torsh-vision 0.1.2

// Framework infrastructure - components designed for future use
#![allow(dead_code)]
use crate::{Result, VisionError};
// use half; // Commented out - half crate not available
use std::collections::HashMap;
use std::fmt;
use std::sync::Arc;
use torsh_core::device::{CpuDevice, Device, DeviceType};
use torsh_core::dtype::DType;
use torsh_tensor::Tensor;

/// Unified transform trait that supports both CPU and GPU operations
pub trait UnifiedTransform: Send + Sync + fmt::Debug {
    /// Apply the transform to an input tensor using the best available device
    fn apply(&self, input: &Tensor<f32>) -> Result<Tensor<f32>>;

    /// Apply the transform using GPU acceleration if available
    fn apply_gpu(&self, input: &Tensor<f32>) -> Result<Tensor<f32>> {
        // Default fallback to CPU implementation
        self.apply(input)
    }

    /// Apply the transform using mixed precision (f16) on GPU
    fn apply_gpu_f16(&self, input: &Tensor<f32>) -> Result<Tensor<f32>> {
        // Default implementation: convert to f32, apply, convert back
        let input_f32 = input.to_dtype(DType::F32)?;
        let output_f32 = self.apply_gpu(&input_f32)?;
        Ok(output_f32.to_dtype(DType::F16)?)
    }

    /// Get the canonical name of this transform
    fn name(&self) -> &'static str;

    /// Get transform parameters for introspection and serialization
    fn parameters(&self) -> HashMap<String, TransformParameter>;

    /// Check if this transform supports GPU acceleration
    fn supports_gpu(&self) -> bool {
        false
    }

    /// Check if this transform supports mixed precision
    fn supports_mixed_precision(&self) -> bool {
        false
    }

    /// Check if this transform modifies the input in-place (for optimization)
    fn is_inplace(&self) -> bool {
        false
    }

    /// Get the output shape given an input shape
    fn output_shape(&self, input_shape: &[usize]) -> Result<Vec<usize>> {
        // Default: output shape same as input shape
        Ok(input_shape.to_vec())
    }

    /// Clone the transform
    fn clone_transform(&self) -> Box<dyn UnifiedTransform>;

    /// Get device affinity for this transform
    fn preferred_device(&self) -> Option<&dyn Device> {
        None
    }
}

/// Parameter type for transform introspection
#[derive(Debug, Clone)]
pub enum TransformParameter {
    Float(f32),
    Int(i32),
    Usize(usize),
    Bool(bool),
    String(String),
    FloatVec(Vec<f32>),
    IntVec(Vec<i32>),
    UsizeVec(Vec<usize>),
    Tuple2Usize((usize, usize)),
    Tuple2Float((f32, f32)),
}

impl fmt::Display for TransformParameter {
    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
        match self {
            TransformParameter::Float(v) => write!(f, "{:.3}", v),
            TransformParameter::Int(v) => write!(f, "{}", v),
            TransformParameter::Usize(v) => write!(f, "{}", v),
            TransformParameter::Bool(v) => write!(f, "{}", v),
            TransformParameter::String(v) => write!(f, "{}", v),
            TransformParameter::FloatVec(v) => write!(f, "{:?}", v),
            TransformParameter::IntVec(v) => write!(f, "{:?}", v),
            TransformParameter::UsizeVec(v) => write!(f, "{:?}", v),
            TransformParameter::Tuple2Usize((a, b)) => write!(f, "({}, {})", a, b),
            TransformParameter::Tuple2Float((a, b)) => write!(f, "({:.3}, {:.3})", a, b),
        }
    }
}

/// Transform execution context for hardware-aware execution
#[derive(Clone)]
pub struct TransformContext {
    pub device: Arc<dyn Device>,
    pub use_mixed_precision: bool,
    pub batch_size: Option<usize>,
    pub enable_optimizations: bool,
}

impl std::fmt::Debug for TransformContext {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        f.debug_struct("TransformContext")
            .field("device", &"Arc<dyn Device>")
            .field("use_mixed_precision", &self.use_mixed_precision)
            .field("batch_size", &self.batch_size)
            .field("enable_optimizations", &self.enable_optimizations)
            .finish()
    }
}

impl Default for TransformContext {
    fn default() -> Self {
        Self {
            device: Arc::new(CpuDevice::new()),
            use_mixed_precision: false,
            batch_size: None,
            enable_optimizations: true,
        }
    }
}

impl TransformContext {
    pub fn new(device: Arc<dyn Device>) -> Self {
        Self {
            device,
            ..Default::default()
        }
    }

    pub fn with_mixed_precision(mut self, enabled: bool) -> Self {
        self.use_mixed_precision = enabled;
        self
    }

    pub fn with_batch_size(mut self, batch_size: usize) -> Self {
        self.batch_size = Some(batch_size);
        self
    }

    pub fn auto_detect() -> Result<Self> {
        // For now, default to CPU device since CUDA detection is not fully implemented
        let _device_type = DeviceType::Cpu;

        let device = Arc::new(CpuDevice::new()) as Arc<dyn Device>;
        let use_mixed_precision = false; // Set to false for CPU devices

        Ok(Self {
            device,
            use_mixed_precision,
            batch_size: None,
            enable_optimizations: true,
        })
    }
}

/// Unified transform chain for composing multiple transforms
#[derive(Debug)]
pub struct UnifiedCompose {
    transforms: Vec<Box<dyn UnifiedTransform>>,
    context: TransformContext,
}

impl UnifiedCompose {
    pub fn new(transforms: Vec<Box<dyn UnifiedTransform>>) -> Self {
        Self {
            transforms,
            context: TransformContext::default(),
        }
    }

    pub fn with_context(mut self, context: TransformContext) -> Self {
        self.context = context;
        self
    }

    pub fn add_transform(&mut self, transform: Box<dyn UnifiedTransform>) {
        self.transforms.push(transform);
    }

    pub fn len(&self) -> usize {
        self.transforms.len()
    }

    pub fn is_empty(&self) -> bool {
        self.transforms.is_empty()
    }
}

impl UnifiedTransform for UnifiedCompose {
    fn apply(&self, input: &Tensor<f32>) -> Result<Tensor<f32>> {
        let mut output = input.clone();
        for transform in &self.transforms {
            output = if self.context.use_mixed_precision && transform.supports_mixed_precision() {
                let output_f16 = output.to_dtype(DType::F16)?;
                let result_f16 = transform.apply_gpu_f16(&output_f16)?;
                result_f16.to_dtype(DType::F32)?
            } else if matches!(self.context.device.device_type(), DeviceType::Cuda(_))
                && transform.supports_gpu()
            {
                transform.apply_gpu(&output)?
            } else {
                transform.apply(&output)?
            };
        }
        Ok(output)
    }

    fn apply_gpu(&self, input: &Tensor<f32>) -> Result<Tensor<f32>> {
        let mut output = input.clone();
        for transform in &self.transforms {
            output = transform.apply_gpu(&output)?;
        }
        Ok(output)
    }

    fn apply_gpu_f16(&self, input: &Tensor<f32>) -> Result<Tensor<f32>> {
        let mut output = input.clone();
        for transform in &self.transforms {
            output = transform.apply_gpu_f16(&output)?;
        }
        Ok(output)
    }

    fn name(&self) -> &'static str {
        "UnifiedCompose"
    }

    fn parameters(&self) -> HashMap<String, TransformParameter> {
        let mut params = HashMap::new();
        params.insert(
            "num_transforms".to_string(),
            TransformParameter::Usize(self.transforms.len()),
        );

        // Add individual transform info
        for (i, transform) in self.transforms.iter().enumerate() {
            params.insert(
                format!("transform_{}", i),
                TransformParameter::String(transform.name().to_string()),
            );
        }

        params
    }

    fn supports_gpu(&self) -> bool {
        self.transforms.iter().any(|t| t.supports_gpu())
    }

    fn supports_mixed_precision(&self) -> bool {
        self.transforms.iter().any(|t| t.supports_mixed_precision())
    }

    fn output_shape(&self, input_shape: &[usize]) -> Result<Vec<usize>> {
        let mut shape = input_shape.to_vec();
        for transform in &self.transforms {
            shape = transform.output_shape(&shape)?;
        }
        Ok(shape)
    }

    fn clone_transform(&self) -> Box<dyn UnifiedTransform> {
        let cloned_transforms = self
            .transforms
            .iter()
            .map(|t| t.clone_transform())
            .collect();
        Box::new(UnifiedCompose::new(cloned_transforms).with_context(self.context.clone()))
    }
}

/// Transform builder for fluent API construction
pub struct TransformBuilder {
    transforms: Vec<Box<dyn UnifiedTransform>>,
    context: TransformContext,
}

impl TransformBuilder {
    pub fn new() -> Self {
        Self {
            transforms: Vec::new(),
            context: TransformContext::default(),
        }
    }

    pub fn with_context(mut self, context: TransformContext) -> Self {
        self.context = context;
        self
    }

    pub fn add<T: UnifiedTransform + 'static>(mut self, transform: T) -> Self {
        self.transforms.push(Box::new(transform));
        self
    }

    pub fn resize(self, size: (usize, usize)) -> Self {
        self.add(crate::transforms::unified::UnifiedResize::new(size))
    }

    pub fn center_crop(self, size: (usize, usize)) -> Self {
        self.add(crate::transforms::unified::UnifiedCenterCrop::new(size))
    }

    pub fn random_horizontal_flip(self, p: f32) -> Self {
        self.add(crate::transforms::unified::UnifiedRandomHorizontalFlip::new(p))
    }

    pub fn normalize(self, mean: Vec<f32>, std: Vec<f32>) -> Self {
        self.add(crate::transforms::unified::UnifiedNormalize::new(mean, std))
    }

    pub fn build(self) -> UnifiedCompose {
        UnifiedCompose::new(self.transforms).with_context(self.context)
    }
}

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

/// Macro for easy transform composition
#[macro_export]
macro_rules! compose_transforms {
    ($($transform:expr),+ $(,)?) => {
        $crate::unified_transforms::UnifiedCompose::new(vec![
            $(Box::new($transform),)+
        ])
    };
}

/// Convenience functions for common transform combinations
pub mod presets {
    use super::*;

    /// Standard ImageNet preprocessing
    pub fn imagenet_preprocessing(size: (usize, usize)) -> UnifiedCompose {
        TransformBuilder::new()
            .resize(size)
            .center_crop(size)
            .normalize(vec![0.485, 0.456, 0.406], vec![0.229, 0.224, 0.225])
            .build()
    }

    /// Data augmentation for training
    pub fn training_augmentation(size: (usize, usize)) -> UnifiedCompose {
        TransformBuilder::new()
            .resize((size.0 + 32, size.1 + 32))
            .random_horizontal_flip(0.5)
            .center_crop(size)
            .normalize(vec![0.485, 0.456, 0.406], vec![0.229, 0.224, 0.225])
            .build()
    }

    /// GPU-optimized transforms with mixed precision
    pub fn gpu_optimized_transforms(size: (usize, usize)) -> Result<UnifiedCompose> {
        let context = TransformContext::auto_detect()?;
        Ok(TransformBuilder::new()
            .with_context(context)
            .resize(size)
            .normalize(vec![0.485, 0.456, 0.406], vec![0.229, 0.224, 0.225])
            .build())
    }
}

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

    #[test]
    fn test_transform_builder() {
        let transform = TransformBuilder::new()
            .resize((224, 224))
            .center_crop((224, 224))
            .normalize(vec![0.5], vec![0.5])
            .build();

        assert_eq!(transform.len(), 3);
        assert_eq!(transform.name(), "UnifiedCompose");
    }

    #[test]
    fn test_compose_macro() {
        let resize = crate::transforms::unified::UnifiedResize::new((224, 224));
        let normalize = crate::transforms::unified::UnifiedNormalize::new(vec![0.5], vec![0.5]);

        let _transform = compose_transforms![resize, normalize];
    }

    #[test]
    fn test_transform_parameters() {
        let param = TransformParameter::Tuple2Usize((224, 224));
        assert_eq!(param.to_string(), "(224, 224)");

        let param = TransformParameter::FloatVec(vec![0.485, 0.456, 0.406]);
        assert!(param.to_string().contains("0.485"));
    }

    #[test]
    fn test_context_auto_detect() {
        let context = TransformContext::auto_detect();
        assert!(context.is_ok());
    }
}