap<String, Vec<BenchmarkMeasurement>>> {
        let mut all_measurements = HashMap::new();

        // Standard model configurations
        let model_configs = self.get_standard_model_configs();

        for model_config in model_configs {
            let model_name = format!(
                "{:?}_{}L_{}H",
                model_config.model_type, model_config.num_layers, model_config.hidden_size
            );

            info!("Running benchmarks for {}", model_name);

            // Forward pass benchmarks
            all_measurements.extend(
                self.run_forward_pass_benchmarks(config, &model_config)
                    .await
                    .with_context(|| format!("Failed forward pass benchmarks for {model_name}"))?,
            );

            // Attention mechanism benchmarks
            all_measurements.extend(
                self.run_attention_benchmarks(config, &model_config)
                    .await
                    .with_context(|| format!("Failed attention benchmarks for {model_name}"))?,
            );

            // Batch size scaling
            all_measurements.extend(
                self.run_batch_scaling_benchmarks(config, &model_config)
                    .await
                    .with_context(|| format!("Failed batch scaling benchmarks for {model_name}"))?,
            );

            // Sequence length scaling
            all_measurements.extend(
                self.run_sequence_scaling_benchmarks(config, &model_config)
                    .await
                    .with_context(|| {
                        format!("Failed sequence scaling benchmarks for {model_name}")
                    })?,
            );

            // Training step benchmarks
            all_measurements.extend(
                self.run_training_step_benchmarks(config, &model_config)
                    .await
                    .with_context(|| format!("Failed training step benchmarks for {model_name}"))?,
            );
        }

        Ok(all_measurements)
    }

    /// Get standard model configurations for benchmarking
    fn get_standard_model_configs(&self) -> Vec<ModelConfig> {
        vec![
            // BERT configurations
            ModelConfig {
                model_type: ModelType::Bert,
                hidden_size: 768,
                num_layers: 12,
                num_heads: 12,
                sequence_length: 512,
                vocab_size: 30522,
            },
            ModelConfig {
                model_type: ModelType::Bert,
                hidden_size: 1024,
                num_layers: 24,
                num_heads: 16,
                sequence_length: 512,
                vocab_size: 30522,
            },
            // GPT configurations
            ModelConfig {
                model_type: ModelType::Gpt,
                hidden_size: 768,
                num_layers: 12,
                num_heads: 12,
                sequence_length: 1024,
                vocab_size: 50257,
            },
            ModelConfig {
                model_type: ModelType::Gpt,
                hidden_size: 1536,
                num_layers: 48,
                num_heads: 25,
                sequence_length: 1024,
                vocab_size: 50257,
            },
            // T5 configurations
            ModelConfig {
                model_type: ModelType::T5,
                hidden_size: 768,
                num_layers: 12,
                num_heads: 12,
                sequence_length: 512,
                vocab_size: 32128,
            },
        ]
    }

    /// Run forward pass benchmarks
    async fn run_forward_pass_benchmarks(
        &self,
        config: &BenchmarkConfig,
        model_config: &ModelConfig,
    ) -> Result<HashMap<String, Vec<BenchmarkMeasurement>>> {
        let mut measurements = HashMap::new();

        let batch_sizes = vec![1, 8, 16, 32];

        for batch_size in batch_sizes {
            let benchmark_name = format!(
                "{:?}_forward_b{}_s{}",
                model_config.model_type, batch_size, model_config.sequence_length
            );

            measurements.insert(
                benchmark_name.clone(),
                self.benchmark_forward_pass(config, model_config, batch_size)
                    .await
                    .with_context(|| format!("Failed forward pass benchmark: {benchmark_name}"))?,
            );
        }

        Ok(measurements)
    }

    /// Run attention mechanism benchmarks
    async fn run_attention_benchmarks(
        &self,
        config: &BenchmarkConfig,
        model_config: &ModelConfig,
    ) -> Result<HashMap<String, Vec<BenchmarkMeasurement>>> {
        let mut measurements = HashMap::new();

        // Single attention layer performance
        let benchmark_name = format!("{:?}_attention_single", model_config.model_type);
        measurements.insert(
            benchmark_name.clone(),
            self.benchmark_attention_layer(config, model_config)
                .await
                .with_context(|| format!("Failed attention benchmark: {benchmark_name}"))?,
        );

        // Multi-head attention scaling
        let benchmark_name = format!("{:?}_attention_multihead", model_config.model_type);
        measurements.insert(
            benchmark_name.clone(),
            self.benchmark_multihead_attention(config, model_config)
                .await
                .with_context(|| {
                    format!("Failed multi-head attention benchmark: {benchmark_name}")
                })?,
        );

        Ok(measurements)
    }

    /// Run batch size scaling benchmarks
    async fn run_batch_scaling_benchmarks(
        &self,
        config: &BenchmarkConfig,
        model_config: &ModelConfig,
    ) -> Result<HashMap<String, Vec<BenchmarkMeasurement>>> {
        let mut measurements = HashMap::new();

        let batch_sizes = vec![1, 2, 4, 8, 16, 32, 64];

        for batch_size in batch_sizes {
            let benchmark_name = format!(
                "{:?}_batch_scaling_b{}",
                model_config.model_type, batch_size
            );

            measurements.insert(
                benchmark_name.clone(),
                self.benchmark_batch_scaling(config, model_config, batch_size)
                    .await
                    .with_context(|| format!("Failed batch scaling benchmark: {benchmark_name}"))?,
            );
        }

        Ok(measurements)
    }

    /// Run sequence length scaling benchmarks
    async fn run_sequence_scaling_benchmarks(
        &self,
        config: &BenchmarkConfig,
        model_config: &ModelConfig,
    ) -> Result<HashMap<String, Vec<BenchmarkMeasurement>>> {
        let mut measurements = HashMap::new();

        let sequence_lengths = vec![128, 256, 512, 1024, 2048];

        for seq_len in sequence_lengths {
            // Skip if sequence length is too large for the model
            if seq_len > model_config.sequence_length * 2 {
                continue;
            }

            let benchmark_name = format!(
                "{:?}_sequence_scaling_s{}",
                model_config.model_type, seq_len
            );

            measurements.insert(
                benchmark_name.clone(),
                self.benchmark_sequence_scaling(config, model_config, seq_len)
                    .await
                    .with_context(|| {
                        format!("Failed sequence scaling benchmark: {benchmark_name}")
                    })?,
            );
        }

        Ok(measurements)
    }

    /// Run training step benchmarks
    async fn run_training_step_benchmarks(
        &self,
        config: &BenchmarkConfig,
        model_config: &ModelConfig,
    ) -> Result<HashMap<String, Vec<BenchmarkMeasurement>>> {
        let mut measurements = HashMap::new();

        let benchmark_name = format!("{:?}_training_step", model_config.model_type);
        measurements.insert(
            benchmark_name.clone(),
            self.benchmark_training_step(config, model_config)
                .await
                .with_context(|| format!("Failed training step benchmark: {benchmark_name}"))?,
        );

        Ok(measurements)
    }

    /// Individual benchmark implementations

    async fn benchmark_forward_pass(
        &self,
        config: &BenchmarkConfig,
        model_config: &ModelConfig,
        batch_size: usize,
    ) -> Result<Vec<BenchmarkMeasurement>> {
        let mut measurements = Vec::with_capacity(config.measurement_iterations);

        // Create model (placeholder)
        let model = self.create_model(model_config).await?;

        // Warmup
        for _ in 0..config.warmup_iterations {
            let _output = self
                .forward_pass(&model, batch_size, model_config.sequence_length)
                .await?;
        }

        // Measurements
        for _ in 0..config.measurement_iterations {
            let measurement = time_benchmark_async(
                format!("{:?}_forward_b{}", model_config.model_type, batch_size),
                || async {
                    let _output = self
                        .forward_pass(&model, batch_size, model_config.sequence_length)
                        .await?;
                    Ok(())
                },
            )
            .await?;

            // Estimate memory usage based on model size and batch size
            let estimated_memory = self.estimate_memory_usage(model_config, batch_size);
            let enhanced_measurement =
                measurement.with_memory_usage(estimated_memory, Some(estimated_memory));

            measurements.push(enhanced_measurement);
        }

        Ok(measurements)
    }

    async fn benchmark_attention_layer(
        &self,
        config: &BenchmarkConfig,
        model_config: &ModelConfig,
    ) -> Result<Vec<BenchmarkMeasurement>> {
        let mut measurements = Vec::with_capacity(config.measurement_iterations);

        let attention_layer = self.create_attention_layer(model_config).await?;
        let batch_size = 8;

        for _ in 0..config.warmup_iterations {
            let _output = self
                .attention_forward(&attention_layer, batch_size, model_config.sequence_length)
                .await?;
        }

        for _ in 0..config.measurement_iterations {
            let measurement = time_benchmark_async(
                format!("{:?}_attention", model_config.model_type),
                || async {
                    let _output = self
                        .attention_forward(
                            &attention_layer,
                            batch_size,
                            model_config.sequence_length,
                        )
                        .await?;
                    Ok(())
                },
            )
            .await?;

            measurements.push(measurement);
        }

        Ok(measurements)
    }

    async fn benchmark_multihead_attention(
        &self,
        config: &BenchmarkConfig,
        model_config: &ModelConfig,
    ) -> Result<Vec<BenchmarkMeasurement>> {
        let mut measurements = Vec::with_capacity(config.measurement_iterations);

        let mha_layer = self.create_multihead_attention(model_config).await?;
        let batch_size = 8;

        for _ in 0..config.warmup_iterations {
            let _output = self
                .multihead_attention_forward(&mha_layer, batch_size, model_config.sequence_length)
                .await?;
        }

        for _ in 0..config.measurement_iterations {
            let measurement = time_benchmark_async(
                format!("{:?}_multihead_attention", model_config.model_type),
                || async {
                    let _output = self
                        .multihead_attention_forward(
                            &mha_layer,
                            batch_size,
                            model_config.sequence_length,
                        )
                        .await?;
                    Ok(())
                },
            )
            .await?;

            measurements.push(measurement);
        }

        Ok(measurements)
    }

    async fn benchmark_batch_scaling(
        &self,
        config: &BenchmarkConfig,
        model_config: &ModelConfig,
        batch_size: usize,
    ) -> Result<Vec<BenchmarkMeasurement>> {
        let mut measurements = Vec::with_capacity(config.measurement_iterations);

        let model = self.create_model(model_config).await?;

        for _ in 0..config.warmup_iterations {
            let _output = self
                .forward_pass(&model, batch_size, model_config.sequence_length)
                .await?;
        }

        for _ in 0..config.measurement_iterations {
            let measurement = time_benchmark_async(
                format!("{:?}_batch_{}", model_config.model_type, batch_size),
                || async {
                    let _output = self
                        .forward_pass(&model, batch_size, model_config.sequence_length)
                        .await?;
                    Ok(())
                },
            )
            .await?;

            let memory_usage = self.estimate_memory_usage(model_config, batch_size);
            let enhanced_measurement =
                measurement.with_memory_usage(memory_usage, Some(memory_usage));

            measurements.push(enhanced_measurement);
        }

        Ok(measurements)
    }

    async fn benchmark_sequence_scaling(
        &self,
        config: &BenchmarkConfig,
        model_config: &ModelConfig,
        sequence_length: usize,
    ) -> Result<Vec<BenchmarkMeasurement>> {
        let mut measurements = Vec::with_capacity(config.measurement_iterations);

        let model = self.create_model(model_config).await?;
        let batch_size = 8;

        for _ in 0..config.warmup_iterations {
            let _output = self
                .forward_pass(&model, batch_size, sequence_length)
                .await?;
        }

        for _ in 0..config.measurement_iterations {
            let measurement = time_benchmark_async(
                format!("{:?}_seq_{}", model_config.model_type, sequence_length),
                || async {
                    let _output = self
                        .forward_pass(&model, batch_size, sequence_length)
                        .await?;
                    Ok(())
                },
            )
            .await?;

            measurements.push(measurement);
        }

        Ok(measurements)
    }

    async fn benchmark_training_step(
        &self,
        config: &BenchmarkConfig,
        model_config: &ModelConfig,
    ) -> Result<Vec<BenchmarkMeasurement>> {
        let mut measurements = Vec::with_capacity(config.measurement_iterations);

        let model = self.create_model(model_config).await?;
        let batch_size = 16;

        for _ in 0..config.warmup_iterations {
            self.training_step(&model, batch_size, model_config.sequence_length)
                .await?;
        }

        for _ in 0..config.measurement_iterations {
            let measurement = time_benchmark_async(
                format!("{:?}_training_step", model_config.model_type),
                || async {
                    self.training_step(&model, batch_size, model_config.sequence_length)
                        .await?;
                    Ok(())
                },
            )
            .await?;

            // Training uses more memory due to gradients
            let memory_usage = self.estimate_memory_usage(model_config, batch_size) * 3; // Rough estimate
            let enhanced_measurement =
                measurement.with_memory_usage(memory_usage, Some(memory_usage));

            measurements.push(enhanced_measurement);
        }

        Ok(measurements)
    }

    /// Helper functions - these would be implemented with actual model operations

    async fn create_model(&self, config: &ModelConfig) -> Result<TestModel> {
        // Simulate model creation time based on model size
        let creation_time = config.num_layers * config.hidden_size / 10000;
        tokio::time::sleep(std::time::Duration::from_millis(creation_time as u64)).await;
        Ok(format!("{:?}_model", config.model_type))
    }

    async fn create_attention_layer(&self, config: &ModelConfig) -> Result<TestAttentionLayer> {
        tokio::time::sleep(std::time::Duration::from_millis(10)).await;
        Ok(format!("{:?}_attention", config.model_type))
    }

    async fn create_multihead_attention(
        &self,
        config: &ModelConfig,
    ) -> Result<TestMultiheadAttention> {
        tokio::time::sleep(std::time::Duration::from_millis(20)).await;
        Ok(format!("{:?}_mha", config.model_type))
    }

    async fn forward_pass(
        &self,
        _model: &TestModel,
        batch_size: usize,
        sequence_length: usize,
    ) -> Result<Vec<f32>> {
        // Simulate forward pass time - scales with batch size and sequence length
        let computation_time = (batch_size * sequence_length) / 1000;
        tokio::time::sleep(std::time::Duration::from_millis(computation_time as u64)).await;
        Ok(vec![0.0; batch_size * sequence_length])
    }

    async fn attention_forward(
        &self,
        _layer: &TestAttentionLayer,
        batch_size: usize,
        sequence_length: usize,
    ) -> Result<Vec<f32>> {
        // Attention is O(n^2) in sequence length
        let computation_time = (batch_size * sequence_length * sequence_length) / 100000;
        tokio::time::sleep(std::time::Duration::from_millis(computation_time as u64)).await;
        Ok(vec![0.0; batch_size * sequence_length])
    }

    async fn multihead_attention_forward(
        &self,
        _layer: &TestMultiheadAttention,
        batch_size: usize,
        sequence_length: usize,
    ) -> Result<Vec<f32>> {
        // Multi-head attention with multiple heads
        let computation_time = (batch_size * sequence_length * sequence_length) / 50000;
        tokio::time::sleep(std::time::Duration::from_millis(computation_time as u64)).await;
        Ok(vec![0.0; batch_size * sequence_length])
    }

    async fn training_step(
        &self,
        _model: &TestModel,
        batch_size: usize,
        sequence_length: usize,
    ) -> Result<f32> {
        // Training includes forward + backward pass
        let computation_time = (batch_size * sequence_length) / 500;
        tokio::time::sleep(std::time::Duration::from_millis(computation_time as u64)).await;
        Ok(0.1) // Mock loss value
    }

    fn estimate_memory_usage(&self, config: &ModelConfig, batch_size: usize) -> u64 {
        // Rough estimation of memory usage in bytes
        let params = config.num_layers * config.hidden_size * config.hidden_size * 4; // 4 bytes per float
        let activations = batch_size * config.sequence_length * config.hidden_size * 4;
        (params + activations) as u64
    }
}

impl Default for TransformerBenchmarks {
    fn default() -> Self {
        Self::new()
    }
}

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

    #[test]
    fn test_transformer_benchmarks_creation() {
        let benchmarks = TransformerBenchmarks::new();
        // Compilation test
    }

    #[test]
    fn test_model_configs() {
        let benchmarks = TransformerBenchmarks::new();
        let configs = benchmarks.get_standard_model_configs();
        assert!(!configs.is_empty());

        // Check that we have different model types
        let has_bert = configs
            .iter()
            .any(|c| matches!(c.model_type, ModelType::Bert));
        let has_gpt = configs
            .iter()
            .any(|c| matches!(c.model_type, ModelType::Gpt));
        let has_t5 = configs
            .iter()
            .any(|c| matches!(c.model_type, ModelType::T5));

        assert!(has_bert);
        assert!(has_gpt);
        assert!(has_t5);
    }

    #[tokio::test]
    async fn test_forward_pass_benchmark() {
        let benchmarks = TransformerBenchmarks::new();
        let config = BenchmarkConfig::default()
            .with_measurement_iterations(2)
            .with_warmup_iterations(1);

        let model_config = ModelConfig {
            model_type: ModelType::Bert,
            hidden_size: 128,
            num_layers: 2,
            num_heads: 4,
            sequence_length: 64,
            vocab_size: 1000,
        };

        let result = benchmarks
            .benchmark_forward_pass(&config, &model_config, 4)
            .await;
        assert!(result.is_ok());

        let measurements = result.unwrap();
        assert_eq!(measurements.len(), 2);
        assert!(measurements[0].memory_bytes.is_some());
    }

    #[tokio::test]
    async fn test_memory_estimation() {
        let benchmarks = TransformerBenchmarks::new();
        let model_config = ModelConfig {
            model_type: ModelType::Bert,
            hidden_size: 768,
            num_layers: 12,
            num_heads: 12,
            sequence_length: 512,
            vocab_size: 30522,
        };

        let memory_b1 = benchmarks.estimate_memory_usage(&model_config, 1);
        let memory_b8 = benchmarks.estimate_memory_usage(&model_config, 8);

        // Memory usage should scale with batch size
        assert!(memory_b8 > memory_b1);
    }
}