ohms-adaptq 2.0.0

use crate::{Result, WeightMatrix};
use crate::model_fetcher::{FetchResult, ModelMetadata, ModelFormat};
use std::path::PathBuf;
use std::collections::HashMap;
use serde::{Deserialize, Serialize};
use std::fs::File;
use std::io::{Read, Seek, SeekFrom};

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelStats {
    pub total_parameters: usize,
    pub total_size_mb: f64,
    pub num_tensors: usize,
    pub largest_tensor: usize,
    pub smallest_tensor: usize,
    pub average_tensor_size: usize,
}

pub struct RealModelLoader;

impl RealModelLoader {
    /// Load model from fetch result and convert to NOVAQ-compatible weights
    pub fn load_model(fetch_result: &FetchResult) -> Result<Vec<WeightMatrix>> {
        match &fetch_result.model_format {
            ModelFormat::SafeTensors => Self::load_safetensors(&fetch_result.local_path),
            ModelFormat::PyTorch => Self::load_pytorch(&fetch_result.local_path),
            ModelFormat::GGUF => Self::load_gguf(&fetch_result.local_path),
            ModelFormat::ONNX => Self::load_onnx(&fetch_result.local_path),
            ModelFormat::Unknown => Err("Unknown model format".into()),
        }
    }

    /// Load SafeTensors format (most common for modern models)
    fn load_safetensors(path: &PathBuf) -> Result<Vec<WeightMatrix>> {
        let mut file = File::open(path)?;
        
        // Read header length (8 bytes)
        let mut header_len_bytes = [0u8; 8];
        file.read_exact(&mut header_len_bytes)?;
        let header_len = u64::from_le_bytes(header_len_bytes) as usize;
        
        // Read JSON header
        let mut header_json = vec![0u8; header_len];
        file.read_exact(&mut header_json)?;
        let header_str = String::from_utf8(header_json)?;
        let header: HashMap<String, serde_json::Value> = serde_json::from_str(&header_str)?;
        
        let mut weights = Vec::new();
        let offset = 8 + header_len as u64;
        
        for (tensor_name, tensor_info) in header {
            if let Some(tensor_obj) = tensor_info.as_object() {
                let dtype = tensor_obj["dtype"].as_str().unwrap_or("F32");
                let shape = tensor_obj["shape"].as_array()
                    .ok_or("Invalid shape")?
                    .iter()
                    .filter_map(|v| v.as_u64().map(|n| n as usize))
                    .collect::<Vec<_>>();
                
                let data_offsets = tensor_obj["data_offsets"].as_array()
                    .ok_or("Invalid data_offsets")?;
                let start_offset = data_offsets[0].as_u64().unwrap_or(0) as u64;
                let end_offset = data_offsets[1].as_u64().unwrap_or(0) as u64;
                let tensor_size = (end_offset - start_offset) as usize;
                
                // Seek to tensor data
                file.seek(SeekFrom::Start(offset + start_offset))?;
                
                // Read tensor data based on dtype
                let tensor_data = match dtype {
                    "F32" => {
                        let mut data = vec![0f32; tensor_size / 4];
                        let mut bytes = vec![0u8; tensor_size];
                        file.read_exact(&mut bytes)?;
                        for (i, chunk) in bytes.chunks(4).enumerate() {
                            if i < data.len() {
                                data[i] = f32::from_le_bytes([chunk[0], chunk[1], chunk[2], chunk[3]]);
                            }
                        }
                        data
                    },
                    "F16" => {
                        let mut data = vec![0f32; tensor_size / 2];
                        let mut bytes = vec![0u8; tensor_size];
                        file.read_exact(&mut bytes)?;
                        for (i, chunk) in bytes.chunks(2).enumerate() {
                            if i < data.len() {
                                let f16_val = u16::from_le_bytes([chunk[0], chunk[1]]);
                                data[i] = half::f16::from_bits(f16_val).to_f32();
                            }
                        }
                        data
                    },
                    _ => return Err(format!("Unsupported dtype: {}", dtype).into()),
                };
                
                // Reshape tensor data
                let total_elements: usize = shape.iter().product();
                if tensor_data.len() != total_elements {
                    return Err(format!("Tensor size mismatch for {}: expected {}, got {}", 
                        tensor_name, total_elements, tensor_data.len()).into());
                }
                
                weights.push(WeightMatrix::new(tensor_data, shape, tensor_name));
            }
        }
        
        Ok(weights)
    }

    /// Load PyTorch format (.bin, .pt, .pth files)
    fn load_pytorch(path: &PathBuf) -> Result<Vec<WeightMatrix>> {
        // For PyTorch, we need to use Python interop or a Rust PyTorch binding
        // For now, we'll use a simplified approach that works with common formats
        
        let mut file = File::open(path)?;
        let mut buffer = Vec::new();
        file.read_to_end(&mut buffer)?;
        
        // Try to parse as a simple binary format
        // This is a simplified implementation - in production you'd want proper PyTorch parsing
        let mut weights = Vec::new();
        
        // Look for tensor markers in the binary data
        // This is a basic heuristic - real implementation would need proper PyTorch format parsing
        let mut pos = 0;
        while pos < buffer.len() {
            // Look for potential tensor headers
            if pos + 8 < buffer.len() {
                // Check for potential tensor size markers
                let potential_size = u64::from_le_bytes([
                    buffer[pos], buffer[pos+1], buffer[pos+2], buffer[pos+3],
                    buffer[pos+4], buffer[pos+5], buffer[pos+6], buffer[pos+7]
                ]);
                
                if potential_size > 0 && potential_size < 1_000_000_000 { // Reasonable size check
                    // Try to extract tensor data
                    let tensor_size = potential_size as usize;
                    if pos + 8 + tensor_size * 4 <= buffer.len() {
                        let mut tensor_data = vec![0f32; tensor_size];
                        for i in 0..tensor_size {
                            let byte_pos = pos + 8 + i * 4;
                            if byte_pos + 3 < buffer.len() {
                                tensor_data[i] = f32::from_le_bytes([
                                    buffer[byte_pos], buffer[byte_pos+1], 
                                    buffer[byte_pos+2], buffer[byte_pos+3]
                                ]);
                            }
                        }
                        
                        // Create a simple 2D shape (this is simplified)
                        let dim = (tensor_size as f64).sqrt() as usize;
                        let shape = vec![dim, dim];
                        
                        weights.push(WeightMatrix::new(
                            tensor_data,
                            shape,
                            format!("tensor_{}", weights.len())
                        ));
                        
                        pos += 8 + tensor_size * 4;
                        continue;
                    }
                }
            }
            pos += 1;
        }
        
        if weights.is_empty() {
            return Err("Could not extract tensors from PyTorch file".into());
        }
        
        Ok(weights)
    }

    /// Load GGUF format (Ollama models)
    fn load_gguf(path: &PathBuf) -> Result<Vec<WeightMatrix>> {
        let mut file = File::open(path)?;
        
        // Read GGUF header
        let mut magic = [0u8; 4];
        file.read_exact(&mut magic)?;
        if &magic != b"GGUF" {
            return Err("Invalid GGUF magic number".into());
        }
        
        let mut version = [0u8; 4];
        file.read_exact(&mut version)?;
        let _version_num = u32::from_le_bytes(version);
        
        let mut tensor_count = [0u8; 8];
        file.read_exact(&mut tensor_count)?;
        let num_tensors = u64::from_le_bytes(tensor_count);
        
        let mut metadata_size = [0u8; 8];
        file.read_exact(&mut metadata_size)?;
        let metadata_len = u64::from_le_bytes(metadata_size) as usize;
        
        // Skip metadata for now (we'll implement proper parsing later)
        file.seek(SeekFrom::Current(metadata_len as i64))?;
        
        let mut weights = Vec::new();
        
        // Read tensors
        for _i in 0..num_tensors {
            // Read tensor name length
            let mut name_len = [0u8; 4];
            file.read_exact(&mut name_len)?;
            let name_length = u32::from_le_bytes(name_len) as usize;
            
            // Read tensor name
            let mut name_bytes = vec![0u8; name_length];
            file.read_exact(&mut name_bytes)?;
            let tensor_name = String::from_utf8(name_bytes)?;
            
            // Read tensor dimensions
            let mut dims = [0u8; 4];
            file.read_exact(&mut dims)?;
            let num_dims = u32::from_le_bytes(dims) as usize;
            
            let mut shape = Vec::new();
            for _ in 0..num_dims {
                let mut dim = [0u8; 8];
                file.read_exact(&mut dim)?;
                shape.push(u64::from_le_bytes(dim) as usize);
            }
            
            // Read tensor type
            let mut tensor_type = [0u8; 4];
            file.read_exact(&mut tensor_type)?;
            let dtype = u32::from_le_bytes(tensor_type);
            
            // Read tensor offset
            let mut offset = [0u8; 8];
            file.read_exact(&mut offset)?;
            let tensor_offset = u64::from_le_bytes(offset);
            
            // Calculate tensor size
            let total_elements: usize = shape.iter().product();
            let bytes_per_element = match dtype {
                0 => 4, // F32
                1 => 2, // F16
                _ => 4, // Default to F32
            };
            let tensor_size = total_elements * bytes_per_element;
            
            // Store current position
            let current_pos = file.stream_position()?;
            
            // Seek to tensor data
            file.seek(SeekFrom::Start(tensor_offset))?;
            
            // Read tensor data
            let mut tensor_data = vec![0f32; total_elements];
            match dtype {
                0 => { // F32
                    let mut bytes = vec![0u8; tensor_size];
                    file.read_exact(&mut bytes)?;
                    for (i, chunk) in bytes.chunks(4).enumerate() {
                        if i < tensor_data.len() {
                            tensor_data[i] = f32::from_le_bytes([chunk[0], chunk[1], chunk[2], chunk[3]]);
                        }
                    }
                },
                1 => { // F16
                    let mut bytes = vec![0u8; tensor_size];
                    file.read_exact(&mut bytes)?;
                    for (i, chunk) in bytes.chunks(2).enumerate() {
                        if i < tensor_data.len() {
                            let f16_val = u16::from_le_bytes([chunk[0], chunk[1]]);
                            tensor_data[i] = half::f16::from_bits(f16_val).to_f32();
                        }
                    }
                },
                _ => {
                    // Default to F32
                    let mut bytes = vec![0u8; tensor_size];
                    file.read_exact(&mut bytes)?;
                    for (i, chunk) in bytes.chunks(4).enumerate() {
                        if i < tensor_data.len() {
                            tensor_data[i] = f32::from_le_bytes([chunk[0], chunk[1], chunk[2], chunk[3]]);
                        }
                    }
                }
            }
            
            weights.push(WeightMatrix::new(tensor_data, shape, tensor_name));
            
            // Return to metadata position
            file.seek(SeekFrom::Start(current_pos))?;
        }
        
        Ok(weights)
    }

    /// Load ONNX format
    fn load_onnx(path: &PathBuf) -> Result<Vec<WeightMatrix>> {
        // ONNX is a complex format that requires proper parsing
        // For now, we'll implement a basic version that can handle simple ONNX files
        
        let mut file = File::open(path)?;
        let mut buffer = Vec::new();
        file.read_to_end(&mut buffer)?;
        
        // Check ONNX magic number
        if buffer.len() < 8 || &buffer[0..8] != b"\x08\x01\x12\x07onnx\x1d" {
            return Err("Invalid ONNX file format".into());
        }
        
        // This is a simplified ONNX parser
        // Real implementation would use proper ONNX parsing libraries
        let mut weights = Vec::new();
        
        // Look for weight tensors in the ONNX file
        // This is a basic heuristic - production code would need proper ONNX parsing
        let mut pos = 0;
        while pos < buffer.len() {
            // Look for tensor data patterns
            if pos + 16 < buffer.len() {
                // Check for potential tensor markers
                let potential_size = u64::from_le_bytes([
                    buffer[pos], buffer[pos+1], buffer[pos+2], buffer[pos+3],
                    buffer[pos+4], buffer[pos+5], buffer[pos+6], buffer[pos+7]
                ]);
                
                if potential_size > 0 && potential_size < 100_000_000 { // Reasonable size
                    let tensor_size = potential_size as usize;
                    if pos + 16 + tensor_size * 4 <= buffer.len() {
                        let mut tensor_data = vec![0f32; tensor_size];
                        for i in 0..tensor_size {
                            let byte_pos = pos + 16 + i * 4;
                            if byte_pos + 3 < buffer.len() {
                                tensor_data[i] = f32::from_le_bytes([
                                    buffer[byte_pos], buffer[byte_pos+1], 
                                    buffer[byte_pos+2], buffer[byte_pos+3]
                                ]);
                            }
                        }
                        
                        // Create a simple 2D shape
                        let dim = (tensor_size as f64).sqrt() as usize;
                        let shape = vec![dim, dim];
                        
                        weights.push(WeightMatrix::new(
                            tensor_data,
                            shape,
                            format!("onnx_tensor_{}", weights.len())
                        ));
                        
                        pos += 16 + tensor_size * 4;
                        continue;
                    }
                }
            }
            pos += 1;
        }
        
        if weights.is_empty() {
            return Err("Could not extract tensors from ONNX file".into());
        }
        
        Ok(weights)
    }

    /// Get model metadata from fetch result
    pub fn get_metadata(fetch_result: &FetchResult) -> Option<&ModelMetadata> {
        fetch_result.metadata.as_ref()
    }

    /// Validate model format and compatibility
    pub fn validate_model(fetch_result: &FetchResult) -> Result<bool> {
        match &fetch_result.model_format {
            ModelFormat::SafeTensors => {
                // Validate SafeTensors header
                let mut file = File::open(&fetch_result.local_path)?;
                let mut magic = [0u8; 15];
                file.read_exact(&mut magic)?;
                Ok(&magic == b"__safetensors__")
            },
            ModelFormat::PyTorch => {
                // Basic PyTorch validation
                let mut file = File::open(&fetch_result.local_path)?;
                let mut header = [0u8; 8];
                file.read_exact(&mut header)?;
                // Check for PyTorch magic (simplified)
                Ok(true) // Assume valid for now
            },
            ModelFormat::GGUF => {
                // Validate GGUF header
                let mut file = File::open(&fetch_result.local_path)?;
                let mut magic = [0u8; 4];
                file.read_exact(&mut magic)?;
                Ok(&magic == b"GGUF")
            },
            ModelFormat::ONNX => {
                // Validate ONNX header
                let mut file = File::open(&fetch_result.local_path)?;
                let mut header = [0u8; 9];
                file.read_exact(&mut header)?;
                Ok(&header == b"\x08\x01\x12\x07onnx\x1d")
            },
            ModelFormat::Unknown => Ok(false),
        }
    }

    /// Get model statistics
    pub fn get_model_stats(weights: &[WeightMatrix]) -> ModelStats {
        let total_parameters: usize = weights.iter()
            .map(|w| w.data.len())
            .sum();
        
        let total_size_bytes = total_parameters * 4; // f32 = 4 bytes
        
        let largest_tensor = weights.iter()
            .max_by_key(|w| w.data.len())
            .map(|w| w.data.len())
            .unwrap_or(0);
        
        let smallest_tensor = weights.iter()
            .min_by_key(|w| w.data.len())
            .map(|w| w.data.len())
            .unwrap_or(0);
        
        ModelStats {
            total_parameters,
            total_size_mb: total_size_bytes as f64 / (1024.0 * 1024.0),
            num_tensors: weights.len(),
            largest_tensor,
            smallest_tensor,
            average_tensor_size: if weights.is_empty() { 0 } else { total_parameters / weights.len() },
        }
    }
}