realizar 0.8.5

    // =========================================================================
    // Shared helpers (duplicated from tests.rs for module isolation)
    // =========================================================================

    /// Helper to create binary tensor entry
    fn create_binary_tensor_entry(
        name: &str,
        dtype: u8,
        shape: &[u64],
        offset: u64,
        size: u64,
    ) -> Vec<u8> {
        let mut data = Vec::new();
        // Name
        data.extend_from_slice(&(name.len() as u16).to_le_bytes());
        data.extend_from_slice(name.as_bytes());
        // Dtype
        data.push(dtype);
        // Shape
        data.push(shape.len() as u8);
        for &dim in shape {
            data.extend_from_slice(&dim.to_le_bytes());
        }
        // Offset and size
        data.extend_from_slice(&offset.to_le_bytes());
        data.extend_from_slice(&size.to_le_bytes());
        data
    }

    /// Helper to create a complete valid APR v2 model
    fn create_test_apr_model() -> Vec<u8> {
        let metadata = r#"{"architecture":"test","vocab_size":100,"hidden_size":64}"#;
        let metadata_bytes = metadata.as_bytes();
        let metadata_padded_size = metadata_bytes.len().div_ceil(64) * 64;

        // Binary tensor index entry for "test.weight"
        let tensor_entry = create_binary_tensor_entry("test.weight", 0, &[4, 4], 0, 64);

        let tensor_index_offset = HEADER_SIZE as u64 + metadata_padded_size as u64;
        let data_offset = tensor_index_offset + tensor_entry.len() as u64;
        let data_size = 64usize; // 16 floats * 4 bytes

        let total_size = data_offset as usize + data_size;
        let mut data = vec![0u8; total_size];

        // Header
        data[0..4].copy_from_slice(&MAGIC);
        data[4] = 2; // version major
        data[5] = 0; // version minor
        data[6..8].copy_from_slice(&0u16.to_le_bytes()); // flags = 0
        data[8..12].copy_from_slice(&1u32.to_le_bytes()); // tensor_count = 1
        data[12..20].copy_from_slice(&(HEADER_SIZE as u64).to_le_bytes()); // metadata_offset
        data[20..24].copy_from_slice(&(metadata_bytes.len() as u32).to_le_bytes()); // metadata_size
        data[24..32].copy_from_slice(&tensor_index_offset.to_le_bytes()); // tensor_index_offset
        data[32..40].copy_from_slice(&data_offset.to_le_bytes()); // data_offset

        // Metadata
        data[HEADER_SIZE..HEADER_SIZE + metadata_bytes.len()].copy_from_slice(metadata_bytes);

        // Tensor index
        let idx_start = tensor_index_offset as usize;
        data[idx_start..idx_start + tensor_entry.len()].copy_from_slice(&tensor_entry);

        // Tensor data (16 floats)
        let data_start = data_offset as usize;
        for i in 0..16 {
            let val = i as f32;
            data[data_start + i * 4..data_start + i * 4 + 4].copy_from_slice(&val.to_le_bytes());
        }

        data
    }

    /// Helper to create an APR model with a specific dtype tensor
    fn create_test_apr_model_with_dtype(dtype: u8, data_bytes: &[u8]) -> Vec<u8> {
        let metadata = r#"{"architecture":"test"}"#;
        let metadata_bytes = metadata.as_bytes();
        let metadata_padded_size = metadata_bytes.len().div_ceil(64) * 64;

        // Tensor shape depends on dtype and data
        let tensor_entry =
            create_binary_tensor_entry("typed.weight", dtype, &[4], 0, data_bytes.len() as u64);

        let tensor_index_offset = HEADER_SIZE as u64 + metadata_padded_size as u64;
        let data_offset = tensor_index_offset + tensor_entry.len() as u64;
        let total_size = data_offset as usize + data_bytes.len();
        let mut data = vec![0u8; total_size];

        // Header
        data[0..4].copy_from_slice(&MAGIC);
        data[4] = 2; // version major
        data[5] = 0; // version minor
        data[6..8].copy_from_slice(&0u16.to_le_bytes()); // flags = 0
        data[8..12].copy_from_slice(&1u32.to_le_bytes()); // tensor_count = 1
        data[12..20].copy_from_slice(&(HEADER_SIZE as u64).to_le_bytes()); // metadata_offset
        data[20..24].copy_from_slice(&(metadata_bytes.len() as u32).to_le_bytes()); // metadata_size
        data[24..32].copy_from_slice(&tensor_index_offset.to_le_bytes()); // tensor_index_offset
        data[32..40].copy_from_slice(&data_offset.to_le_bytes()); // data_offset

        // Metadata
        data[HEADER_SIZE..HEADER_SIZE + metadata_bytes.len()].copy_from_slice(metadata_bytes);

        // Tensor index
        let idx_start = tensor_index_offset as usize;
        data[idx_start..idx_start + tensor_entry.len()].copy_from_slice(&tensor_entry);

        // Tensor data
        let data_start = data_offset as usize;
        data[data_start..data_start + data_bytes.len()].copy_from_slice(data_bytes);

        data
    }

    // =========================================================================
    // rms_norm Tests
    // =========================================================================

    #[test]
    fn test_rms_norm_basic() {
        let x = vec![1.0, 2.0, 3.0, 4.0];
        let weight = vec![1.0, 1.0, 1.0, 1.0];
        let eps = 1e-6;
        let result = crate::apr::rms_norm(&x, &weight, eps);
        assert_eq!(result.len(), 4);
        // RMS of [1,2,3,4] = sqrt(30/4) ≈ 2.74
        // Each element normalized by RMS
    }

    #[test]
    fn test_rms_norm_zeros() {
        let x = vec![0.0, 0.0, 0.0, 0.0];
        let weight = vec![1.0, 1.0, 1.0, 1.0];
        let eps = 1e-6;
        let result = crate::apr::rms_norm(&x, &weight, eps);
        assert_eq!(result.len(), 4);
        // All zeros remain zeros
        for &v in &result {
            assert!(v.abs() < 1e-3);
        }
    }

    #[test]
    fn test_rms_norm_with_weight() {
        let x = vec![1.0, 1.0, 1.0, 1.0];
        let weight = vec![2.0, 2.0, 2.0, 2.0];
        let eps = 1e-6;
        let result = crate::apr::rms_norm(&x, &weight, eps);
        assert_eq!(result.len(), 4);
        // Weight should scale the result
        assert!(result[0] > 1.0);
    }

    // =========================================================================
    // matmul Tests
    // =========================================================================

    #[test]
    fn test_matmul_identity() {
        // 2x2 identity matrix times [1,2,1,2] (2 seq, 2 dim)
        let x = vec![1.0, 2.0, 1.0, 2.0];
        let w = vec![1.0, 0.0, 0.0, 1.0]; // Identity
        let result = crate::apr::matmul(&x, &w, 2, 2, 2);
        assert_eq!(result.len(), 4);
        assert!((result[0] - 1.0).abs() < 1e-6);
        assert!((result[1] - 2.0).abs() < 1e-6);
    }

    #[test]
    fn test_matmul_simple() {
        // [1,2] * [[1],[1]] = [3]
        let x = vec![1.0, 2.0];
        let w = vec![1.0, 1.0];
        let result = crate::apr::matmul(&x, &w, 1, 2, 1);
        assert_eq!(result.len(), 1);
        assert!((result[0] - 3.0).abs() < 1e-6);
    }

    // transpose_matrix is not in public API

    // =========================================================================
    // simd_dot Tests
    // =========================================================================

    #[test]
    fn test_simd_dot_basic() {
        let a = vec![1.0, 2.0, 3.0, 4.0];
        let b = vec![1.0, 1.0, 1.0, 1.0];
        let result = crate::apr::simd_dot(&a, &b);
        assert!((result - 10.0).abs() < 1e-6);
    }

    #[test]
    fn test_simd_dot_zeros() {
        let a = vec![1.0, 2.0, 3.0, 4.0];
        let b = vec![0.0, 0.0, 0.0, 0.0];
        let result = crate::apr::simd_dot(&a, &b);
        assert!((result - 0.0).abs() < 1e-6);
    }

    #[test]
    fn test_simd_dot_large() {
        let a: Vec<f32> = (0..256).map(|i| i as f32).collect();
        let b: Vec<f32> = vec![1.0; 256];
        let result = crate::apr::simd_dot(&a, &b);
        // Sum of 0..255 = 255*256/2 = 32640
        assert!((result - 32640.0).abs() < 1e-3);
    }

    // detect_format and AprFlags tests already exist above
    // f16_to_f32 tests already exist above

    // =========================================================================
    // dequantize_f16 Tests (extended)
    // =========================================================================

    #[test]
    fn test_dequantize_f16_basic() {
        // f16 1.0 = 0x3C00, stored as little-endian [0x00, 0x3C]
        let bytes = vec![0x00, 0x3C, 0x00, 0x3C];
        let result = crate::apr::dequantize_f16(&bytes, 2);
        assert_eq!(result.len(), 2);
        assert!((result[0] - 1.0).abs() < 0.01);
        assert!((result[1] - 1.0).abs() < 0.01);
    }

    #[test]
    fn test_dequantize_f16_truncated() {
        let bytes = vec![0x00]; // Only 1 byte, needs 2
        let result = crate::apr::dequantize_f16(&bytes, 1);
        // Truncated input returns empty
        assert_eq!(result.len(), 0);
    }

    // =========================================================================
    // dequantize_q8_0 Tests (extended)
    // =========================================================================

    #[test]
    fn test_dequantize_q8_0_zero_scale() {
        // Q8_0 block: 2-byte scale + 32 bytes data
        let mut bytes = vec![0u8; 34];
        // Scale = 0.0 (f16)
        bytes[0] = 0x00;
        bytes[1] = 0x00;
        let result = crate::apr::dequantize_q8_0(&bytes, 32);
        assert_eq!(result.len(), 32);
        // All zeros with zero scale
        for &v in &result {
            assert!((v - 0.0).abs() < 1e-6);
        }
    }

    // BpeTokenizer tests already exist above

    // =========================================================================
    // TensorEntry Tests (extended)
    // =========================================================================

    #[test]
    fn test_tensor_entry_byte_size_f32() {
        let entry = TensorEntry {
            name: "test".to_string(),
            dtype: "F32".to_string(),
            shape: vec![2, 3],
            offset: 0,
            size: 24, // 6 elements * 4 bytes
        };
        assert_eq!(entry.element_count(), 6);
    }

    #[test]
    fn test_tensor_entry_byte_size_f16() {
        let entry = TensorEntry {
            name: "test".to_string(),
            dtype: "F16".to_string(),
            shape: vec![4, 4],
            offset: 0,
            size: 32, // 16 elements * 2 bytes
        };
        assert_eq!(entry.element_count(), 16);
    }

    // =========================================================================
    // AprMetadata Tests (extended)
    // =========================================================================

    #[test]
    fn test_apr_metadata_is_transformer_missing_heads() {
        let meta = AprMetadata {
            hidden_size: Some(256),
            num_layers: Some(4),
            num_heads: None,
            vocab_size: Some(32000),
            ..Default::default()
        };
        assert!(!meta.is_transformer());
    }

    #[test]
    fn test_apr_metadata_is_transformer_missing_vocab() {
        let meta = AprMetadata {
            hidden_size: Some(256),
            num_layers: Some(4),
            num_heads: Some(8),
            vocab_size: None,
            ..Default::default()
        };
        assert!(!meta.is_transformer());
    }

    // =========================================================================
    // simple_attention Tests
    // =========================================================================

    #[test]
    fn test_simple_attention_single_head() {
        // Very simple case: 1 token, 1 head, head_dim=2
        let q = vec![1.0, 0.0];
        let k = vec![1.0, 0.0];
        let v = vec![1.0, 1.0];
        let result = crate::apr::simple_attention(&q, &k, &v, 1, 1, 1, 2);
        assert_eq!(result.len(), 2);
        // Attention output should be weighted v
    }

    // CudaAprModel tests require GPU feature flag - tested elsewhere

    // =========================================================================
    // AprV2Model Error Path Tests
    // =========================================================================

    #[test]
    fn test_apr_v2_model_from_bytes_truncated() {
        // Too short to contain header
        let data = vec![0u8; 10];
        let result = AprV2Model::from_bytes(data);
        assert!(result.is_err());
    }

    #[test]
    fn test_apr_v2_model_from_bytes_invalid_magic() {
        let mut data = vec![0u8; 100];
        // Invalid magic
        data[0..4].copy_from_slice(b"GGUF");
        let result = AprV2Model::from_bytes(data);
        assert!(result.is_err());
    }

    // Version test removed - ONE format, no versioning

    #[test]
    fn test_apr_v2_model_get_tensor_bytes_existing() {
        let data = create_test_apr_model();
        let model = AprV2Model::from_bytes(data).expect("APR operation failed");
        // test.weight exists in test model
        let result = model.get_tensor_bytes("test.weight");
        assert!(result.is_ok());
    }

    #[test]
    fn test_apr_v2_model_get_tensor_f32_existing() {
        let data = create_test_apr_model();
        let model = AprV2Model::from_bytes(data).expect("APR operation failed");
        // test.weight exists in test model
        let result = model.get_tensor_f32("test.weight");
        assert!(result.is_ok());
        let tensor = result.expect("APR operation failed");
        assert!(!tensor.is_empty());
    }

    // =========================================================================
    // AprHeader Extended Tests
    // =========================================================================

    #[test]
    fn test_apr_header_magic_validation() {
        let mut data = vec![0u8; 64];
        data[0..4].copy_from_slice(b"XXXX"); // Invalid magic
        let result = AprHeader::from_bytes(&data);
        assert!(result.is_err());
    }

    #[test]
    fn test_apr_header_version_tuple() {
        let mut data = vec![0u8; 64];
        data[0..4].copy_from_slice(&MAGIC);
        data[4] = 2; // Major
        data[5] = 1; // Minor
        let result = AprHeader::from_bytes(&data);
        assert!(result.is_ok());
        let header = result.expect("APR operation failed");
        assert_eq!(header.version, (2, 1));
    }

    // =========================================================================
    // AprFlags Extended Tests
    // =========================================================================

    #[test]
    fn test_apr_flags_from_default() {
        let flags = AprFlags::default();
        // Default flags have no bits set
        assert!(!flags.is_compressed());
        assert!(!flags.is_encrypted());
    }

    // =========================================================================
    // TensorEntry Extended Tests
    // =========================================================================

    #[test]
    fn test_tensor_entry_from_binary_large_shape() {
        // Create entry with large multidimensional shape
        let entry = create_binary_tensor_entry("big_tensor", 0, &[10, 20, 30, 40], 0, 240000);
        let (parsed, consumed) = TensorEntry::from_binary(&entry).expect("APR operation failed");
        assert_eq!(parsed.name, "big_tensor");
        assert_eq!(parsed.shape, vec![10, 20, 30, 40]);
        assert_eq!(parsed.element_count(), 10 * 20 * 30 * 40);
        assert!(consumed > 0);
    }

    #[test]
    fn test_tensor_entry_from_binary_zero_dim() {
        // Scalar tensor (empty shape)
        let entry = create_binary_tensor_entry("scalar", 0, &[], 0, 4);
        let (parsed, _) = TensorEntry::from_binary(&entry).expect("APR operation failed");
        assert_eq!(parsed.name, "scalar");
        assert!(parsed.shape.is_empty());
        assert_eq!(parsed.element_count(), 1); // Scalar has 1 element
    }

    // =========================================================================
    // dequantize Function Tests
    // =========================================================================

    #[test]
    fn test_dequantize_f16_multiple_values() {
        // f16 values: 1.0 = 0x3C00, 2.0 = 0x4000
        let bytes = vec![
            0x00, 0x3C, // 1.0
            0x00, 0x40, // 2.0
            0x00, 0x3C, // 1.0
        ];
        let result = crate::apr::dequantize_f16(&bytes, 3);
        assert_eq!(result.len(), 3);
        assert!((result[0] - 1.0).abs() < 0.1);
        assert!((result[1] - 2.0).abs() < 0.1);
    }