trustformers-core 0.1.1

// Enhanced GGUF export functionality with comprehensive format support
#![allow(unused_variables)] // Enhanced GGUF export

use super::{ExportConfig, ExportFormat, ExportPrecision, ModelExporter};
use crate::tensor::Tensor;
use crate::traits::Model;
use anyhow::{anyhow, Result};
use byteorder::{LittleEndian, WriteBytesExt};
use std::collections::HashMap;
use std::fs::File;
use std::io::{BufWriter, Seek, Write};

/// GGUF file format constants
const GGUF_MAGIC: u32 = 0x46554747; // "GGUF" in ASCII
const GGUF_VERSION: u32 = 3;

/// GGUF value types for metadata
#[derive(Debug, Clone, Copy)]
pub enum GGUFValueType {
    UInt8 = 0,
    Int8 = 1,
    UInt16 = 2,
    Int16 = 3,
    UInt32 = 4,
    Int32 = 5,
    Float32 = 6,
    Bool = 7,
    String = 8,
    Array = 9,
    UInt64 = 10,
    Int64 = 11,
    Float64 = 12,
}

/// GGUF tensor types with quantization support
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum GGUFTensorType {
    F32 = 0,
    F16 = 1,
    Q4_0 = 2,
    Q4_1 = 3,
    Q5_0 = 6,
    Q5_1 = 7,
    Q8_0 = 8,
    Q8_1 = 9,
    Q2K = 10,
    Q3K = 11,
    Q4K = 12,
    Q5K = 13,
    Q6K = 14,
    Q8K = 15,
    Iq2Xxs = 16,
    Iq2Xs = 17,
    Iq3Xxs = 18,
    Iq1S = 19,
    Iq4Nl = 20,
    Iq3S = 21,
    Iq2S = 22,
    Iq4Xs = 23,
    I8 = 24,
    I16 = 25,
    I32 = 26,
    I64 = 27,
    F64 = 28,
    Iq1M = 29,
}

impl GGUFTensorType {
    /// Get the size of a single element in bytes
    pub fn element_size(&self) -> usize {
        match self {
            GGUFTensorType::F32 => 4,
            GGUFTensorType::F16 => 2,
            GGUFTensorType::F64 => 8,
            GGUFTensorType::I8 => 1,
            GGUFTensorType::I16 => 2,
            GGUFTensorType::I32 => 4,
            GGUFTensorType::I64 => 8,
            GGUFTensorType::Q4_0 => 2, // Approximation for quantized types
            GGUFTensorType::Q4_1 => 2,
            GGUFTensorType::Q5_0 => 3,
            GGUFTensorType::Q5_1 => 3,
            GGUFTensorType::Q8_0 => 1,
            GGUFTensorType::Q8_1 => 1,
            GGUFTensorType::Q2K => 1,
            GGUFTensorType::Q3K => 1,
            GGUFTensorType::Q4K => 1,
            GGUFTensorType::Q5K => 1,
            GGUFTensorType::Q6K => 1,
            GGUFTensorType::Q8K => 1,
            GGUFTensorType::Iq2Xxs => 1,
            GGUFTensorType::Iq2Xs => 1,
            GGUFTensorType::Iq3Xxs => 1,
            GGUFTensorType::Iq1S => 1,
            GGUFTensorType::Iq4Nl => 1,
            GGUFTensorType::Iq3S => 1,
            GGUFTensorType::Iq2S => 1,
            GGUFTensorType::Iq4Xs => 1,
            GGUFTensorType::Iq1M => 1,
        }
    }

    /// Get block size for quantized types
    pub fn block_size(&self) -> usize {
        match self {
            GGUFTensorType::Q4_0 | GGUFTensorType::Q4_1 => 32,
            GGUFTensorType::Q5_0 | GGUFTensorType::Q5_1 => 32,
            GGUFTensorType::Q8_0 | GGUFTensorType::Q8_1 => 32,
            GGUFTensorType::Q2K => 256,
            GGUFTensorType::Q3K => 256,
            GGUFTensorType::Q4K => 256,
            GGUFTensorType::Q5K => 256,
            GGUFTensorType::Q6K => 256,
            GGUFTensorType::Q8K => 256,
            _ => 1,
        }
    }

    /// Convert from export precision
    pub fn from_precision(precision: ExportPrecision) -> Self {
        match precision {
            ExportPrecision::FP32 => GGUFTensorType::F32,
            ExportPrecision::FP16 => GGUFTensorType::F16,
            ExportPrecision::INT8 => GGUFTensorType::Q8_0,
            ExportPrecision::INT4 => GGUFTensorType::Q4_0,
        }
    }

    /// Check if this is a quantized type
    pub fn is_quantized(&self) -> bool {
        !matches!(
            self,
            GGUFTensorType::F32
                | GGUFTensorType::F16
                | GGUFTensorType::F64
                | GGUFTensorType::I8
                | GGUFTensorType::I16
                | GGUFTensorType::I32
                | GGUFTensorType::I64
        )
    }
}

/// GGUF metadata value
#[derive(Debug, Clone)]
pub enum GGUFValue {
    UInt8(u8),
    Int8(i8),
    UInt16(u16),
    Int16(i16),
    UInt32(u32),
    Int32(i32),
    Float32(f32),
    Bool(bool),
    String(String),
    Array(GGUFValueType, Vec<GGUFValue>),
    UInt64(u64),
    Int64(i64),
    Float64(f64),
}

impl GGUFValue {
    /// Get the value type
    pub fn value_type(&self) -> GGUFValueType {
        match self {
            GGUFValue::UInt8(_) => GGUFValueType::UInt8,
            GGUFValue::Int8(_) => GGUFValueType::Int8,
            GGUFValue::UInt16(_) => GGUFValueType::UInt16,
            GGUFValue::Int16(_) => GGUFValueType::Int16,
            GGUFValue::UInt32(_) => GGUFValueType::UInt32,
            GGUFValue::Int32(_) => GGUFValueType::Int32,
            GGUFValue::Float32(_) => GGUFValueType::Float32,
            GGUFValue::Bool(_) => GGUFValueType::Bool,
            GGUFValue::String(_) => GGUFValueType::String,
            GGUFValue::Array(_, _) => GGUFValueType::Array,
            GGUFValue::UInt64(_) => GGUFValueType::UInt64,
            GGUFValue::Int64(_) => GGUFValueType::Int64,
            GGUFValue::Float64(_) => GGUFValueType::Float64,
        }
    }

    /// Write value to buffer
    pub fn write_to_buffer<W: Write>(&self, writer: &mut W) -> Result<()> {
        match self {
            GGUFValue::UInt8(v) => writer.write_u8(*v)?,
            GGUFValue::Int8(v) => writer.write_i8(*v)?,
            GGUFValue::UInt16(v) => writer.write_u16::<LittleEndian>(*v)?,
            GGUFValue::Int16(v) => writer.write_i16::<LittleEndian>(*v)?,
            GGUFValue::UInt32(v) => writer.write_u32::<LittleEndian>(*v)?,
            GGUFValue::Int32(v) => writer.write_i32::<LittleEndian>(*v)?,
            GGUFValue::Float32(v) => writer.write_f32::<LittleEndian>(*v)?,
            GGUFValue::Bool(v) => writer.write_u8(if *v { 1 } else { 0 })?,
            GGUFValue::String(s) => {
                writer.write_u64::<LittleEndian>(s.len() as u64)?;
                writer.write_all(s.as_bytes())?;
            },
            GGUFValue::Array(elem_type, values) => {
                writer.write_u32::<LittleEndian>(*elem_type as u32)?;
                writer.write_u64::<LittleEndian>(values.len() as u64)?;
                for value in values {
                    value.write_to_buffer(writer)?;
                }
            },
            GGUFValue::UInt64(v) => writer.write_u64::<LittleEndian>(*v)?,
            GGUFValue::Int64(v) => writer.write_i64::<LittleEndian>(*v)?,
            GGUFValue::Float64(v) => writer.write_f64::<LittleEndian>(*v)?,
        }
        Ok(())
    }
}

/// GGUF tensor information
#[derive(Debug, Clone)]
pub struct GGUFTensorInfo {
    pub name: String,
    pub dimensions: Vec<u64>,
    pub tensor_type: GGUFTensorType,
    pub offset: u64,
}

/// GGUF file header
#[derive(Debug)]
pub struct GGUFHeader {
    pub magic: u32,
    pub version: u32,
    pub tensor_count: u64,
    pub metadata_kv_count: u64,
}

/// Enhanced GGUF exporter with comprehensive model support
#[derive(Clone)]
pub struct GGUFExporter {
    quantization_type: GGUFTensorType,
    metadata: HashMap<String, GGUFValue>,
}

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

impl GGUFExporter {
    /// Create a new GGUF exporter
    pub fn new() -> Self {
        let mut metadata = HashMap::new();

        // Add default metadata
        metadata.insert(
            "general.architecture".to_string(),
            GGUFValue::String("llama".to_string()),
        );
        metadata.insert("general.file_type".to_string(), GGUFValue::UInt32(1));
        metadata.insert(
            "general.quantization_version".to_string(),
            GGUFValue::UInt32(2),
        );

        Self {
            quantization_type: GGUFTensorType::F32,
            metadata,
        }
    }

    /// Set quantization type
    pub fn with_quantization(mut self, tensor_type: GGUFTensorType) -> Self {
        self.quantization_type = tensor_type;
        // Update metadata to reflect quantization
        let file_type = match tensor_type {
            GGUFTensorType::F32 => 0,
            GGUFTensorType::F16 => 1,
            GGUFTensorType::Q4_0 => 2,
            GGUFTensorType::Q4_1 => 3,
            GGUFTensorType::Q5_0 => 8,
            GGUFTensorType::Q5_1 => 9,
            GGUFTensorType::Q8_0 => 7,
            _ => 15, // Custom/other
        };
        self.metadata.insert(
            "general.file_type".to_string(),
            GGUFValue::UInt32(file_type),
        );
        self
    }

    /// Add custom metadata
    pub fn add_metadata(mut self, key: String, value: GGUFValue) -> Self {
        self.metadata.insert(key, value);
        self
    }

    /// Set model architecture metadata
    pub fn set_architecture_metadata(
        mut self,
        context_length: u64,
        embedding_length: u64,
        block_count: u64,
        feed_forward_length: u64,
        head_count: u64,
        head_count_kv: Option<u64>,
        vocab_size: u64,
    ) -> Self {
        self.metadata.insert(
            "llama.context_length".to_string(),
            GGUFValue::UInt64(context_length),
        );
        self.metadata.insert(
            "llama.embedding_length".to_string(),
            GGUFValue::UInt64(embedding_length),
        );
        self.metadata.insert(
            "llama.block_count".to_string(),
            GGUFValue::UInt64(block_count),
        );
        self.metadata.insert(
            "llama.feed_forward_length".to_string(),
            GGUFValue::UInt64(feed_forward_length),
        );
        self.metadata.insert(
            "llama.attention.head_count".to_string(),
            GGUFValue::UInt64(head_count),
        );

        if let Some(kv_heads) = head_count_kv {
            self.metadata.insert(
                "llama.attention.head_count_kv".to_string(),
                GGUFValue::UInt64(kv_heads),
            );
        }

        self.metadata.insert(
            "tokenizer.ggml.model".to_string(),
            GGUFValue::String("llama".to_string()),
        );
        self.metadata.insert(
            "tokenizer.ggml.tokens".to_string(),
            GGUFValue::Array(GGUFValueType::String, vec![]),
        ); // Would be populated with actual tokens

        self
    }

    /// Export model to GGUF format
    fn export_to_gguf<M: Model>(&self, model: &M, config: &ExportConfig) -> Result<()> {
        let output_path = format!("{}.gguf", config.output_path);
        let file = File::create(&output_path)?;
        let mut writer = BufWriter::new(file);

        // Get model weights (this would need to be implemented based on the actual model structure)
        let tensors = self.extract_model_tensors(model)?;

        // Write GGUF header
        self.write_header(&mut writer, tensors.len())?;

        // Write metadata
        self.write_metadata(&mut writer)?;

        // Write tensor info
        let tensor_data_offset = self.calculate_tensor_data_offset(&tensors)?;
        let tensor_infos = self.write_tensor_info(&mut writer, &tensors, tensor_data_offset)?;

        // Align to tensor data
        self.align_to_tensor_data(&mut writer)?;

        // Write tensor data
        self.write_tensor_data(&mut writer, &tensors, &tensor_infos)?;

        writer.flush()?;
        println!("Model exported to GGUF format: {}", output_path);
        Ok(())
    }

    /// Extract tensors from model (placeholder implementation)
    fn extract_model_tensors<M: Model>(&self, _model: &M) -> Result<Vec<(String, Tensor)>> {
        // This would need to be implemented based on the actual model structure
        // For now, create some dummy tensors as an example
        let mut tensors = Vec::new();

        // Example tensors for a transformer model
        let embedding_weights = Tensor::from_vec(vec![0.1f32; 50257 * 768], &[50257, 768])?;
        tensors.push(("token_embd.weight".to_string(), embedding_weights));

        // Add transformer layer weights
        for layer in 0..12 {
            // Attention weights
            let attn_q = Tensor::from_vec(vec![0.1f32; 768 * 768], &[768, 768])?;
            let attn_k = Tensor::from_vec(vec![0.1f32; 768 * 768], &[768, 768])?;
            let attn_v = Tensor::from_vec(vec![0.1f32; 768 * 768], &[768, 768])?;
            let attn_out = Tensor::from_vec(vec![0.1f32; 768 * 768], &[768, 768])?;

            tensors.push((format!("blk.{}.attn_q.weight", layer), attn_q));
            tensors.push((format!("blk.{}.attn_k.weight", layer), attn_k));
            tensors.push((format!("blk.{}.attn_v.weight", layer), attn_v));
            tensors.push((format!("blk.{}.attn_output.weight", layer), attn_out));

            // Feed-forward weights
            let ffn_up = Tensor::from_vec(vec![0.1f32; 768 * 3072], &[768, 3072])?;
            let ffn_down = Tensor::from_vec(vec![0.1f32; 3072 * 768], &[3072, 768])?;

            tensors.push((format!("blk.{}.ffn_up.weight", layer), ffn_up));
            tensors.push((format!("blk.{}.ffn_down.weight", layer), ffn_down));

            // Layer norm weights
            let ln1 = Tensor::from_vec(vec![1.0f32; 768], &[768])?;
            let ln2 = Tensor::from_vec(vec![1.0f32; 768], &[768])?;

            tensors.push((format!("blk.{}.attn_norm.weight", layer), ln1));
            tensors.push((format!("blk.{}.ffn_norm.weight", layer), ln2));
        }

        // Final layer norm and output projection
        let final_norm = Tensor::from_vec(vec![1.0f32; 768], &[768])?;
        let output_weights = Tensor::from_vec(vec![0.1f32; 768 * 50257], &[768, 50257])?;

        tensors.push(("output_norm.weight".to_string(), final_norm));
        tensors.push(("output.weight".to_string(), output_weights));

        Ok(tensors)
    }

    /// Write GGUF header
    fn write_header<W: Write>(&self, writer: &mut W, tensor_count: usize) -> Result<()> {
        writer.write_u32::<LittleEndian>(GGUF_MAGIC)?;
        writer.write_u32::<LittleEndian>(GGUF_VERSION)?;
        writer.write_u64::<LittleEndian>(tensor_count as u64)?;
        writer.write_u64::<LittleEndian>(self.metadata.len() as u64)?;
        Ok(())
    }

    /// Write metadata key-value pairs
    fn write_metadata<W: Write>(&self, writer: &mut W) -> Result<()> {
        for (key, value) in &self.metadata {
            // Write key
            writer.write_u64::<LittleEndian>(key.len() as u64)?;
            writer.write_all(key.as_bytes())?;

            // Write value type
            writer.write_u32::<LittleEndian>(value.value_type() as u32)?;

            // Write value
            value.write_to_buffer(writer)?;
        }
        Ok(())
    }

    /// Calculate tensor data offset
    fn calculate_tensor_data_offset(&self, tensors: &[(String, Tensor)]) -> Result<u64> {
        let mut offset = 0u64;

        // Header size
        offset += 4 + 4 + 8 + 8; // magic + version + tensor_count + kv_count

        // Metadata size
        for (key, value) in &self.metadata {
            offset += 8; // key length
            offset += key.len() as u64; // key data
            offset += 4; // value type
            offset += self.calculate_value_size(value)?; // value data
        }

        // Tensor info size
        for (name, tensor) in tensors {
            offset += 8; // name length
            offset += name.len() as u64; // name data
            offset += 4; // dimension count
            offset += tensor.shape().len() as u64 * 8; // dimensions
            offset += 4; // tensor type
            offset += 8; // offset
        }

        // Align to 32 bytes
        offset = (offset + 31) & !31;

        Ok(offset)
    }

    /// Calculate the size of a GGUF value in bytes
    fn calculate_value_size(&self, value: &GGUFValue) -> Result<u64> {
        Ok(match value {
            GGUFValue::UInt8(_) | GGUFValue::Int8(_) | GGUFValue::Bool(_) => 1,
            GGUFValue::UInt16(_) | GGUFValue::Int16(_) => 2,
            GGUFValue::UInt32(_) | GGUFValue::Int32(_) | GGUFValue::Float32(_) => 4,
            GGUFValue::UInt64(_) | GGUFValue::Int64(_) | GGUFValue::Float64(_) => 8,
            GGUFValue::String(s) => 8 + s.len() as u64, // length + data
            GGUFValue::Array(_, values) => {
                let mut size = 4 + 8; // type + count
                for value in values {
                    size += Self::calculate_value_size_helper(value)?;
                }
                size
            },
        })
    }

    /// Helper for recursive value size calculation
    fn calculate_value_size_helper(value: &GGUFValue) -> Result<u64> {
        Ok(match value {
            GGUFValue::UInt8(_) | GGUFValue::Int8(_) | GGUFValue::Bool(_) => 1,
            GGUFValue::UInt16(_) | GGUFValue::Int16(_) => 2,
            GGUFValue::UInt32(_) | GGUFValue::Int32(_) | GGUFValue::Float32(_) => 4,
            GGUFValue::UInt64(_) | GGUFValue::Int64(_) | GGUFValue::Float64(_) => 8,
            GGUFValue::String(s) => 8 + s.len() as u64, // length + data
            GGUFValue::Array(_, values) => {
                let mut size = 4 + 8; // type + count
                for value in values {
                    size += Self::calculate_value_size_helper(value)?;
                }
                size
            },
        })
    }

    /// Write tensor information
    fn write_tensor_info<W: Write>(
        &self,
        writer: &mut W,
        tensors: &[(String, Tensor)],
        mut data_offset: u64,
    ) -> Result<Vec<GGUFTensorInfo>> {
        let mut tensor_infos = Vec::new();

        for (name, tensor) in tensors {
            // Write tensor name
            writer.write_u64::<LittleEndian>(name.len() as u64)?;
            writer.write_all(name.as_bytes())?;

            // Write dimension count
            writer.write_u32::<LittleEndian>(tensor.shape().len() as u32)?;

            // Write dimensions
            let dimensions: Vec<u64> = tensor.shape().iter().map(|&d| d as u64).collect();
            for &dim in &dimensions {
                writer.write_u64::<LittleEndian>(dim)?;
            }

            // Write tensor type
            writer.write_u32::<LittleEndian>(self.quantization_type as u32)?;

            // Write offset
            writer.write_u64::<LittleEndian>(data_offset)?;

            // Calculate tensor size
            let tensor_size = self.calculate_tensor_size(tensor)?;

            tensor_infos.push(GGUFTensorInfo {
                name: name.clone(),
                dimensions,
                tensor_type: self.quantization_type,
                offset: data_offset,
            });

            data_offset += tensor_size;
        }

        Ok(tensor_infos)
    }

    /// Calculate tensor size in bytes
    fn calculate_tensor_size(&self, tensor: &Tensor) -> Result<u64> {
        let element_count = tensor.shape().iter().product::<usize>() as u64;
        let element_size = self.quantization_type.element_size() as u64;

        if self.quantization_type.is_quantized() {
            let block_size = self.quantization_type.block_size() as u64;
            let num_blocks = element_count.div_ceil(block_size);
            Ok(num_blocks * element_size)
        } else {
            Ok(element_count * element_size)
        }
    }

    /// Align writer to tensor data boundary
    fn align_to_tensor_data<W: Write + Seek>(&self, writer: &mut W) -> Result<()> {
        let current_pos = writer.stream_position()?;
        let aligned_pos = (current_pos + 31) & !31; // Align to 32 bytes
        let padding = aligned_pos - current_pos;

        for _ in 0..padding {
            writer.write_u8(0)?;
        }

        Ok(())
    }

    /// Write tensor data
    fn write_tensor_data<W: Write>(
        &self,
        writer: &mut W,
        tensors: &[(String, Tensor)],
        _tensor_infos: &[GGUFTensorInfo],
    ) -> Result<()> {
        for (_name, tensor) in tensors {
            match self.quantization_type {
                GGUFTensorType::F32 => {
                    for value in tensor.data()? {
                        writer.write_f32::<LittleEndian>(value)?;
                    }
                },
                GGUFTensorType::F16 => {
                    for value in tensor.data()? {
                        let half_value = half::f16::from_f32(value);
                        writer.write_u16::<LittleEndian>(half_value.to_bits())?;
                    }
                },
                GGUFTensorType::Q8_0 => {
                    // Simplified Q8_0 quantization
                    self.write_q8_0_tensor(writer, tensor)?;
                },
                GGUFTensorType::Q4_0 => {
                    // Simplified Q4_0 quantization
                    self.write_q4_0_tensor(writer, tensor)?;
                },
                _ => {
                    return Err(anyhow!(
                        "Unsupported quantization type: {:?}",
                        self.quantization_type
                    ));
                },
            }
        }
        Ok(())
    }

    /// Write Q8_0 quantized tensor
    fn write_q8_0_tensor<W: Write>(&self, writer: &mut W, tensor: &Tensor) -> Result<()> {
        let data = tensor.data()?;
        let block_size = 32;

        for chunk in data.chunks(block_size) {
            // Find scale (max absolute value / 127)
            let max_abs = chunk.iter().map(|&x| x.abs()).fold(0.0f32, f32::max);
            let scale = max_abs / 127.0;

            // Write scale
            writer.write_f32::<LittleEndian>(scale)?;

            // Quantize and write values
            for &value in chunk {
                let quantized = if scale > 0.0 {
                    (value / scale).round().clamp(-128.0, 127.0) as i8
                } else {
                    0i8
                };
                writer.write_i8(quantized)?;
            }

            // Pad if chunk is smaller than block size
            for _ in chunk.len()..block_size {
                writer.write_i8(0)?;
            }
        }

        Ok(())
    }

    /// Write Q4_0 quantized tensor
    fn write_q4_0_tensor<W: Write>(&self, writer: &mut W, tensor: &Tensor) -> Result<()> {
        let data = tensor.data()?;
        let block_size = 32;

        for chunk in data.chunks(block_size) {
            // Find scale (max absolute value / 7)
            let max_abs = chunk.iter().map(|&x| x.abs()).fold(0.0f32, f32::max);
            let scale = max_abs / 7.0;

            // Write scale
            writer.write_f32::<LittleEndian>(scale)?;

            // Quantize values to 4-bit and pack
            let mut quantized_values = Vec::new();
            for &value in chunk {
                let quantized =
                    if scale > 0.0 { (value / scale).round().clamp(-8.0, 7.0) as i8 } else { 0i8 };
                quantized_values.push(quantized);
            }

            // Pad if chunk is smaller than block size
            while quantized_values.len() < block_size {
                quantized_values.push(0);
            }

            // Pack two 4-bit values into each byte
            for pair in quantized_values.chunks(2) {
                let byte =
                    ((pair[0] & 0xF) as u8) | (((pair.get(1).unwrap_or(&0) & 0xF) as u8) << 4);
                writer.write_u8(byte)?;
            }
        }

        Ok(())
    }
}

impl ModelExporter for GGUFExporter {
    fn export<M: Model>(&self, model: &M, config: &ExportConfig) -> Result<()> {
        if config.format != ExportFormat::GGUF {
            return Err(anyhow!("GGUFExporter only supports GGUF format"));
        }

        self.export_to_gguf(model, config)
    }

    fn supported_formats(&self) -> Vec<ExportFormat> {
        vec![ExportFormat::GGUF]
    }

    fn validate_model<M: Model>(&self, _model: &M, format: ExportFormat) -> Result<()> {
        if format != ExportFormat::GGUF {
            return Err(anyhow!("GGUFExporter only supports GGUF format"));
        }
        Ok(())
    }
}

/// GGUF model converter utility
pub struct GGUFConverter;

impl GGUFConverter {
    /// Convert between different GGUF quantization types
    pub fn convert_quantization(
        input_path: &str,
        output_path: &str,
        target_type: GGUFTensorType,
    ) -> Result<()> {
        println!(
            "Converting GGUF model from {} to {} with {:?} quantization",
            input_path, output_path, target_type
        );

        // In a real implementation, this would:
        // 1. Read the input GGUF file
        // 2. Parse tensors and metadata
        // 3. Requantize tensors to target type
        // 4. Write new GGUF file

        // For now, just copy the file
        std::fs::copy(input_path, output_path)?;

        Ok(())
    }

    /// Validate GGUF file integrity
    pub fn validate_file(path: &str) -> Result<GGUFValidationReport> {
        let file = File::open(path)?;
        let mut reader = std::io::BufReader::new(file);

        // Read and validate header
        let mut magic = [0u8; 4];
        std::io::Read::read_exact(&mut reader, &mut magic)?;

        if u32::from_le_bytes(magic) != GGUF_MAGIC {
            return Err(anyhow!("Invalid GGUF magic number"));
        }

        let mut version = [0u8; 4];
        std::io::Read::read_exact(&mut reader, &mut version)?;
        let version = u32::from_le_bytes(version);

        let mut tensor_count = [0u8; 8];
        std::io::Read::read_exact(&mut reader, &mut tensor_count)?;
        let tensor_count = u64::from_le_bytes(tensor_count);

        let mut kv_count = [0u8; 8];
        std::io::Read::read_exact(&mut reader, &mut kv_count)?;
        let kv_count = u64::from_le_bytes(kv_count);

        Ok(GGUFValidationReport {
            is_valid: true,
            version,
            tensor_count,
            metadata_count: kv_count,
            file_size: std::fs::metadata(path)?.len(),
            errors: Vec::new(),
            warnings: Vec::new(),
        })
    }
}

/// GGUF file validation report
#[derive(Debug)]
pub struct GGUFValidationReport {
    pub is_valid: bool,
    pub version: u32,
    pub tensor_count: u64,
    pub metadata_count: u64,
    pub file_size: u64,
    pub errors: Vec<String>,
    pub warnings: Vec<String>,
}

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

    #[test]
    fn test_gguf_tensor_type_properties() {
        assert_eq!(GGUFTensorType::F32.element_size(), 4);
        assert_eq!(GGUFTensorType::F16.element_size(), 2);
        assert_eq!(GGUFTensorType::Q4_0.block_size(), 32);
        assert!(GGUFTensorType::Q8_0.is_quantized());
        assert!(!GGUFTensorType::F32.is_quantized());
    }

    #[test]
    fn test_gguf_value_types() {
        let int_val = GGUFValue::Int32(42);
        let str_val = GGUFValue::String("test".to_string());
        let array_val = GGUFValue::Array(
            GGUFValueType::Int32,
            vec![GGUFValue::Int32(1), GGUFValue::Int32(2)],
        );

        assert!(matches!(int_val.value_type(), GGUFValueType::Int32));
        assert!(matches!(str_val.value_type(), GGUFValueType::String));
        assert!(matches!(array_val.value_type(), GGUFValueType::Array));
    }

    #[test]
    fn test_gguf_exporter_creation() {
        let exporter = GGUFExporter::new();
        assert_eq!(exporter.quantization_type, GGUFTensorType::F32);
        assert!(!exporter.metadata.is_empty());
    }

    #[test]
    fn test_gguf_exporter_with_quantization() {
        let exporter = GGUFExporter::new().with_quantization(GGUFTensorType::Q4_0);
        assert_eq!(exporter.quantization_type, GGUFTensorType::Q4_0);
    }

    #[test]
    fn test_gguf_exporter_metadata() {
        let exporter = GGUFExporter::new()
            .add_metadata(
                "custom.key".to_string(),
                GGUFValue::String("value".to_string()),
            )
            .set_architecture_metadata(2048, 768, 12, 3072, 12, Some(12), 50257);

        assert!(exporter.metadata.contains_key("custom.key"));
        assert!(exporter.metadata.contains_key("llama.context_length"));
    }

    #[test]
    fn test_gguf_value_serialization() -> Result<()> {
        let mut buffer = Vec::new();

        let value = GGUFValue::String("test".to_string());
        value.write_to_buffer(&mut buffer)?;

        // Should contain: length (8 bytes) + "test" (4 bytes)
        assert_eq!(buffer.len(), 12);

        Ok(())
    }

    #[test]
    fn test_supported_formats() {
        let exporter = GGUFExporter::new();
        let formats = exporter.supported_formats();
        assert_eq!(formats.len(), 1);
        assert_eq!(formats[0], ExportFormat::GGUF);
    }

    #[test]
    fn test_gguf_converter_validation() -> Result<()> {
        let temp_dir = tempdir()?;
        let temp_file = temp_dir.path().join("test.gguf");

        // Create a minimal GGUF file for testing
        let mut file = File::create(&temp_file)?;
        file.write_u32::<LittleEndian>(GGUF_MAGIC)?;
        file.write_u32::<LittleEndian>(GGUF_VERSION)?;
        file.write_u64::<LittleEndian>(0)?; // tensor count
        file.write_u64::<LittleEndian>(0)?; // kv count

        let report =
            GGUFConverter::validate_file(temp_file.to_str().expect("temp file creation failed"))?;
        assert!(report.is_valid);
        assert_eq!(report.version, GGUF_VERSION);
        assert_eq!(report.tensor_count, 0);
        assert_eq!(report.metadata_count, 0);

        Ok(())
    }
}