torsh-cli 0.1.2

//! PyTorch model format parser for ToRSh compatibility
//!
//! This module provides functionality to parse and convert PyTorch models
//! to ToRSh format, enabling interoperability between frameworks.

// Infrastructure module - functions designed for CLI command integration
#![allow(dead_code)]

use anyhow::{Context, Result};
use std::collections::HashMap;
use std::path::Path;
use tracing::{debug, info, warn};

// ✅ SciRS2 POLICY COMPLIANT: Use scirs2-core unified access patterns
use scirs2_core::random::{thread_rng, Distribution, Normal};

// ToRSh integration
use torsh::core::device::DeviceType;

use super::tensor_integration::ModelTensor;
use super::types::{DType, Device, LayerInfo, ModelMetadata, TensorInfo, TorshModel};

/// PyTorch model metadata extracted from .pth files
#[derive(Debug, Clone)]
pub struct PyTorchModelInfo {
    /// PyTorch version
    pub pytorch_version: String,
    /// Model class name (if available)
    pub model_class: Option<String>,
    /// State dict keys
    pub state_dict_keys: Vec<String>,
    /// Total file size in bytes
    pub file_size: u64,
    /// Number of parameters
    pub num_parameters: u64,
    /// Whether this is a full model or just state_dict
    pub is_full_model: bool,
}

/// PyTorch layer type mapping
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum PyTorchLayerType {
    Linear,
    Conv2d,
    Conv1d,
    Conv3d,
    BatchNorm2d,
    BatchNorm1d,
    LayerNorm,
    Dropout,
    Embedding,
    LSTM,
    GRU,
    Attention,
    Unknown,
}

impl PyTorchLayerType {
    /// Convert PyTorch layer type to ToRSh layer type string
    pub fn to_torsh_type(&self) -> &'static str {
        match self {
            PyTorchLayerType::Linear => "Linear",
            PyTorchLayerType::Conv2d => "Conv2d",
            PyTorchLayerType::Conv1d => "Conv1d",
            PyTorchLayerType::Conv3d => "Conv3d",
            PyTorchLayerType::BatchNorm2d => "BatchNorm2d",
            PyTorchLayerType::BatchNorm1d => "BatchNorm1d",
            PyTorchLayerType::LayerNorm => "LayerNorm",
            PyTorchLayerType::Dropout => "Dropout",
            PyTorchLayerType::Embedding => "Embedding",
            PyTorchLayerType::LSTM => "LSTM",
            PyTorchLayerType::GRU => "GRU",
            PyTorchLayerType::Attention => "Attention",
            PyTorchLayerType::Unknown => "Unknown",
        }
    }

    /// Infer layer type from parameter name
    pub fn from_param_name(param_name: &str) -> Self {
        if param_name.contains("linear") || param_name.contains("fc") {
            PyTorchLayerType::Linear
        } else if param_name.contains("conv3d") {
            PyTorchLayerType::Conv3d
        } else if param_name.contains("conv1d") {
            PyTorchLayerType::Conv1d
        } else if param_name.contains("conv2d") || param_name.contains("conv") {
            // Default conv layers to Conv2d (most common in vision models)
            PyTorchLayerType::Conv2d
        } else if param_name.contains("bn") || param_name.contains("batch_norm") {
            PyTorchLayerType::BatchNorm2d
        } else if param_name.contains("layer_norm") || param_name.contains("ln") {
            PyTorchLayerType::LayerNorm
        } else if param_name.contains("embed") {
            PyTorchLayerType::Embedding
        } else if param_name.contains("lstm") {
            PyTorchLayerType::LSTM
        } else if param_name.contains("gru") {
            PyTorchLayerType::GRU
        } else if param_name.contains("attn") || param_name.contains("attention") {
            PyTorchLayerType::Attention
        } else {
            PyTorchLayerType::Unknown
        }
    }
}

/// Parse PyTorch model file and extract metadata
pub async fn parse_pytorch_model(path: &Path) -> Result<PyTorchModelInfo> {
    info!("Parsing PyTorch model from: {}", path.display());

    // Read file metadata
    let metadata = tokio::fs::metadata(path)
        .await
        .with_context(|| format!("Failed to read file metadata: {}", path.display()))?;

    let file_size = metadata.len();

    // Read file header to detect format
    let file_data = tokio::fs::read(path)
        .await
        .with_context(|| format!("Failed to read PyTorch file: {}", path.display()))?;

    // Check if it's a ZIP file (PyTorch >= 1.6 uses ZIP format)
    let is_zip = file_data.len() >= 4 && &file_data[0..4] == b"PK\x03\x04";

    debug!(
        "PyTorch model format: {}",
        if is_zip { "ZIP" } else { "Pickle" }
    );

    // Parse model structure (simplified for now)
    let (state_dict_keys, num_parameters, is_full_model) =
        parse_pytorch_structure(&file_data, is_zip)?;

    Ok(PyTorchModelInfo {
        pytorch_version: detect_pytorch_version(&file_data)?,
        model_class: None, // Would be extracted from full model files
        state_dict_keys,
        file_size,
        num_parameters,
        is_full_model,
    })
}

/// Parse PyTorch file structure
fn parse_pytorch_structure(_file_data: &[u8], _is_zip: bool) -> Result<(Vec<String>, u64, bool)> {
    // Simplified parsing - in real implementation would use proper PyTorch parser
    // For now, simulate common layer names

    let common_layers = vec![
        "conv1.weight".to_string(),
        "conv1.bias".to_string(),
        "bn1.weight".to_string(),
        "bn1.running_mean".to_string(),
        "bn1.running_var".to_string(),
        "fc1.weight".to_string(),
        "fc1.bias".to_string(),
        "fc2.weight".to_string(),
        "fc2.bias".to_string(),
    ];

    // Estimate parameter count from file size
    let num_parameters = (_file_data.len() / 4) as u64; // Rough estimate

    Ok((common_layers, num_parameters, false))
}

/// Detect PyTorch version from file
fn detect_pytorch_version(_file_data: &[u8]) -> Result<String> {
    // In real implementation, would parse version from file metadata
    // For now, return a common version
    Ok("2.0.0".to_string())
}

/// Convert PyTorch model to ToRSh model
pub async fn convert_pytorch_to_torsh(
    pytorch_path: &Path,
    device: DeviceType,
) -> Result<TorshModel> {
    info!("Converting PyTorch model to ToRSh format");

    let pytorch_info = parse_pytorch_model(pytorch_path).await?;

    // Build ToRSh model structure from PyTorch state dict
    let (layers, weights) = build_torsh_structure(&pytorch_info, device)?;

    let mut metadata = ModelMetadata::default();
    metadata.format = "torsh".to_string();
    metadata.framework = "pytorch".to_string();
    metadata.description = Some(format!(
        "Converted from PyTorch {} model",
        pytorch_info.pytorch_version
    ));
    metadata.tags = vec!["converted".to_string(), "pytorch".to_string()];

    // Add conversion metadata
    metadata
        .custom
        .insert("original_format".to_string(), serde_json::json!("pytorch"));
    metadata.custom.insert(
        "pytorch_version".to_string(),
        serde_json::json!(pytorch_info.pytorch_version),
    );
    metadata.custom.insert(
        "original_file_size".to_string(),
        serde_json::json!(pytorch_info.file_size),
    );

    Ok(TorshModel {
        layers,
        weights,
        metadata,
    })
}

/// Build ToRSh model structure from PyTorch state dict
fn build_torsh_structure(
    pytorch_info: &PyTorchModelInfo,
    _device: DeviceType,
) -> Result<(Vec<LayerInfo>, HashMap<String, TensorInfo>)> {
    debug!(
        "Building ToRSh structure from {} parameters",
        pytorch_info.num_parameters
    );

    let mut layers = Vec::new();
    let mut weights = HashMap::new();

    // Group parameters by layer
    let layer_groups = group_parameters_by_layer(&pytorch_info.state_dict_keys);

    for (layer_name, param_names) in layer_groups {
        debug!(
            "Processing layer: {} with {} parameters",
            layer_name,
            param_names.len()
        );

        // Infer layer type from parameter names
        let layer_type = PyTorchLayerType::from_param_name(&layer_name);

        // Infer shapes from parameter names
        let (input_shape, output_shape) = infer_layer_shapes(&param_names, layer_type);

        // Count parameters
        let param_count = estimate_layer_parameters(&param_names, layer_type);

        // Create layer info
        let layer = LayerInfo {
            name: layer_name.clone(),
            layer_type: layer_type.to_torsh_type().to_string(),
            input_shape,
            output_shape,
            parameters: param_count,
            trainable: true,
            config: create_layer_config(layer_type),
        };

        layers.push(layer);

        // Create weight tensors
        for param_name in param_names {
            let shape = infer_tensor_shape(&param_name, layer_type);

            let weight_info = TensorInfo {
                name: param_name.clone(),
                shape,
                dtype: DType::F32,
                requires_grad: !param_name.contains("running"), // Running stats are non-trainable
                device: Device::Cpu,
            };

            weights.insert(param_name, weight_info);
        }
    }

    Ok((layers, weights))
}

/// Group parameters by layer name
fn group_parameters_by_layer(param_names: &[String]) -> HashMap<String, Vec<String>> {
    let mut groups: HashMap<String, Vec<String>> = HashMap::new();

    for param_name in param_names {
        // Extract layer name (everything before the last dot)
        let layer_name = if let Some(pos) = param_name.rfind('.') {
            param_name[..pos].to_string()
        } else {
            param_name.clone()
        };

        groups
            .entry(layer_name)
            .or_insert_with(Vec::new)
            .push(param_name.clone());
    }

    groups
}

/// Infer layer shapes from parameter names
fn infer_layer_shapes(
    param_names: &[String],
    layer_type: PyTorchLayerType,
) -> (Vec<usize>, Vec<usize>) {
    // Find weight parameter to infer dimensions
    let weight_param = param_names.iter().find(|name| name.ends_with(".weight"));

    match layer_type {
        PyTorchLayerType::Linear => {
            // Linear layers: weight shape is [out_features, in_features]
            if weight_param.is_some() {
                // Realistic sizes for common architectures
                let input_dim = 512;
                let output_dim = 256;
                (vec![input_dim], vec![output_dim])
            } else {
                (vec![512], vec![256])
            }
        }
        PyTorchLayerType::Conv2d => {
            // Conv2d: input [batch, in_channels, height, width]
            (vec![3, 224, 224], vec![64, 112, 112])
        }
        PyTorchLayerType::BatchNorm2d | PyTorchLayerType::BatchNorm1d => {
            // BatchNorm preserves shape
            (vec![64, 56, 56], vec![64, 56, 56])
        }
        PyTorchLayerType::Embedding => {
            // Embedding: [vocab_size, embedding_dim]
            (vec![30000], vec![512])
        }
        PyTorchLayerType::LSTM | PyTorchLayerType::GRU => {
            // RNN: [seq_len, batch, features]
            (vec![128, 512], vec![128, 256])
        }
        _ => (vec![512], vec![512]),
    }
}

/// Estimate layer parameter count
fn estimate_layer_parameters(param_names: &[String], layer_type: PyTorchLayerType) -> u64 {
    let (input_shape, output_shape) = infer_layer_shapes(param_names, layer_type);

    let input_size: u64 = input_shape.iter().map(|&x| x as u64).product();
    let output_size: u64 = output_shape.iter().map(|&x| x as u64).product();

    match layer_type {
        PyTorchLayerType::Linear => {
            // weight: out * in, bias: out
            input_size * output_size + output_size
        }
        PyTorchLayerType::Conv2d => {
            // Rough estimate based on typical kernel sizes
            let kernel_size = 9; // 3x3
            output_size * kernel_size + output_size // weights + bias
        }
        PyTorchLayerType::BatchNorm2d | PyTorchLayerType::BatchNorm1d => {
            // gamma, beta, running_mean, running_var
            output_size * 4
        }
        PyTorchLayerType::Embedding => input_size * output_size,
        _ => output_size,
    }
}

/// Infer tensor shape from parameter name
fn infer_tensor_shape(param_name: &str, layer_type: PyTorchLayerType) -> Vec<usize> {
    if param_name.ends_with(".weight") {
        match layer_type {
            PyTorchLayerType::Linear => vec![256, 512],
            PyTorchLayerType::Conv2d => vec![64, 3, 3, 3], // [out_ch, in_ch, kH, kW]
            PyTorchLayerType::BatchNorm2d => vec![64],
            PyTorchLayerType::Embedding => vec![30000, 512],
            _ => vec![512, 512],
        }
    } else if param_name.ends_with(".bias") {
        match layer_type {
            PyTorchLayerType::Linear => vec![256],
            PyTorchLayerType::Conv2d => vec![64],
            _ => vec![512],
        }
    } else if param_name.contains("running_mean") || param_name.contains("running_var") {
        vec![64]
    } else {
        vec![512]
    }
}

/// Create layer configuration based on type
fn create_layer_config(layer_type: PyTorchLayerType) -> HashMap<String, serde_json::Value> {
    let mut config = HashMap::new();

    match layer_type {
        PyTorchLayerType::Conv2d => {
            config.insert("kernel_size".to_string(), serde_json::json!(3));
            config.insert("stride".to_string(), serde_json::json!(1));
            config.insert("padding".to_string(), serde_json::json!(1));
        }
        PyTorchLayerType::Dropout => {
            config.insert("p".to_string(), serde_json::json!(0.5));
        }
        PyTorchLayerType::LSTM | PyTorchLayerType::GRU => {
            config.insert("hidden_size".to_string(), serde_json::json!(256));
            config.insert("num_layers".to_string(), serde_json::json!(2));
            config.insert("bidirectional".to_string(), serde_json::json!(false));
        }
        _ => {}
    }

    config
}

/// Map PyTorch tensor to ToRSh tensor (simplified)
pub fn map_pytorch_tensor_to_torsh(
    _pytorch_tensor: &[u8],
    shape: Vec<usize>,
    requires_grad: bool,
    device: DeviceType,
) -> Result<ModelTensor> {
    // In real implementation, would deserialize PyTorch tensor format
    // For now, create a random tensor with the correct shape

    let mut rng = thread_rng();
    let normal = Normal::new(0.0, 0.1)?;

    let num_elements: usize = shape.iter().product();
    let data: Vec<f32> = (0..num_elements)
        .map(|_| normal.sample(&mut rng) as f32)
        .collect();

    ModelTensor::from_data("converted".to_string(), data, shape, requires_grad, device)
}

/// Validate PyTorch to ToRSh conversion
pub fn validate_conversion(
    pytorch_info: &PyTorchModelInfo,
    torsh_model: &TorshModel,
) -> Result<()> {
    info!("Validating PyTorch to ToRSh conversion");

    // Check parameter count is reasonable
    let torsh_params: u64 = torsh_model.layers.iter().map(|l| l.parameters).sum();

    let param_ratio = torsh_params as f64 / pytorch_info.num_parameters as f64;

    if param_ratio < 0.5 || param_ratio > 2.0 {
        warn!(
            "Parameter count mismatch: PyTorch {} vs ToRSh {} (ratio: {:.2})",
            pytorch_info.num_parameters, torsh_params, param_ratio
        );
    }

    // Check all layers have valid shapes
    for layer in &torsh_model.layers {
        if layer.input_shape.is_empty() || layer.output_shape.is_empty() {
            anyhow::bail!("Layer {} has invalid shape", layer.name);
        }
    }

    info!("Conversion validation passed");
    Ok(())
}

/// Export conversion report
pub fn generate_conversion_report(
    pytorch_info: &PyTorchModelInfo,
    torsh_model: &TorshModel,
) -> String {
    let mut report = String::new();

    report.push_str("╔═══════════════════════════════════════════════════════════════════════╗\n");
    report.push_str("║                  PYTORCH → TORSH CONVERSION REPORT                    ║\n");
    report
        .push_str("╚═══════════════════════════════════════════════════════════════════════╝\n\n");

    report.push_str("📦 Source Model (PyTorch)\n");
    report.push_str("━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n");
    report.push_str(&format!(
        "  PyTorch Version:    {}\n",
        pytorch_info.pytorch_version
    ));
    report.push_str(&format!(
        "  File Size:          {:.2} MB\n",
        pytorch_info.file_size as f64 / (1024.0 * 1024.0)
    ));
    report.push_str(&format!(
        "  Parameters:         {}\n",
        pytorch_info.num_parameters
    ));
    report.push_str(&format!(
        "  State Dict Keys:    {}\n",
        pytorch_info.state_dict_keys.len()
    ));
    report.push_str("\n");

    report.push_str("🎯 Target Model (ToRSh)\n");
    report.push_str("━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n");
    let torsh_params: u64 = torsh_model.layers.iter().map(|l| l.parameters).sum();
    report.push_str(&format!(
        "  ToRSh Version:      {}\n",
        torsh_model.metadata.version
    ));
    report.push_str(&format!(
        "  Layers:             {}\n",
        torsh_model.layers.len()
    ));
    report.push_str(&format!("  Parameters:         {}\n", torsh_params));
    report.push_str(&format!(
        "  Tensors:            {}\n",
        torsh_model.weights.len()
    ));
    report.push_str("\n");

    report.push_str("📊 Conversion Statistics\n");
    report.push_str("━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n");
    let param_ratio = torsh_params as f64 / pytorch_info.num_parameters as f64;
    report.push_str(&format!("  Parameter Ratio:    {:.2}\n", param_ratio));
    report.push_str(&format!(
        "  Layers Created:     {}\n",
        torsh_model.layers.len()
    ));

    report.push_str("\n");
    report
}

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

    #[test]
    fn test_layer_type_inference() {
        assert_eq!(
            PyTorchLayerType::from_param_name("model.fc1.weight"),
            PyTorchLayerType::Linear
        );
        assert_eq!(
            PyTorchLayerType::from_param_name("conv1.weight"),
            PyTorchLayerType::Conv2d
        );
        assert_eq!(
            PyTorchLayerType::from_param_name("bn1.running_mean"),
            PyTorchLayerType::BatchNorm2d
        );
    }

    #[test]
    fn test_parameter_grouping() {
        let params = vec![
            "layer1.weight".to_string(),
            "layer1.bias".to_string(),
            "layer2.weight".to_string(),
            "layer2.bias".to_string(),
        ];

        let groups = group_parameters_by_layer(&params);
        assert_eq!(groups.len(), 2);
        assert_eq!(
            groups
                .get("layer1")
                .expect("element retrieval should succeed for valid index")
                .len(),
            2
        );
        assert_eq!(
            groups
                .get("layer2")
                .expect("element retrieval should succeed for valid index")
                .len(),
            2
        );
    }

    #[test]
    fn test_shape_inference() {
        let params = vec!["fc.weight".to_string(), "fc.bias".to_string()];
        let (input, output) = infer_layer_shapes(&params, PyTorchLayerType::Linear);

        assert!(!input.is_empty());
        assert!(!output.is_empty());
    }

    #[test]
    fn test_layer_config_creation() {
        let config = create_layer_config(PyTorchLayerType::Conv2d);
        assert!(config.contains_key("kernel_size"));
        assert!(config.contains_key("stride"));
        assert!(config.contains_key("padding"));
    }
}