torsh-vision 0.1.2

// Framework infrastructure - components designed for future use
#![allow(dead_code)]
use crate::unified_transforms::{TransformContext, TransformParameter, UnifiedTransform};
use crate::{Result, VisionError};
use scirs2_core::random::{Random, Rng};
use scirs2_core::RngExt;
use std::collections::HashMap;
use std::sync::Arc;
use torsh_core::device::Device;
use torsh_core::dtype::DType;
use torsh_tensor::Tensor;

/// Unified resize transform that implements both CPU and GPU acceleration
#[derive(Debug)]
pub struct UnifiedResize {
    size: (usize, usize),
    device: Option<Arc<dyn Device>>,
}

impl UnifiedResize {
    pub fn new(size: (usize, usize)) -> Self {
        Self { size, device: None }
    }

    pub fn with_device(size: (usize, usize), device: Arc<dyn Device>) -> Self {
        Self {
            size,
            device: Some(device),
        }
    }
}

impl UnifiedTransform for UnifiedResize {
    fn apply(&self, input: &Tensor<f32>) -> Result<Tensor<f32>> {
        crate::ops::resize(input, self.size)
    }

    fn apply_gpu(&self, input: &Tensor<f32>) -> Result<Tensor<f32>> {
        if let Some(device) = &self.device {
            if matches!(
                device.device_type(),
                torsh_core::device::DeviceType::Cuda(_)
            ) {
                // Use GPU-accelerated resize if available
                // For now, fallback to CPU implementation
                self.apply(input)
            } else {
                self.apply(input)
            }
        } else {
            self.apply(input)
        }
    }

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

    fn parameters(&self) -> HashMap<String, TransformParameter> {
        let mut params = HashMap::new();
        params.insert(
            "size".to_string(),
            TransformParameter::Tuple2Usize(self.size),
        );
        if let Some(device) = &self.device {
            params.insert(
                "device".to_string(),
                TransformParameter::String(format!("{:?}", device)),
            );
        }
        params
    }

    fn supports_gpu(&self) -> bool {
        self.device.as_ref().map_or(false, |d| {
            matches!(d.device_type(), torsh_core::device::DeviceType::Cuda(_))
        })
    }

    fn output_shape(&self, input_shape: &[usize]) -> Result<Vec<usize>> {
        if input_shape.len() != 3 {
            return Err(VisionError::InvalidShape(format!(
                "Expected 3D tensor (C, H, W), got {}D",
                input_shape.len()
            )));
        }
        Ok(vec![input_shape[0], self.size.1, self.size.0])
    }

    fn clone_transform(&self) -> Box<dyn UnifiedTransform> {
        Box::new(UnifiedResize {
            size: self.size,
            device: self.device.clone(),
        })
    }

    fn preferred_device(&self) -> Option<&dyn Device> {
        self.device.as_ref().map(|d| d.as_ref())
    }
}

/// Unified center crop transform
#[derive(Debug, Clone)]
pub struct UnifiedCenterCrop {
    size: (usize, usize),
}

impl UnifiedCenterCrop {
    pub fn new(size: (usize, usize)) -> Self {
        Self { size }
    }
}

impl UnifiedTransform for UnifiedCenterCrop {
    fn apply(&self, input: &Tensor<f32>) -> Result<Tensor<f32>> {
        crate::ops::center_crop(input, self.size)
    }

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

    fn parameters(&self) -> HashMap<String, TransformParameter> {
        let mut params = HashMap::new();
        params.insert(
            "size".to_string(),
            TransformParameter::Tuple2Usize(self.size),
        );
        params
    }

    fn output_shape(&self, input_shape: &[usize]) -> Result<Vec<usize>> {
        if input_shape.len() != 3 {
            return Err(VisionError::InvalidShape(format!(
                "Expected 3D tensor (C, H, W), got {}D",
                input_shape.len()
            )));
        }
        Ok(vec![input_shape[0], self.size.1, self.size.0])
    }

    fn clone_transform(&self) -> Box<dyn UnifiedTransform> {
        Box::new(self.clone())
    }
}

/// Unified random horizontal flip transform
#[derive(Debug, Clone)]
pub struct UnifiedRandomHorizontalFlip {
    p: f32,
}

impl UnifiedRandomHorizontalFlip {
    pub fn new(p: f32) -> Self {
        Self { p }
    }
}

impl UnifiedTransform for UnifiedRandomHorizontalFlip {
    fn apply(&self, input: &Tensor<f32>) -> Result<Tensor<f32>> {
        let mut rng = Random::seed(42);
        if rng.random::<f32>() < self.p {
            crate::ops::horizontal_flip(input)
        } else {
            Ok(input.clone())
        }
    }

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

    fn parameters(&self) -> HashMap<String, TransformParameter> {
        let mut params = HashMap::new();
        params.insert("probability".to_string(), TransformParameter::Float(self.p));
        params
    }

    fn clone_transform(&self) -> Box<dyn UnifiedTransform> {
        Box::new(self.clone())
    }
}

/// Unified normalization transform
#[derive(Debug)]
pub struct UnifiedNormalize {
    mean: Vec<f32>,
    std: Vec<f32>,
    device: Option<Arc<dyn Device>>,
}

impl UnifiedNormalize {
    pub fn new(mean: Vec<f32>, std: Vec<f32>) -> Self {
        Self {
            mean,
            std,
            device: None,
        }
    }

    pub fn with_device(mean: Vec<f32>, std: Vec<f32>, device: Arc<dyn Device>) -> Self {
        Self {
            mean,
            std,
            device: Some(device),
        }
    }
}

impl UnifiedTransform for UnifiedNormalize {
    fn apply(&self, input: &Tensor<f32>) -> Result<Tensor<f32>> {
        crate::ops::normalize(
            input,
            crate::ops::color::NormalizationConfig {
                method: crate::ops::color::NormalizationMethod::Custom,
                mean: Some(self.mean.clone()),
                std: Some(self.std.clone()),
                per_channel: true,
                eps: 1e-8,
            },
        )
    }

    fn apply_gpu(&self, input: &Tensor<f32>) -> Result<Tensor<f32>> {
        if let Some(device) = &self.device {
            if matches!(
                device.device_type(),
                torsh_core::device::DeviceType::Cuda(_)
            ) {
                // Use GPU-accelerated normalization if available
                // For now, fallback to CPU implementation
                self.apply(input)
            } else {
                self.apply(input)
            }
        } else {
            self.apply(input)
        }
    }

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

    fn parameters(&self) -> HashMap<String, TransformParameter> {
        let mut params = HashMap::new();
        params.insert(
            "mean".to_string(),
            TransformParameter::FloatVec(self.mean.clone()),
        );
        params.insert(
            "std".to_string(),
            TransformParameter::FloatVec(self.std.clone()),
        );
        if let Some(device) = &self.device {
            params.insert(
                "device".to_string(),
                TransformParameter::String(format!("{:?}", device)),
            );
        }
        params
    }

    fn supports_gpu(&self) -> bool {
        self.device.as_ref().map_or(false, |d| {
            matches!(d.device_type(), torsh_core::device::DeviceType::Cuda(_))
        })
    }

    fn supports_mixed_precision(&self) -> bool {
        self.supports_gpu()
    }

    fn clone_transform(&self) -> Box<dyn UnifiedTransform> {
        Box::new(UnifiedNormalize {
            mean: self.mean.clone(),
            std: self.std.clone(),
            device: self.device.clone(),
        })
    }

    fn preferred_device(&self) -> Option<&dyn Device> {
        self.device.as_ref().map(|d| d.as_ref())
    }
}

/// Unified random crop transform
#[derive(Debug, Clone)]
pub struct UnifiedRandomCrop {
    size: (usize, usize),
    padding: Option<usize>,
}

impl UnifiedRandomCrop {
    pub fn new(size: (usize, usize)) -> Self {
        Self {
            size,
            padding: None,
        }
    }

    pub fn with_padding(size: (usize, usize), padding: usize) -> Self {
        Self {
            size,
            padding: Some(padding),
        }
    }
}

impl UnifiedTransform for UnifiedRandomCrop {
    fn apply(&self, input: &Tensor<f32>) -> Result<Tensor<f32>> {
        let input_to_crop = if let Some(padding) = self.padding {
            crate::ops::pad(
                input,
                (padding, padding, padding, padding),
                crate::ops::PaddingMode::Zero,
                0.0,
            )?
        } else {
            input.clone()
        };

        crate::ops::random_crop(&input_to_crop, self.size)
    }

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

    fn parameters(&self) -> HashMap<String, TransformParameter> {
        let mut params = HashMap::new();
        params.insert(
            "size".to_string(),
            TransformParameter::Tuple2Usize(self.size),
        );
        if let Some(padding) = self.padding {
            params.insert("padding".to_string(), TransformParameter::Usize(padding));
        }
        params
    }

    fn output_shape(&self, input_shape: &[usize]) -> Result<Vec<usize>> {
        if input_shape.len() != 3 {
            return Err(VisionError::InvalidShape(format!(
                "Expected 3D tensor (C, H, W), got {}D",
                input_shape.len()
            )));
        }
        Ok(vec![input_shape[0], self.size.1, self.size.0])
    }

    fn clone_transform(&self) -> Box<dyn UnifiedTransform> {
        Box::new(self.clone())
    }
}

/// Unified color jitter transform with GPU support
#[derive(Debug)]
pub struct UnifiedColorJitter {
    brightness: Option<f32>,
    contrast: Option<f32>,
    saturation: Option<f32>,
    hue: Option<f32>,
    device: Option<Arc<dyn Device>>,
}

impl UnifiedColorJitter {
    pub fn new() -> Self {
        Self {
            brightness: None,
            contrast: None,
            saturation: None,
            hue: None,
            device: None,
        }
    }

    pub fn brightness(mut self, brightness: f32) -> Self {
        self.brightness = Some(brightness);
        self
    }

    pub fn contrast(mut self, contrast: f32) -> Self {
        self.contrast = Some(contrast);
        self
    }

    pub fn saturation(mut self, saturation: f32) -> Self {
        self.saturation = Some(saturation);
        self
    }

    pub fn hue(mut self, hue: f32) -> Self {
        self.hue = Some(hue);
        self
    }

    pub fn with_device(mut self, device: Arc<dyn Device>) -> Self {
        self.device = Some(device);
        self
    }
}

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

impl UnifiedTransform for UnifiedColorJitter {
    fn apply(&self, input: &Tensor<f32>) -> Result<Tensor<f32>> {
        let mut output = input.clone();
        let mut rng = Random::seed(42);

        // Apply brightness adjustment
        if let Some(brightness) = self.brightness {
            let factor = rng.gen_range(1.0 - brightness..=1.0 + brightness);
            output = output.mul_scalar(factor)?;
        }

        // Apply contrast adjustment
        if let Some(contrast) = self.contrast {
            let factor = rng.gen_range(1.0 - contrast..=1.0 + contrast);
            let mean = output.mean(None, false)?;
            let mean_val = mean.to_vec()?[0];
            output.sub_scalar_(mean_val)?;
            output = output.mul_scalar(factor)?;
            output = output.add_scalar(mean_val)?;
        }

        // For saturation and hue, we'd need to convert to HSV space
        // This is a simplified implementation
        Ok(output)
    }

    fn apply_gpu(&self, input: &Tensor<f32>) -> Result<Tensor<f32>> {
        if let Some(device) = &self.device {
            if matches!(
                device.device_type(),
                torsh_core::device::DeviceType::Cuda(_)
            ) {
                // Use GPU-accelerated color jitter if available
                // For now, fallback to CPU implementation
                self.apply(input)
            } else {
                self.apply(input)
            }
        } else {
            self.apply(input)
        }
    }

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

    fn parameters(&self) -> HashMap<String, TransformParameter> {
        let mut params = HashMap::new();
        if let Some(brightness) = self.brightness {
            params.insert(
                "brightness".to_string(),
                TransformParameter::Float(brightness),
            );
        }
        if let Some(contrast) = self.contrast {
            params.insert("contrast".to_string(), TransformParameter::Float(contrast));
        }
        if let Some(saturation) = self.saturation {
            params.insert(
                "saturation".to_string(),
                TransformParameter::Float(saturation),
            );
        }
        if let Some(hue) = self.hue {
            params.insert("hue".to_string(), TransformParameter::Float(hue));
        }
        if let Some(device) = &self.device {
            params.insert(
                "device".to_string(),
                TransformParameter::String(format!("{:?}", device)),
            );
        }
        params
    }

    fn supports_gpu(&self) -> bool {
        self.device.as_ref().map_or(false, |d| {
            matches!(d.device_type(), torsh_core::device::DeviceType::Cuda(_))
        })
    }

    fn supports_mixed_precision(&self) -> bool {
        self.supports_gpu()
    }

    fn clone_transform(&self) -> Box<dyn UnifiedTransform> {
        Box::new(UnifiedColorJitter {
            brightness: self.brightness,
            contrast: self.contrast,
            saturation: self.saturation,
            hue: self.hue,
            device: self.device.clone(),
        })
    }

    fn preferred_device(&self) -> Option<&dyn Device> {
        self.device.as_ref().map(|d| d.as_ref())
    }
}

/// Unified random rotation transform
#[derive(Debug, Clone)]
pub struct UnifiedRandomRotation {
    degrees: (f32, f32),
}

impl UnifiedRandomRotation {
    pub fn new(degrees: (f32, f32)) -> Self {
        Self { degrees }
    }
}

impl UnifiedTransform for UnifiedRandomRotation {
    fn apply(&self, input: &Tensor<f32>) -> Result<Tensor<f32>> {
        let mut rng = Random::seed(42);
        let angle = rng.gen_range(self.degrees.0..=self.degrees.1);
        crate::ops::rotate(input, angle)
    }

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

    fn parameters(&self) -> HashMap<String, TransformParameter> {
        let mut params = HashMap::new();
        params.insert(
            "degrees".to_string(),
            TransformParameter::Tuple2Float(self.degrees),
        );
        params
    }

    fn clone_transform(&self) -> Box<dyn UnifiedTransform> {
        Box::new(self.clone())
    }
}

/// Bridge implementations for backward compatibility with existing Transform trait

/// Bridge from old Transform trait to UnifiedTransform
#[derive(Debug)]
pub struct TransformBridge<T: crate::transforms::Transform> {
    inner: T,
}

impl<T: crate::transforms::Transform + Clone + std::fmt::Debug> TransformBridge<T> {
    pub fn new(transform: T) -> Self {
        Self { inner: transform }
    }
}

impl<T: crate::transforms::Transform + Clone + std::fmt::Debug + 'static> UnifiedTransform
    for TransformBridge<T>
{
    fn apply(&self, input: &Tensor<f32>) -> Result<Tensor<f32>> {
        self.inner.forward(input)
    }

    fn name(&self) -> &'static str {
        self.inner.name()
    }

    fn parameters(&self) -> HashMap<String, TransformParameter> {
        let old_params = self.inner.parameters();
        let mut params = HashMap::new();
        for (key, value) in old_params {
            params.insert(key.to_string(), TransformParameter::String(value));
        }
        params
    }

    fn is_inplace(&self) -> bool {
        self.inner.is_inplace()
    }

    fn clone_transform(&self) -> Box<dyn UnifiedTransform> {
        Box::new(TransformBridge::new(self.inner.clone()))
    }
}

/// Bridge from UnifiedTransform to old Transform trait
pub struct UnifiedTransformBridge {
    inner: Box<dyn UnifiedTransform>,
}

impl UnifiedTransformBridge {
    pub fn new(transform: Box<dyn UnifiedTransform>) -> Self {
        Self { inner: transform }
    }
}

impl crate::transforms::Transform for UnifiedTransformBridge {
    fn forward(&self, input: &Tensor<f32>) -> Result<Tensor<f32>> {
        self.inner.apply(input)
    }

    fn name(&self) -> &'static str {
        self.inner.name()
    }

    fn is_inplace(&self) -> bool {
        self.inner.is_inplace()
    }

    fn parameters(&self) -> Vec<(&'static str, String)> {
        // Simplified conversion - using static fallback to avoid memory leaks
        vec![("bridge", "legacy_transform".to_string())]
    }

    fn clone_transform(&self) -> Box<dyn crate::transforms::Transform> {
        Box::new(UnifiedTransformBridge::new(self.inner.clone_transform()))
    }
}

/// Factory functions for creating unified transforms with different backends
pub mod factory {
    use super::*;

    /// Create a resize transform optimized for the given context
    pub fn create_resize(
        size: (usize, usize),
        context: &TransformContext,
    ) -> Box<dyn UnifiedTransform> {
        if matches!(
            context.device.device_type(),
            torsh_core::device::DeviceType::Cuda(_)
        ) {
            Box::new(UnifiedResize::with_device(size, context.device.clone()))
        } else {
            Box::new(UnifiedResize::new(size))
        }
    }

    /// Create a normalization transform optimized for the given context
    pub fn create_normalize(
        mean: Vec<f32>,
        std: Vec<f32>,
        context: &TransformContext,
    ) -> Box<dyn UnifiedTransform> {
        if matches!(
            context.device.device_type(),
            torsh_core::device::DeviceType::Cuda(_)
        ) {
            Box::new(UnifiedNormalize::with_device(
                mean,
                std,
                context.device.clone(),
            ))
        } else {
            Box::new(UnifiedNormalize::new(mean, std))
        }
    }

    /// Create a color jitter transform optimized for the given context
    pub fn create_color_jitter(context: &TransformContext) -> UnifiedColorJitter {
        if matches!(
            context.device.device_type(),
            torsh_core::device::DeviceType::Cuda(_)
        ) {
            UnifiedColorJitter::new().with_device(context.device.clone())
        } else {
            UnifiedColorJitter::new()
        }
    }

    /// Create an ImageNet preprocessing pipeline
    pub fn imagenet_preprocessing(
        size: (usize, usize),
        context: &TransformContext,
    ) -> crate::unified_transforms::UnifiedCompose {
        use crate::unified_transforms::TransformBuilder;

        TransformBuilder::new()
            .with_context(context.clone())
            .add(UnifiedResize::with_device(size, context.device.clone()))
            .add(UnifiedCenterCrop::new(size))
            .add(UnifiedNormalize::with_device(
                vec![0.485, 0.456, 0.406],
                vec![0.229, 0.224, 0.225],
                context.device.clone(),
            ))
            .build()
    }

    /// Create a training augmentation pipeline
    pub fn training_augmentation(
        size: (usize, usize),
        context: &TransformContext,
    ) -> crate::unified_transforms::UnifiedCompose {
        use crate::unified_transforms::TransformBuilder;

        TransformBuilder::new()
            .with_context(context.clone())
            .add(UnifiedResize::with_device(
                (size.0 + 32, size.1 + 32),
                context.device.clone(),
            ))
            .add(UnifiedRandomCrop::with_padding(size, 4))
            .add(UnifiedRandomHorizontalFlip::new(0.5))
            .add(
                UnifiedColorJitter::new()
                    .brightness(0.2)
                    .contrast(0.2)
                    .with_device(context.device.clone()),
            )
            .add(UnifiedNormalize::with_device(
                vec![0.485, 0.456, 0.406],
                vec![0.229, 0.224, 0.225],
                context.device.clone(),
            ))
            .build()
    }
}

/// Migration utilities for transitioning between transform APIs
pub mod migration {
    use super::*;

    /// Convert a vector of old transforms to unified transforms
    ///
    /// # Status
    /// NOT IMPLEMENTED in v0.1.0
    ///
    /// # Reason
    /// The bridge pattern requires concrete types, not trait objects.
    /// Automatic conversion requires runtime type inspection or manual mapping.
    ///
    /// # Workaround
    /// Manually reconstruct transform pipeline using UnifiedTransform API:
    /// ```ignore
    /// // Old API
    /// let old = Compose::new(vec![Box::new(Resize::new(256)), ...]);
    ///
    /// // New API (manual conversion)
    /// let new = PipelineBuilder::new()
    ///     .add_transform(ResizeTransform::new(256, InterpolationMode::Bilinear))
    ///     .build();
    /// ```
    ///
    /// # Future
    /// May implement with macro-based conversion in v0.3.0
    /// Deferred - See ROADMAP.md
    pub fn convert_transforms(
        _transforms: Vec<Box<dyn crate::transforms::Transform>>,
    ) -> Vec<Box<dyn UnifiedTransform>> {
        // Returns empty vector - user must manually convert
        // Consider using analyze_pipeline() for migration guidance
        Vec::new()
    }

    /// Analyze old transform pipeline and suggest unified equivalents
    pub fn analyze_pipeline(_compose: &crate::transforms::Compose) -> String {
        let mut suggestions = Vec::new();

        suggestions.push("Consider migrating to UnifiedTransform API for:".to_string());
        suggestions.push("- Better GPU acceleration support".to_string());
        suggestions.push("- Mixed precision training capabilities".to_string());
        suggestions.push("- Improved parameter introspection".to_string());
        suggestions.push("- Hardware-aware optimization".to_string());

        suggestions.join("\n")
    }
}

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

    #[test]
    fn test_unified_resize() {
        let transform = UnifiedResize::new((224, 224));
        let input = zeros(&[3, 256, 256]).expect("zeros should succeed");
        let output = transform
            .apply(&input)
            .expect("apply operation should succeed");
        assert_eq!(output.shape().dims(), &[3, 224, 224]);
    }

    #[test]
    fn test_unified_parameters() {
        let transform = UnifiedResize::new((224, 224));
        let params = transform.parameters();
        assert!(params.contains_key("size"));
    }

    #[test]
    fn test_transform_bridge() {
        let old_transform = crate::transforms::Resize::new((224, 224));
        let bridge = TransformBridge::new(old_transform);

        let input = zeros(&[3, 256, 256]).expect("zeros should succeed");
        let output = bridge
            .apply(&input)
            .expect("apply operation should succeed");
        assert_eq!(output.shape().dims(), &[3, 224, 224]);
    }

    #[test]
    fn test_factory_functions() {
        let context = TransformContext::default();
        let transform = factory::create_resize((224, 224), &context);

        let input = zeros(&[3, 256, 256]).expect("zeros should succeed");
        let output = transform
            .apply(&input)
            .expect("apply operation should succeed");
        assert_eq!(output.shape().dims(), &[3, 224, 224]);
    }

    #[test]
    fn test_gpu_context() {
        let context = TransformContext::auto_detect().unwrap_or_default();
        let transform = factory::create_normalize(
            vec![0.485, 0.456, 0.406],
            vec![0.229, 0.224, 0.225],
            &context,
        );

        assert_eq!(transform.name(), "UnifiedNormalize");
        assert!(
            transform.supports_gpu()
                || !matches!(
                    context.device.device_type(),
                    torsh_core::device::DeviceType::Cuda(_)
                )
        );
    }
}