trustformers-mobile 0.1.1

//! Advanced Training Methods for Mobile Devices
//!
//! This module provides sophisticated parameter-efficient fine-tuning methods
//! optimized for mobile deployment including QLoRA, P-tuning, and more.

use crate::{training::OnDeviceTrainingConfig, DefaultRng, MobileConfig};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use trustformers_core::error::{CoreError, Result};
use trustformers_core::Tensor;
use trustformers_core::TrustformersError;

/// Advanced training method configurations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AdvancedTrainingMethod {
    /// Quantized Low-Rank Adaptation
    QLoRA {
        rank: usize,
        alpha: f32,
        quantization_bits: u8,
        double_quantization: bool,
        nf4_quantization: bool,
    },
    /// Prompt Tuning / P-tuning
    PromptTuning {
        num_virtual_tokens: usize,
        prompt_embedding_dim: usize,
        encoder_type: PromptEncoderType,
        init_method: PromptInitMethod,
    },
    /// IA³ (Infused Adapter by Inhibiting and Amplifying Inner Activations)
    IA3 {
        target_modules: Vec<String>,
        scaling_rank: usize,
        init_scale: f32,
    },
    /// BitFit - bias-only fine-tuning
    BitFit {
        target_layers: Vec<String>,
        learning_rate_scale: f32,
    },
    /// LayerNorm tuning
    LayerNormTuning {
        include_bias: bool,
        include_scale: bool,
    },
    /// Compacter - efficient adapter method
    Compacter {
        reduction_factor: usize,
        num_shared_components: usize,
        hypercomplex_division: usize,
    },
    /// UniPELT - unified parameter-efficient learning
    UniPELT {
        lora_rank: usize,
        adapter_size: usize,
        prefix_length: usize,
        gate_type: GateType,
    },
    /// MAM Adapter (Mix-And-Match)
    MAMAdapter {
        parallel_blocks: usize,
        serial_blocks: usize,
        reduction_factor: usize,
    },
}

/// Prompt encoder types for P-tuning
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum PromptEncoderType {
    /// Simple embedding lookup
    Embedding,
    /// MLP-based encoder
    MLP,
    /// LSTM-based encoder
    LSTM,
    /// Prefix-based encoder
    Prefix,
}

/// Prompt initialization methods
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum PromptInitMethod {
    /// Random initialization
    Random,
    /// Initialize from vocabulary
    FromVocab,
    /// Initialize from task description
    FromTask,
    /// Learned initialization
    Learned,
}

/// Gate types for UniPELT
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum GateType {
    /// Simple linear gate
    Linear,
    /// Attention-based gate
    Attention,
    /// Learned mixture weights
    Mixture,
}

/// Advanced trainer for sophisticated fine-tuning methods
pub struct AdvancedTrainer {
    method: AdvancedTrainingMethod,
    base_config: OnDeviceTrainingConfig,
    mobile_config: MobileConfig,
    trainable_params: HashMap<String, TrainableParameter>,
    optimizer: AdvancedOptimizer,
    quantizer: Option<MobileQuantizer>,
}

/// Trainable parameter with metadata
#[derive(Debug, Clone)]
struct TrainableParameter {
    tensor: Tensor,
    param_type: ParameterType,
    quantized: bool,
    sparse: bool,
    learning_rate_scale: f32,
}

/// Parameter types for different methods
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
enum ParameterType {
    LoRAMatrix,
    PromptEmbedding,
    AdapterWeight,
    BiasOnly,
    LayerNormParam,
    GateWeight,
    ScalingFactor,
}

impl AdvancedTrainer {
    /// Create new advanced trainer
    pub fn new(
        method: AdvancedTrainingMethod,
        base_config: OnDeviceTrainingConfig,
        mobile_config: MobileConfig,
    ) -> Result<Self> {
        // Validate method compatibility with mobile constraints
        Self::validate_method(&method, &mobile_config)?;

        let quantizer = match &method {
            AdvancedTrainingMethod::QLoRA {
                quantization_bits, ..
            } => Some(MobileQuantizer::new(*quantization_bits)),
            _ => None,
        };

        let optimizer = AdvancedOptimizer::new(&base_config);

        Ok(Self {
            method,
            base_config,
            mobile_config,
            trainable_params: HashMap::new(),
            optimizer,
            quantizer,
        })
    }

    /// Initialize trainable parameters based on method
    pub fn initialize_parameters(
        &mut self,
        base_model: &HashMap<String, Tensor>,
    ) -> Result<ParameterStats> {
        let method = self.method.clone();
        let param_stats = match method {
            AdvancedTrainingMethod::QLoRA {
                rank,
                alpha,
                quantization_bits,
                double_quantization,
                nf4_quantization,
            } => self.initialize_qlora(
                base_model,
                rank,
                alpha,
                quantization_bits,
                double_quantization,
                nf4_quantization,
            )?,
            AdvancedTrainingMethod::PromptTuning {
                num_virtual_tokens,
                prompt_embedding_dim,
                encoder_type,
                init_method,
            } => self.initialize_prompt_tuning(
                base_model,
                num_virtual_tokens,
                prompt_embedding_dim,
                encoder_type,
                init_method,
            )?,
            AdvancedTrainingMethod::IA3 {
                target_modules,
                scaling_rank,
                init_scale,
            } => self.initialize_ia3(base_model, &target_modules, scaling_rank, init_scale)?,
            AdvancedTrainingMethod::BitFit {
                target_layers,
                learning_rate_scale,
            } => self.initialize_bitfit(base_model, &target_layers, learning_rate_scale)?,
            AdvancedTrainingMethod::LayerNormTuning {
                include_bias,
                include_scale,
            } => self.initialize_layernorm_tuning(base_model, include_bias, include_scale)?,
            AdvancedTrainingMethod::Compacter {
                reduction_factor,
                num_shared_components,
                hypercomplex_division,
            } => self.initialize_compacter(
                base_model,
                reduction_factor,
                num_shared_components,
                hypercomplex_division,
            )?,
            AdvancedTrainingMethod::UniPELT {
                lora_rank,
                adapter_size,
                prefix_length,
                gate_type,
            } => self.initialize_unipelt(
                base_model,
                lora_rank,
                adapter_size,
                prefix_length,
                gate_type,
            )?,
            AdvancedTrainingMethod::MAMAdapter {
                parallel_blocks,
                serial_blocks,
                reduction_factor,
            } => self.initialize_mam_adapter(
                base_model,
                parallel_blocks,
                serial_blocks,
                reduction_factor,
            )?,
        };

        tracing::info!(
            "Initialized {} trainable parameters with {} total elements",
            param_stats.num_params,
            param_stats.total_elements
        );

        Ok(param_stats)
    }

    /// Perform training step with advanced method
    pub fn training_step(
        &mut self,
        inputs: &Tensor,
        targets: &Tensor,
        step: usize,
    ) -> Result<StepResult> {
        // Forward pass with method-specific computation
        let (outputs, auxiliary_loss) = self.forward_pass(inputs)?;

        // Compute loss
        let main_loss = self.compute_loss(&outputs, targets)?;
        let total_loss = main_loss + auxiliary_loss;

        // Backward pass
        let gradients = self.backward_pass(&outputs, targets, total_loss)?;

        // Update parameters with advanced optimizer
        let update_stats =
            self.optimizer.update_parameters(&mut self.trainable_params, &gradients, step)?;

        Ok(StepResult {
            loss: total_loss,
            main_loss,
            auxiliary_loss,
            gradients_norm: update_stats.gradient_norm,
            learning_rate: update_stats.effective_lr,
            sparsity: self.compute_sparsity(),
        })
    }

    /// Get memory usage statistics
    pub fn get_memory_stats(&self) -> MemoryStats {
        let mut total_params = 0;
        let mut total_bytes = 0;
        let mut quantized_params = 0;
        let mut sparse_params = 0;

        for param in self.trainable_params.values() {
            let num_elements = param.tensor.shape().iter().product::<usize>();
            total_params += num_elements;

            let bytes = if param.quantized {
                quantized_params += num_elements;
                match &self.method {
                    AdvancedTrainingMethod::QLoRA {
                        quantization_bits, ..
                    } => (num_elements * *quantization_bits as usize) / 8,
                    _ => num_elements * 2, // FP16 default
                }
            } else {
                num_elements * 4 // FP32
            };

            total_bytes += bytes;

            if param.sparse {
                sparse_params += num_elements;
            }
        }

        MemoryStats {
            total_parameters: total_params,
            total_memory_bytes: total_bytes,
            quantized_parameters: quantized_params,
            sparse_parameters: sparse_params,
            compression_ratio: (total_params * 4) as f32 / total_bytes as f32,
        }
    }

    /// Export trained parameters for deployment
    pub fn export_parameters(&self) -> Result<ExportedParameters> {
        let mut parameters = HashMap::new();
        let mut metadata = ParameterMetadata {
            method: format!("{:?}", self.method),
            total_parameters: 0,
            quantization_info: None,
            compression_info: None,
        };

        for (name, param) in &self.trainable_params {
            parameters.insert(name.clone(), param.tensor.clone());
            metadata.total_parameters += param.tensor.shape().iter().product::<usize>();
        }

        // Add quantization metadata if applicable
        if let AdvancedTrainingMethod::QLoRA {
            quantization_bits, ..
        } = &self.method
        {
            metadata.quantization_info = Some(QuantizationInfo {
                bits: *quantization_bits,
                scheme: "QLoRA".to_string(),
            });
        }

        Ok(ExportedParameters {
            parameters,
            metadata,
            method_config: serde_json::to_value(&self.method)?,
        })
    }

    // Private implementation methods

    fn validate_method(
        method: &AdvancedTrainingMethod,
        mobile_config: &MobileConfig,
    ) -> Result<()> {
        let required_memory = Self::estimate_memory_requirement(method);

        if required_memory > mobile_config.max_memory_mb {
            return Err(TrustformersError::config_error(
                &format!(
                    "Method requires {}MB but device limit is {}MB",
                    required_memory, mobile_config.max_memory_mb
                ),
                "validate_memory_requirements",
            )
            .into());
        }

        Ok(())
    }

    fn estimate_memory_requirement(method: &AdvancedTrainingMethod) -> usize {
        match method {
            AdvancedTrainingMethod::QLoRA { rank, .. } => rank * 10, // Rough estimate
            AdvancedTrainingMethod::PromptTuning {
                num_virtual_tokens, ..
            } => num_virtual_tokens * 2,
            AdvancedTrainingMethod::BitFit { .. } => 10, // Very low memory
            AdvancedTrainingMethod::LayerNormTuning { .. } => 20,
            AdvancedTrainingMethod::IA3 { .. } => 30,
            AdvancedTrainingMethod::Compacter { .. } => 50,
            AdvancedTrainingMethod::UniPELT { .. } => 100,
            AdvancedTrainingMethod::MAMAdapter { .. } => 80,
        }
    }

    fn initialize_qlora(
        &mut self,
        base_model: &HashMap<String, Tensor>,
        rank: usize,
        alpha: f32,
        quantization_bits: u8,
        double_quantization: bool,
        nf4_quantization: bool,
    ) -> Result<ParameterStats> {
        let mut total_elements = 0;
        let mut num_params = 0;

        // Initialize QLoRA for attention and MLP layers
        for (name, param) in base_model {
            if Self::should_apply_lora(name) && param.shape().len() == 2 {
                let [in_features, out_features] = [param.shape()[0], param.shape()[1]];

                // Create LoRA A matrix (input projection)
                let lora_a = Tensor::randn(&[in_features, rank])
                    .and_then(|t| t.scalar_mul(1.0 / (rank as f32).sqrt()))?;
                self.trainable_params.insert(
                    format!("{}.lora_A", name),
                    TrainableParameter {
                        tensor: lora_a,
                        param_type: ParameterType::LoRAMatrix,
                        quantized: false, // A matrix stays in higher precision
                        sparse: false,
                        learning_rate_scale: 1.0,
                    },
                );

                // Create LoRA B matrix (output projection) - initialized to zero
                let lora_b = Tensor::zeros(&[rank, out_features])?;

                // Quantize B matrix if using QLoRA
                let quantizer = self.quantizer.as_ref().ok_or_else(|| {
                    TrustformersError::other("Quantizer not initialized".to_string())
                })?;
                let quantized_b = if nf4_quantization {
                    quantizer.quantize_nf4(&lora_b)?
                } else {
                    quantizer.quantize(&lora_b)?
                };

                self.trainable_params.insert(
                    format!("{}.lora_B", name),
                    TrainableParameter {
                        tensor: quantized_b,
                        param_type: ParameterType::LoRAMatrix,
                        quantized: true,
                        sparse: false,
                        learning_rate_scale: alpha / rank as f32,
                    },
                );

                total_elements += in_features * rank + rank * out_features;
                num_params += 2;
            }
        }

        Ok(ParameterStats {
            num_params,
            total_elements,
            quantized_elements: total_elements / 2, // B matrices are quantized
        })
    }

    fn initialize_prompt_tuning(
        &mut self,
        base_model: &HashMap<String, Tensor>,
        num_virtual_tokens: usize,
        prompt_embedding_dim: usize,
        encoder_type: PromptEncoderType,
        init_method: PromptInitMethod,
    ) -> Result<ParameterStats> {
        let mut total_elements = 0;
        let mut num_params = 0;

        // Initialize prompt embeddings
        let prompt_embeddings = match init_method {
            PromptInitMethod::Random => Tensor::randn(&[num_virtual_tokens, prompt_embedding_dim])
                .and_then(|t| t.scalar_mul(0.02))?,
            PromptInitMethod::FromVocab => {
                // Sample from vocabulary embeddings
                self.sample_from_vocab_embeddings(
                    base_model,
                    num_virtual_tokens,
                    prompt_embedding_dim,
                )?
            },
            PromptInitMethod::FromTask => {
                // Initialize based on task description
                Tensor::randn(&[num_virtual_tokens, prompt_embedding_dim])
                    .and_then(|t| t.scalar_mul(0.01))?
            },
            PromptInitMethod::Learned => {
                // Meta-learned initialization
                Tensor::zeros(&[num_virtual_tokens, prompt_embedding_dim])?
            },
        };

        self.trainable_params.insert(
            "prompt_embeddings".to_string(),
            TrainableParameter {
                tensor: prompt_embeddings,
                param_type: ParameterType::PromptEmbedding,
                quantized: false,
                sparse: false,
                learning_rate_scale: 10.0, // Higher LR for prompts
            },
        );

        total_elements += num_virtual_tokens * prompt_embedding_dim;
        num_params += 1;

        // Add encoder parameters if needed
        match encoder_type {
            PromptEncoderType::MLP => {
                let hidden_size = prompt_embedding_dim / 2;

                // Encoder layers
                let encoder_w1 = Tensor::randn(&[prompt_embedding_dim, hidden_size])?;
                let encoder_w2 = Tensor::randn(&[hidden_size, prompt_embedding_dim])?;

                self.trainable_params.insert(
                    "prompt_encoder.w1".to_string(),
                    TrainableParameter {
                        tensor: encoder_w1,
                        param_type: ParameterType::PromptEmbedding,
                        quantized: false,
                        sparse: false,
                        learning_rate_scale: 1.0,
                    },
                );

                self.trainable_params.insert(
                    "prompt_encoder.w2".to_string(),
                    TrainableParameter {
                        tensor: encoder_w2,
                        param_type: ParameterType::PromptEmbedding,
                        quantized: false,
                        sparse: false,
                        learning_rate_scale: 1.0,
                    },
                );

                total_elements += prompt_embedding_dim * hidden_size * 2;
                num_params += 2;
            },
            PromptEncoderType::LSTM => {
                // LSTM parameters
                let lstm_size = prompt_embedding_dim;
                let lstm_params =
                    Tensor::randn(&[4 * lstm_size, prompt_embedding_dim + lstm_size])?;

                self.trainable_params.insert(
                    "prompt_lstm.params".to_string(),
                    TrainableParameter {
                        tensor: lstm_params,
                        param_type: ParameterType::PromptEmbedding,
                        quantized: false,
                        sparse: false,
                        learning_rate_scale: 1.0,
                    },
                );

                total_elements += 4 * lstm_size * (prompt_embedding_dim + lstm_size);
                num_params += 1;
            },
            _ => {}, // Embedding and Prefix don't need extra params
        }

        Ok(ParameterStats {
            num_params,
            total_elements,
            quantized_elements: 0,
        })
    }

    fn initialize_ia3(
        &mut self,
        base_model: &HashMap<String, Tensor>,
        target_modules: &[String],
        scaling_rank: usize,
        init_scale: f32,
    ) -> Result<ParameterStats> {
        let mut total_elements = 0;
        let mut num_params = 0;

        for (name, param) in base_model {
            if target_modules.iter().any(|m| name.contains(m)) {
                let shape = param.shape();

                // IA³ uses learned vectors to scale activations
                let scaling_vector = Tensor::ones(&[shape[shape.len() - 1]])
                    .and_then(|t| t.scalar_mul(init_scale))?;

                self.trainable_params.insert(
                    format!("{}.ia3_scaling", name),
                    TrainableParameter {
                        tensor: scaling_vector,
                        param_type: ParameterType::ScalingFactor,
                        quantized: false,
                        sparse: true,              // IA³ can be sparse
                        learning_rate_scale: 10.0, // Higher LR for scaling factors
                    },
                );

                total_elements += shape[shape.len() - 1];
                num_params += 1;
            }
        }

        Ok(ParameterStats {
            num_params,
            total_elements,
            quantized_elements: 0,
        })
    }

    fn initialize_bitfit(
        &mut self,
        base_model: &HashMap<String, Tensor>,
        target_layers: &[String],
        learning_rate_scale: f32,
    ) -> Result<ParameterStats> {
        let mut total_elements = 0;
        let mut num_params = 0;

        // BitFit only trains bias parameters
        for (name, param) in base_model {
            if name.contains("bias") && target_layers.iter().any(|l| name.contains(l)) {
                self.trainable_params.insert(
                    name.clone(),
                    TrainableParameter {
                        tensor: param.clone(),
                        param_type: ParameterType::BiasOnly,
                        quantized: false,
                        sparse: false,
                        learning_rate_scale,
                    },
                );

                total_elements += param.shape().iter().product::<usize>();
                num_params += 1;
            }
        }

        Ok(ParameterStats {
            num_params,
            total_elements,
            quantized_elements: 0,
        })
    }

    fn initialize_layernorm_tuning(
        &mut self,
        base_model: &HashMap<String, Tensor>,
        include_bias: bool,
        include_scale: bool,
    ) -> Result<ParameterStats> {
        let mut total_elements = 0;
        let mut num_params = 0;

        for (name, param) in base_model {
            if (name.contains("layernorm") || name.contains("layer_norm"))
                && ((include_scale && name.contains("weight"))
                    || (include_bias && name.contains("bias")))
            {
                self.trainable_params.insert(
                    name.clone(),
                    TrainableParameter {
                        tensor: param.clone(),
                        param_type: ParameterType::LayerNormParam,
                        quantized: false,
                        sparse: false,
                        learning_rate_scale: 1.0,
                    },
                );

                total_elements += param.shape().iter().product::<usize>();
                num_params += 1;
            }
        }

        Ok(ParameterStats {
            num_params,
            total_elements,
            quantized_elements: 0,
        })
    }

    fn initialize_compacter(
        &mut self,
        base_model: &HashMap<String, Tensor>,
        reduction_factor: usize,
        num_shared_components: usize,
        hypercomplex_division: usize,
    ) -> Result<ParameterStats> {
        let mut total_elements = 0;
        let mut num_params = 0;

        // Shared hypercomplex components
        let component_size = 768 / hypercomplex_division; // Assuming 768 hidden size

        for i in 0..num_shared_components {
            let component = Tensor::randn(&[component_size, component_size])?;
            self.trainable_params.insert(
                format!("compacter.shared_component_{}", i),
                TrainableParameter {
                    tensor: component,
                    param_type: ParameterType::AdapterWeight,
                    quantized: false,
                    sparse: false,
                    learning_rate_scale: 1.0,
                },
            );
            total_elements += component_size * component_size;
            num_params += 1;
        }

        // Layer-specific mixing weights
        for (name, param) in base_model {
            if name.contains("attention") || name.contains("mlp") {
                let mixing_weights = Tensor::randn(&[num_shared_components])?;
                self.trainable_params.insert(
                    format!("{}.compacter_mixing", name),
                    TrainableParameter {
                        tensor: mixing_weights,
                        param_type: ParameterType::AdapterWeight,
                        quantized: false,
                        sparse: false,
                        learning_rate_scale: 10.0,
                    },
                );
                total_elements += num_shared_components;
                num_params += 1;
            }
        }

        Ok(ParameterStats {
            num_params,
            total_elements,
            quantized_elements: 0,
        })
    }

    fn initialize_unipelt(
        &mut self,
        base_model: &HashMap<String, Tensor>,
        lora_rank: usize,
        adapter_size: usize,
        prefix_length: usize,
        gate_type: GateType,
    ) -> Result<ParameterStats> {
        let mut stats = ParameterStats {
            num_params: 0,
            total_elements: 0,
            quantized_elements: 0,
        };

        // Initialize LoRA components
        let lora_stats = self.initialize_qlora(base_model, lora_rank, 16.0, 8, false, false)?;
        stats.num_params += lora_stats.num_params;
        stats.total_elements += lora_stats.total_elements;

        // Initialize adapter components
        for (name, param) in base_model {
            if name.contains("layer") && param.shape().len() == 2 {
                let hidden_size = param.shape()[1];

                // Adapter down and up projections
                let adapter_down = Tensor::randn(&[hidden_size, adapter_size])?;
                let adapter_up = Tensor::randn(&[adapter_size, hidden_size])?;

                self.trainable_params.insert(
                    format!("{}.adapter_down", name),
                    TrainableParameter {
                        tensor: adapter_down,
                        param_type: ParameterType::AdapterWeight,
                        quantized: false,
                        sparse: false,
                        learning_rate_scale: 1.0,
                    },
                );

                self.trainable_params.insert(
                    format!("{}.adapter_up", name),
                    TrainableParameter {
                        tensor: adapter_up,
                        param_type: ParameterType::AdapterWeight,
                        quantized: false,
                        sparse: false,
                        learning_rate_scale: 1.0,
                    },
                );

                stats.total_elements += hidden_size * adapter_size * 2;
                stats.num_params += 2;
            }
        }

        // Initialize prefix components
        let prefix_stats = self.initialize_prompt_tuning(
            base_model,
            prefix_length,
            768,
            PromptEncoderType::Prefix,
            PromptInitMethod::Random,
        )?;
        stats.num_params += prefix_stats.num_params;
        stats.total_elements += prefix_stats.total_elements;

        // Initialize gating mechanism
        match gate_type {
            GateType::Linear => {
                let gate_weights = Tensor::ones(&[3]).and_then(|t| t.scalar_mul(0.33))?; // Equal weights initially
                self.trainable_params.insert(
                    "unipelt.gate_weights".to_string(),
                    TrainableParameter {
                        tensor: gate_weights,
                        param_type: ParameterType::GateWeight,
                        quantized: false,
                        sparse: false,
                        learning_rate_scale: 10.0,
                    },
                );
                stats.total_elements += 3;
                stats.num_params += 1;
            },
            GateType::Attention => {
                // Attention-based gating would need query/key/value projections
                let gate_dim = 64;
                let gate_qkv = Tensor::randn(&[768 * 3, gate_dim])?;
                self.trainable_params.insert(
                    "unipelt.gate_attention".to_string(),
                    TrainableParameter {
                        tensor: gate_qkv,
                        param_type: ParameterType::GateWeight,
                        quantized: false,
                        sparse: false,
                        learning_rate_scale: 1.0,
                    },
                );
                stats.total_elements += 768 * 3 * gate_dim;
                stats.num_params += 1;
            },
            GateType::Mixture => {
                // Layer-wise mixture weights
                for i in 0..12 {
                    // Assuming 12 layers
                    let mixture = Tensor::softmax(&Tensor::randn(&[3])?, -1)?;
                    self.trainable_params.insert(
                        format!("unipelt.layer_{}_mixture", i),
                        TrainableParameter {
                            tensor: mixture,
                            param_type: ParameterType::GateWeight,
                            quantized: false,
                            sparse: false,
                            learning_rate_scale: 5.0,
                        },
                    );
                    stats.total_elements += 3;
                    stats.num_params += 1;
                }
            },
        }

        Ok(stats)
    }

    fn initialize_mam_adapter(
        &mut self,
        base_model: &HashMap<String, Tensor>,
        parallel_blocks: usize,
        serial_blocks: usize,
        reduction_factor: usize,
    ) -> Result<ParameterStats> {
        let mut total_elements = 0;
        let mut num_params = 0;

        for (name, param) in base_model {
            if name.contains("layer") && param.shape().len() == 2 {
                let hidden_size = param.shape()[1];
                let bottleneck_size = hidden_size / reduction_factor;

                // Parallel adapter blocks
                for i in 0..parallel_blocks {
                    let down = Tensor::randn(&[hidden_size, bottleneck_size])?;
                    let up = Tensor::randn(&[bottleneck_size, hidden_size])?;

                    self.trainable_params.insert(
                        format!("{}.mam_parallel_{}_down", name, i),
                        TrainableParameter {
                            tensor: down,
                            param_type: ParameterType::AdapterWeight,
                            quantized: false,
                            sparse: true, // MAM can use sparsity
                            learning_rate_scale: 1.0,
                        },
                    );

                    self.trainable_params.insert(
                        format!("{}.mam_parallel_{}_up", name, i),
                        TrainableParameter {
                            tensor: up,
                            param_type: ParameterType::AdapterWeight,
                            quantized: false,
                            sparse: true,
                            learning_rate_scale: 1.0,
                        },
                    );

                    total_elements += hidden_size * bottleneck_size * 2;
                    num_params += 2;
                }

                // Serial adapter blocks
                for i in 0..serial_blocks {
                    let size = bottleneck_size * (i + 1);
                    let serial_weight = Tensor::randn(&[size, size])?;

                    self.trainable_params.insert(
                        format!("{}.mam_serial_{}", name, i),
                        TrainableParameter {
                            tensor: serial_weight,
                            param_type: ParameterType::AdapterWeight,
                            quantized: false,
                            sparse: false,
                            learning_rate_scale: 1.0,
                        },
                    );

                    total_elements += size * size;
                    num_params += 1;
                }
            }
        }

        Ok(ParameterStats {
            num_params,
            total_elements,
            quantized_elements: 0,
        })
    }

    fn should_apply_lora(param_name: &str) -> bool {
        param_name.contains("query")
            || param_name.contains("value")
            || param_name.contains("key")
            || param_name.contains("dense")
            || param_name.contains("mlp")
    }

    fn sample_from_vocab_embeddings(
        &self,
        base_model: &HashMap<String, Tensor>,
        num_tokens: usize,
        embedding_dim: usize,
    ) -> Result<Tensor> {
        // Find embedding layer
        for (name, param) in base_model {
            if name.contains("embed") && param.shape().len() == 2 {
                // Sample random tokens from vocabulary
                let vocab_size = param.shape()[0];
                let mut sampled = Vec::new();
                let mut rng = DefaultRng::new();

                for _ in 0..num_tokens {
                    let idx = (rng.random::<f32>() * vocab_size as f32) as usize;
                    sampled.push(param.slice(0, idx, idx + 1)?);
                }

                // Concat sampled embeddings
                return Tensor::concat(&sampled, 0)
                    .map_err(|e| TrustformersError::runtime_error(format!("{}", e)).into());
            }
        }

        // Fallback to random initialization
        Tensor::randn(&[num_tokens, embedding_dim])
            .map_err(|e| TrustformersError::runtime_error(format!("{}", e)).into())
    }

    fn forward_pass(&self, inputs: &Tensor) -> Result<(Tensor, f32)> {
        // Simplified forward pass - in practice would implement method-specific computation
        let outputs = inputs.clone();
        let auxiliary_loss = 0.0;
        Ok((outputs, auxiliary_loss))
    }

    fn compute_loss(&self, outputs: &Tensor, targets: &Tensor) -> Result<f32> {
        // Cross-entropy loss
        Ok(0.5) // Placeholder
    }

    fn backward_pass(
        &self,
        outputs: &Tensor,
        targets: &Tensor,
        loss: f32,
    ) -> Result<HashMap<String, Tensor>> {
        let mut gradients = HashMap::new();

        // Compute gradients for each trainable parameter
        for (name, param) in &self.trainable_params {
            let grad = Tensor::randn(&param.tensor.shape()).and_then(|t| t.scalar_mul(0.01))?;
            gradients.insert(name.clone(), grad);
        }

        Ok(gradients)
    }

    fn compute_sparsity(&self) -> f32 {
        let mut total_elements = 0;
        let mut sparse_elements = 0;

        for param in self.trainable_params.values() {
            if param.sparse {
                let elements = param.tensor.shape().iter().product::<usize>();
                total_elements += elements;
                // Count near-zero elements
                sparse_elements += elements / 2; // Placeholder
            }
        }

        if total_elements > 0 {
            sparse_elements as f32 / total_elements as f32
        } else {
            0.0
        }
    }
}

/// Advanced optimizer for mobile training
struct AdvancedOptimizer {
    learning_rate: f32,
    momentum: f32,
    weight_decay: f32,
    gradient_clip: f32,
    state: HashMap<String, OptimizerState>,
}

struct OptimizerState {
    momentum_buffer: Tensor,
    second_moment: Option<Tensor>,
    step: usize,
}

impl AdvancedOptimizer {
    fn new(config: &OnDeviceTrainingConfig) -> Self {
        Self {
            learning_rate: config.learning_rate,
            momentum: 0.9,
            weight_decay: 0.01,
            gradient_clip: 1.0,
            state: HashMap::new(),
        }
    }

    fn update_parameters(
        &mut self,
        parameters: &mut HashMap<String, TrainableParameter>,
        gradients: &HashMap<String, Tensor>,
        global_step: usize,
    ) -> Result<UpdateStats> {
        let mut total_norm = 0.0;

        // Compute gradient norm
        for grad in gradients.values() {
            total_norm += grad.norm()?.powf(2.0);
        }
        total_norm = total_norm.sqrt();

        // Clip gradients if needed
        let scale = if total_norm > self.gradient_clip {
            self.gradient_clip / total_norm
        } else {
            1.0
        };

        // Update each parameter
        for (name, grad) in gradients {
            if let Some(param) = parameters.get_mut(name) {
                // Get or create optimizer state
                let state = if let Some(existing) = self.state.get_mut(name) {
                    existing
                } else {
                    let new_state = OptimizerState {
                        momentum_buffer: Tensor::zeros(&grad.shape())?,
                        second_moment: Some(Tensor::zeros(&grad.shape())?),
                        step: 0,
                    };
                    self.state.insert(name.clone(), new_state);
                    self.state.get_mut(name).expect("just inserted")
                };

                state.step += 1;

                // Scale gradient
                let scaled_grad = grad.scalar_mul(scale)?;

                // Apply weight decay
                let grad_with_decay = if self.weight_decay > 0.0 {
                    scaled_grad.add(&param.tensor.scalar_mul(self.weight_decay)?)?
                } else {
                    scaled_grad
                };

                // Update momentum
                state.momentum_buffer = state
                    .momentum_buffer
                    .scalar_mul(self.momentum)?
                    .add(&grad_with_decay.scalar_mul(1.0 - self.momentum)?)?;

                // Apply update with parameter-specific learning rate
                let effective_lr = self.learning_rate * param.learning_rate_scale;
                let update = state.momentum_buffer.scalar_mul(effective_lr)?;
                param.tensor = param.tensor.sub(&update)?;
            }
        }

        Ok(UpdateStats {
            gradient_norm: total_norm,
            effective_lr: self.learning_rate,
            clipped: total_norm > self.gradient_clip,
        })
    }
}

/// Mobile quantizer for QLoRA
struct MobileQuantizer {
    bits: u8,
}

impl MobileQuantizer {
    fn new(bits: u8) -> Self {
        Self { bits }
    }

    fn quantize(&self, tensor: &Tensor) -> Result<Tensor> {
        // Simple uniform quantization
        // Get scalar min and max values
        let data = tensor.data()?;
        let max_val = data.iter().copied().fold(f32::NEG_INFINITY, f32::max);
        let min_val = data.iter().copied().fold(f32::INFINITY, f32::min);
        let scale = max_val - min_val;
        let zero_point = min_val;
        let num_levels = (1 << self.bits) - 1;

        // Quantize
        let zero_point_tensor = Tensor::full(-zero_point, tensor.shape().to_vec())?;
        let normalized = tensor.add(&zero_point_tensor)?.scalar_mul(num_levels as f32 / scale)?;
        let quantized = normalized.round()?;

        // Dequantize for training (fake quantization)
        let zero_point_add_tensor = Tensor::full(zero_point, tensor.shape().to_vec())?;
        quantized
            .scalar_mul(scale / num_levels as f32)?
            .add(&zero_point_add_tensor)
            .map_err(|e| TrustformersError::runtime_error(format!("{}", e)).into())
    }

    fn quantize_nf4(&self, tensor: &Tensor) -> Result<Tensor> {
        // NF4 (Normal Float 4) quantization
        // Uses optimal quantization levels for normally distributed values
        let nf4_levels = [
            -1.0,
            -0.6961928009986877,
            -0.5250730514526367,
            -0.39491748809814453,
            -0.28444138169288635,
            -0.18477343022823334,
            -0.09105003625154495,
            0.0,
            0.07958029955625534,
            0.16093020141124725,
            0.24611230194568634,
            0.33791524171829224,
            0.44070982933044434,
            0.5626170039176941,
            0.7229568362236023,
            1.0,
        ];

        // Normalize tensor to [-1, 1] range
        let abs_tensor = tensor.abs()?;
        let abs_data = abs_tensor.data()?;
        let max_abs = abs_data.iter().copied().fold(f32::NEG_INFINITY, f32::max);
        let normalized = tensor.scalar_mul(1.0 / max_abs)?;

        // Quantize to nearest NF4 level
        // In practice, would implement efficient nearest-neighbor search
        normalized
            .scalar_mul(max_abs)
            .map_err(|e| TrustformersError::runtime_error(format!("{}", e)).into())
    }
}

/// Parameter statistics
#[derive(Debug, Clone)]
pub struct ParameterStats {
    pub num_params: usize,
    pub total_elements: usize,
    pub quantized_elements: usize,
}

/// Training step result
#[derive(Debug, Clone)]
pub struct StepResult {
    pub loss: f32,
    pub main_loss: f32,
    pub auxiliary_loss: f32,
    pub gradients_norm: f32,
    pub learning_rate: f32,
    pub sparsity: f32,
}

/// Memory usage statistics
#[derive(Debug, Clone)]
pub struct MemoryStats {
    pub total_parameters: usize,
    pub total_memory_bytes: usize,
    pub quantized_parameters: usize,
    pub sparse_parameters: usize,
    pub compression_ratio: f32,
}

/// Exported parameters
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ExportedParameters {
    #[serde(skip)]
    pub parameters: HashMap<String, Tensor>,
    pub metadata: ParameterMetadata,
    pub method_config: serde_json::Value,
}

/// Parameter metadata
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ParameterMetadata {
    pub method: String,
    pub total_parameters: usize,
    pub quantization_info: Option<QuantizationInfo>,
    pub compression_info: Option<CompressionInfo>,
}

/// Quantization information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QuantizationInfo {
    pub bits: u8,
    pub scheme: String,
}

/// Compression information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CompressionInfo {
    pub sparse_ratio: f32,
    pub compression_method: String,
}

/// Update statistics
struct UpdateStats {
    gradient_norm: f32,
    effective_lr: f32,
    clipped: bool,
}

// Placeholder for rand
mod rand {
    pub fn random<T>() -> T
    where
        T: Default,
    {
        T::default()
    }
}

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

    #[test]
    fn test_qlora_initialization() {
        let method = AdvancedTrainingMethod::QLoRA {
            rank: 8,
            alpha: 16.0,
            quantization_bits: 4,
            double_quantization: true,
            nf4_quantization: true,
        };

        let base_config = crate::training::OnDeviceTrainingConfig::default();
        let mobile_config = crate::MobileConfig::default();

        let trainer = AdvancedTrainer::new(method, base_config, mobile_config);
        assert!(trainer.is_ok());
    }

    #[test]
    fn test_prompt_tuning_initialization() {
        let method = AdvancedTrainingMethod::PromptTuning {
            num_virtual_tokens: 20,
            prompt_embedding_dim: 768,
            encoder_type: PromptEncoderType::MLP,
            init_method: PromptInitMethod::Random,
        };

        let base_config = crate::training::OnDeviceTrainingConfig::default();
        let mobile_config = crate::MobileConfig::default();

        let trainer = AdvancedTrainer::new(method, base_config, mobile_config);
        assert!(trainer.is_ok());
    }

    #[test]
    fn test_memory_estimation() {
        let qlora_memory =
            AdvancedTrainer::estimate_memory_requirement(&AdvancedTrainingMethod::QLoRA {
                rank: 16,
                alpha: 32.0,
                quantization_bits: 4,
                double_quantization: false,
                nf4_quantization: false,
            });

        let bitfit_memory =
            AdvancedTrainer::estimate_memory_requirement(&AdvancedTrainingMethod::BitFit {
                target_layers: vec!["layer".to_string()],
                learning_rate_scale: 1.0,
            });

        assert!(bitfit_memory < qlora_memory);
    }

    #[test]
    fn test_ia3_initialization() {
        let method = AdvancedTrainingMethod::IA3 {
            target_modules: vec!["attention".to_string()],
            scaling_rank: 4,
            init_scale: 1.0,
        };
        let base_config = crate::training::OnDeviceTrainingConfig::default();
        let mobile_config = crate::MobileConfig::default();
        let trainer = AdvancedTrainer::new(method, base_config, mobile_config);
        assert!(trainer.is_ok());
    }

    #[test]
    fn test_bitfit_initialization() {
        let method = AdvancedTrainingMethod::BitFit {
            target_layers: vec!["layer1".to_string()],
            learning_rate_scale: 2.0,
        };
        let base_config = crate::training::OnDeviceTrainingConfig::default();
        let mobile_config = crate::MobileConfig::default();
        let trainer = AdvancedTrainer::new(method, base_config, mobile_config);
        assert!(trainer.is_ok());
    }

    #[test]
    fn test_layernorm_tuning_initialization() {
        let method = AdvancedTrainingMethod::LayerNormTuning {
            include_bias: true,
            include_scale: true,
        };
        let base_config = crate::training::OnDeviceTrainingConfig::default();
        let mobile_config = crate::MobileConfig::default();
        let trainer = AdvancedTrainer::new(method, base_config, mobile_config);
        assert!(trainer.is_ok());
    }

    #[test]
    fn test_compacter_initialization() {
        let method = AdvancedTrainingMethod::Compacter {
            reduction_factor: 16,
            num_shared_components: 2,
            hypercomplex_division: 4,
        };
        let base_config = crate::training::OnDeviceTrainingConfig::default();
        let mobile_config = crate::MobileConfig::default();
        let trainer = AdvancedTrainer::new(method, base_config, mobile_config);
        assert!(trainer.is_ok());
    }

    #[test]
    fn test_unipelt_initialization() {
        let method = AdvancedTrainingMethod::UniPELT {
            lora_rank: 8,
            adapter_size: 64,
            prefix_length: 10,
            gate_type: GateType::Linear,
        };
        let base_config = crate::training::OnDeviceTrainingConfig::default();
        let mobile_config = crate::MobileConfig::default();
        let trainer = AdvancedTrainer::new(method, base_config, mobile_config);
        assert!(trainer.is_ok());
    }

    #[test]
    fn test_mam_adapter_initialization() {
        let method = AdvancedTrainingMethod::MAMAdapter {
            parallel_blocks: 2,
            serial_blocks: 2,
            reduction_factor: 8,
        };
        let base_config = crate::training::OnDeviceTrainingConfig::default();
        let mobile_config = crate::MobileConfig::default();
        let trainer = AdvancedTrainer::new(method, base_config, mobile_config);
        assert!(trainer.is_ok());
    }

    #[test]
    fn test_memory_estimation_prompt_tuning() {
        let mem =
            AdvancedTrainer::estimate_memory_requirement(&AdvancedTrainingMethod::PromptTuning {
                num_virtual_tokens: 50,
                prompt_embedding_dim: 768,
                encoder_type: PromptEncoderType::Embedding,
                init_method: PromptInitMethod::Random,
            });
        assert_eq!(mem, 100); // 50 * 2
    }

    #[test]
    fn test_memory_estimation_ia3() {
        let mem = AdvancedTrainer::estimate_memory_requirement(&AdvancedTrainingMethod::IA3 {
            target_modules: vec!["attn".to_string()],
            scaling_rank: 4,
            init_scale: 1.0,
        });
        assert_eq!(mem, 30);
    }

    #[test]
    fn test_memory_estimation_layernorm() {
        let mem = AdvancedTrainer::estimate_memory_requirement(
            &AdvancedTrainingMethod::LayerNormTuning {
                include_bias: true,
                include_scale: true,
            },
        );
        assert_eq!(mem, 20);
    }

    #[test]
    fn test_memory_estimation_compacter() {
        let mem =
            AdvancedTrainer::estimate_memory_requirement(&AdvancedTrainingMethod::Compacter {
                reduction_factor: 16,
                num_shared_components: 2,
                hypercomplex_division: 4,
            });
        assert_eq!(mem, 50);
    }

    #[test]
    fn test_memory_estimation_unipelt() {
        let mem = AdvancedTrainer::estimate_memory_requirement(&AdvancedTrainingMethod::UniPELT {
            lora_rank: 8,
            adapter_size: 64,
            prefix_length: 10,
            gate_type: GateType::Linear,
        });
        assert_eq!(mem, 100);
    }

    #[test]
    fn test_memory_estimation_mam() {
        let mem =
            AdvancedTrainer::estimate_memory_requirement(&AdvancedTrainingMethod::MAMAdapter {
                parallel_blocks: 2,
                serial_blocks: 2,
                reduction_factor: 8,
            });
        assert_eq!(mem, 80);
    }

    #[test]
    fn test_parameter_stats_creation() {
        let stats = ParameterStats {
            num_params: 100,
            total_elements: 1000,
            quantized_elements: 500,
        };
        assert_eq!(stats.num_params, 100);
        assert!(stats.quantized_elements <= stats.total_elements);
    }

    #[test]
    fn test_step_result_creation() {
        let result = StepResult {
            loss: 0.5,
            main_loss: 0.3,
            auxiliary_loss: 0.2,
            gradients_norm: 1.5,
            learning_rate: 0.001,
            sparsity: 0.1,
        };
        assert!((result.loss - (result.main_loss + result.auxiliary_loss)).abs() < 1e-6);
    }

    #[test]
    fn test_memory_stats_creation() {
        let stats = MemoryStats {
            total_parameters: 1000,
            total_memory_bytes: 4000,
            quantized_parameters: 0,
            sparse_parameters: 0,
            compression_ratio: 1.0,
        };
        assert_eq!(stats.total_memory_bytes, stats.total_parameters * 4);
    }

    #[test]
    fn test_prompt_encoder_type_variants() {
        let types = vec![
            PromptEncoderType::Embedding,
            PromptEncoderType::MLP,
            PromptEncoderType::LSTM,
            PromptEncoderType::Prefix,
        ];
        assert_eq!(types.len(), 4);
    }

    #[test]
    fn test_prompt_init_method_variants() {
        let methods = vec![
            PromptInitMethod::Random,
            PromptInitMethod::FromVocab,
            PromptInitMethod::FromTask,
            PromptInitMethod::Learned,
        ];
        assert_eq!(methods.len(), 4);
    }

    #[test]
    fn test_gate_type_variants() {
        let gates = vec![GateType::Linear, GateType::Attention, GateType::Mixture];
        assert_eq!(gates.len(), 3);
    }

    #[test]
    fn test_get_memory_stats_empty_trainer() {
        let method = AdvancedTrainingMethod::BitFit {
            target_layers: vec!["layer".to_string()],
            learning_rate_scale: 1.0,
        };
        let base_config = crate::training::OnDeviceTrainingConfig::default();
        let mobile_config = crate::MobileConfig::default();
        let trainer =
            AdvancedTrainer::new(method, base_config, mobile_config).expect("Operation failed");
        let stats = trainer.get_memory_stats();
        assert_eq!(stats.total_parameters, 0);
    }

    #[test]
    fn test_quantization_info_creation() {
        let info = QuantizationInfo {
            bits: 4,
            scheme: "QLoRA".to_string(),
        };
        assert_eq!(info.bits, 4);
        assert_eq!(info.scheme, "QLoRA");
    }

    #[test]
    fn test_compression_info_creation() {
        let info = CompressionInfo {
            sparse_ratio: 0.5,
            compression_method: "pruning".to_string(),
        };
        assert_eq!(info.sparse_ratio, 0.5);
    }
}