//! ControlNet TDD Tests
//!
//! Test-driven development for ControlNet implementation following:
//! "Adding Conditional Control to Text-to-Image Diffusion Models" (Zhang et al. 2023)
#![cfg(feature = "disabled_tests")]

use crate::controlnet::*;
use crate::*;
use rtx_tensor::{DType, Device, Tensor};
use std::collections::HashMap;

/// Test configuration for ControlNet (re-export)
pub use crate::controlnet::ControlNetConfig as ControlNetTestConfig;

/// Test helper for creating test tensors
fn create_test_tensor(shape: &[usize], device: &Device) -> Result<Tensor> {
    Tensor::randn(shape, device)
        .map_err(|e| DiffusionError::TensorError(format!("Failed to create test tensor: {}", e)))
}

/// Test helper for creating control condition tensors
fn create_control_condition(
    batch_size: usize,
    channels: usize,
    height: usize,
    width: usize,
    device: &Device,
) -> Result<Tensor> {
    create_test_tensor(&[batch_size, channels, height, width], device)
}

#[cfg(test)]
mod controlnet_zero_conv_tests {
    use super::*;

    #[test]
    fn test_zero_conv_initialization() {
        // RED: Test fails - ZeroConv not implemented
        let device = Device::cuda(0).unwrap_or(Device::default());
        let in_channels = 320;
        let out_channels = 320;

        let zero_conv = ZeroConv::new(in_channels, out_channels).unwrap();

        // Zero convolution should initialize weights to zero
        let input = create_test_tensor(&[1, in_channels, 8, 8], &device).unwrap();
        let output = zero_conv.forward(&input).unwrap();

        // Output should be zero for zero-initialized weights
        let output_data = output.to_vec().unwrap();
        assert!(
            output_data.iter().all(|&x| x.abs() < 1e-8),
            "Zero conv output should be zero"
        );

        // Test that we can make it trainable
        assert!(zero_conv.is_trainable(), "Zero conv should be trainable");
    }

    #[test]
    fn test_zero_conv_gradual_learning() {
        // RED: Test fails - ZeroConv gradual learning not implemented
        let device = Device::cuda(0).unwrap_or(Device::default());
        let mut zero_conv = ZeroConv::new(320, 320).unwrap();

        // Simulate training by manually setting small weights
        zero_conv.set_learning_rate(0.001);

        let input = create_test_tensor(&[1, 320, 8, 8], &device).unwrap();

        // Initial forward should be zero
        let output1 = zero_conv.forward(&input).unwrap();
        let norm1 = output1
            .abs()
            .unwrap()
            .sum(None)
            .unwrap()
            .to_scalar::<f32>()
            .unwrap();
        assert!(norm1 < 1e-6, "Initial output should be near zero");

        // After simulated gradient update
        zero_conv.simulate_gradient_update(&input).unwrap();
        let output2 = zero_conv.forward(&input).unwrap();
        let norm2 = output2
            .abs()
            .unwrap()
            .sum(None)
            .unwrap()
            .to_scalar::<f32>()
            .unwrap();

        assert!(
            norm2 > norm1,
            "Output should increase after gradient update"
        );
        assert!(
            norm2 < 0.1,
            "Output should still be small after initial updates"
        );
    }
}

#[cfg(test)]
mod controlnet_conditioning_tests {
    use super::*;

    #[test]
    fn test_control_injection_at_multiple_scales() {
        // RED: Test fails - ControlNet not implemented
        let device = Device::cuda(0).unwrap_or(Device::default());
        let config = ControlNetTestConfig::default();
        let controlnet = ControlNet::new(config.clone()).unwrap();

        // Create input tensors
        let batch_size = 2;
        let height = 64;
        let width = 64;

        let x =
            create_test_tensor(&[batch_size, config.in_channels, height, width], &device).unwrap();
        let timesteps = Tensor::from_slice(&[100.0f32, 200.0f32], &[batch_size], &device).unwrap();
        let control_condition =
            create_control_condition(batch_size, 3, height, width, &device).unwrap(); // Default RGB

        let context = ControlContext::new()
            .with_condition(control_condition)
            .with_strength(0.8);

        let (output, control_residuals) = controlnet.forward(&x, &timesteps, &context).unwrap();

        // Output should have same shape as input
        assert_eq!(output.dims(), x.dims());

        // Should have control residuals at multiple scales
        assert!(
            control_residuals.len() > 1,
            "Should have multiple control residuals"
        );

        // Each residual should have appropriate dimensions
        for (i, residual) in control_residuals.iter().enumerate() {
            let dims = residual.dims();
            assert_eq!(
                dims[0], batch_size,
                "Batch size should match for residual {}",
                i
            );
            assert!(
                dims[2] <= height,
                "Height should be <= input height for residual {}",
                i
            );
            assert!(
                dims[3] <= width,
                "Width should be <= input width for residual {}",
                i
            );
        }
    }

    #[test]
    fn test_control_strength_scheduling() {
        // RED: Test fails - Control strength scheduling not implemented
        let device = Device::cuda(0).unwrap_or(Device::default());
        let config = ControlNetTestConfig::default();
        let controlnet = ControlNet::new(config.clone()).unwrap();

        let batch_size = 1;
        let x = create_test_tensor(&[batch_size, config.in_channels, 64, 64], &device).unwrap();
        let timesteps = Tensor::from_slice(&[500.0f32], &[batch_size], &device).unwrap();
        let control_condition = create_control_condition(batch_size, 3, 64, 64, &device).unwrap(); // Default RGB

        // Test different control strengths
        let strengths = [0.0, 0.5, 1.0];
        let mut outputs = Vec::new();

        for &strength in &strengths {
            let context = ControlContext::new()
                .with_condition(control_condition.clone())
                .with_strength(strength);

            let (output, _) = controlnet.forward(&x, &timesteps, &context).unwrap();
            outputs.push(output);
        }

        // With strength=0.0, should behave like no control
        let no_control_context = ControlContext::new().with_strength(0.0);
        let (no_control_output, _) = controlnet
            .forward(&x, &timesteps, &no_control_context)
            .unwrap();

        let diff_zero = (&outputs[0] - &no_control_output)
            .unwrap()
            .abs()
            .unwrap()
            .max()
            .unwrap()
            .to_scalar::<f32>()
            .unwrap();
        assert!(
            diff_zero < 1e-4,
            "Zero strength should produce similar output to no control"
        );

        // Higher strength should produce more different results
        let diff_half = (&outputs[1] - &outputs[0])
            .unwrap()
            .abs()
            .unwrap()
            .max()
            .unwrap()
            .to_scalar::<f32>()
            .unwrap();
        let diff_full = (&outputs[2] - &outputs[0])
            .unwrap()
            .abs()
            .unwrap()
            .max()
            .unwrap()
            .to_scalar::<f32>()
            .unwrap();

        assert!(
            diff_full > diff_half,
            "Full strength should differ more from zero than half strength"
        );
    }

    #[test]
    fn test_various_control_types() {
        // RED: Test fails - Multiple control types not implemented
        let device = Device::cuda(0).unwrap_or(Device::default());
        let config = ControlNetTestConfig::default();

        // Test different control types
        let control_types = vec![
            ControlType::Edge,
            ControlType::Pose,
            ControlType::Depth,
            ControlType::Normal,
            ControlType::Segmentation,
        ];

        for control_type in control_types {
            let controlnet = ControlNet::with_control_type(config.clone(), control_type).unwrap();

            let batch_size = 1;
            let x = create_test_tensor(&[batch_size, config.in_channels, 64, 64], &device).unwrap();
            let timesteps = Tensor::from_slice(&[250.0f32], &[batch_size], &device).unwrap();
            let control_condition = create_control_condition(
                batch_size,
                control_type.input_channels(),
                64,
                64,
                &device,
            )
            .unwrap();

            let context = ControlContext::new()
                .with_condition(control_condition)
                .with_control_type(control_type)
                .with_strength(1.0);

            let result = controlnet.forward(&x, &timesteps, &context);
            assert!(
                result.is_ok(),
                "Forward pass should work for control type {:?}",
                control_type
            );
        }
    }

    #[test]
    fn test_trainable_copy_of_encoder_blocks() {
        // RED: Test fails - Trainable encoder copy not implemented
        let device = Device::cuda(0).unwrap_or(Device::default());
        let config = ControlNetTestConfig::default();
        let controlnet = ControlNet::new(config.clone()).unwrap();

        // ControlNet should have trainable copies of UNet encoder blocks
        let encoder_blocks = controlnet.get_encoder_blocks();
        assert!(!encoder_blocks.is_empty(), "Should have encoder blocks");

        for (i, block) in encoder_blocks.iter().enumerate() {
            assert!(
                block.is_trainable(),
                "Encoder block {} should be trainable",
                i
            );

            // Verify block has same architecture as UNet but different parameters
            let unet = UNet::new(UNetConfig::from(config.clone())).unwrap();
            let unet_encoder_blocks = unet.get_encoder_blocks();

            assert_eq!(
                block.parameter_count(),
                unet_encoder_blocks[i].parameter_count(),
                "Parameter count should match UNet encoder block {}",
                i
            );

            // Parameters should be different (not shared)
            let controlnet_params = block.get_parameters();
            let unet_params = unet_encoder_blocks[i].get_parameters();

            for (cp, up) in controlnet_params.iter().zip(unet_params.iter()) {
                let diff = (cp - up)
                    .unwrap()
                    .abs()
                    .unwrap()
                    .max()
                    .unwrap()
                    .to_scalar::<f32>()
                    .unwrap();
                assert!(diff > 1e-6, "ControlNet and UNet parameters should differ");
            }
        }
    }
}

#[cfg(test)]
mod controlnet_architecture_tests {
    use super::*;

    #[test]
    fn test_controlnet_backbone_compatibility() {
        // RED: Test fails - ControlNet backbone not implemented
        let device = Device::cuda(0).unwrap_or(Device::default());
        let config = ControlNetTestConfig::default();
        let controlnet = ControlNet::new(config.clone()).unwrap();
        let unet = UNet::new(UNetConfig::from(config.clone())).unwrap();

        let batch_size = 1;
        let x = create_test_tensor(&[batch_size, config.in_channels, 64, 64], &device).unwrap();
        let timesteps = Tensor::from_slice(&[400.0f32], &[batch_size], &device).unwrap();
        let control_condition = create_control_condition(batch_size, 3, 64, 64, &device).unwrap(); // Default RGB

        // Test without control (should behave similarly to UNet)
        let no_control_context = ControlContext::new().with_strength(0.0);
        let (controlnet_output, _) = controlnet
            .forward(&x, &timesteps, &no_control_context)
            .unwrap();

        let unet_output = unet.forward(&x, &timesteps, &None).unwrap();

        // Outputs should be similar when no control is applied
        let diff = (&controlnet_output - &unet_output)
            .unwrap()
            .abs()
            .unwrap()
            .max()
            .unwrap()
            .to_scalar::<f32>()
            .unwrap();
        assert!(
            diff < 0.1,
            "ControlNet without control should behave similarly to UNet"
        );
    }

    #[test]
    fn test_controlnet_memory_efficiency() {
        // RED: Test fails - Memory efficiency not implemented
        let device = Device::cuda(0).unwrap_or(Device::default());
        let config = ControlNetTestConfig::default();
        let controlnet = ControlNet::new(config.clone()).unwrap();

        // Test gradient checkpointing
        controlnet.enable_gradient_checkpointing(true);
        assert!(controlnet.is_gradient_checkpointing_enabled());

        // Test memory usage estimation
        let memory_usage = controlnet.estimate_memory_usage(2, 64, 64).unwrap();
        assert!(
            memory_usage.parameters > 0,
            "Should report parameter memory"
        );
        assert!(
            memory_usage.activations > 0,
            "Should report activation memory"
        );
        assert!(memory_usage.gradients > 0, "Should report gradient memory");

        // With gradient checkpointing, activation memory should be lower
        controlnet.enable_gradient_checkpointing(false);
        let memory_usage_no_checkpoint = controlnet.estimate_memory_usage(2, 64, 64).unwrap();

        assert!(
            memory_usage.activations < memory_usage_no_checkpoint.activations,
            "Gradient checkpointing should reduce activation memory"
        );
    }

    #[test]
    fn test_controlnet_conditioning_fusion() {
        // RED: Test fails - Conditioning fusion not implemented
        let device = Device::cuda(0).unwrap_or(Device::default());
        let config = ControlNetTestConfig::default();
        let controlnet = ControlNet::new(config.clone()).unwrap();

        let batch_size = 2;
        let x = create_test_tensor(&[batch_size, config.in_channels, 64, 64], &device).unwrap();
        let timesteps = Tensor::from_slice(&[300.0f32, 400.0f32], &[batch_size], &device).unwrap();

        // Test multiple conditioning inputs
        let edge_condition = create_control_condition(batch_size, 1, 64, 64, &device).unwrap(); // Edge map
        let depth_condition = create_control_condition(batch_size, 1, 64, 64, &device).unwrap(); // Depth map

        let context = ControlContext::new()
            .with_condition(edge_condition)
            .with_secondary_condition("depth", depth_condition)
            .with_strength(0.8)
            .with_fusion_mode(FusionMode::Additive);

        let result = controlnet.forward(&x, &timesteps, &context);
        assert!(result.is_ok(), "Multi-condition forward should work");

        let (output, residuals) = result.unwrap();
        assert_eq!(output.dims(), x.dims(), "Output shape should match input");
        assert!(!residuals.is_empty(), "Should have control residuals");
    }
}

// Note: All structs are now defined in the main controlnet module