//! Simplified RegNet tests - TDD Red Phase
//! 
//! This file tests RegNet implementation in isolation using minimal mock tensor framework.

use std::fmt;

/// Mock tensor error for testing
#[derive(Debug)]
pub enum MockTensorError {
    ShapeMismatch(String),
    DeviceMismatch(String),
    InvalidOperation(String),
}

impl fmt::Display for MockTensorError {
    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
        match self {
            MockTensorError::ShapeMismatch(msg) => write!(f, "Shape mismatch: {}", msg),
            MockTensorError::DeviceMismatch(msg) => write!(f, "Device mismatch: {}", msg),
            MockTensorError::InvalidOperation(msg) => write!(f, "Invalid operation: {}", msg),
        }
    }
}

impl std::error::Error for MockTensorError {}

pub type Result<T> = std::result::Result<T, MockTensorError>;

/// Mock device for testing
#[derive(Debug, Clone, PartialEq)]
pub enum Device {
    Cpu,
}

impl Device {
    pub fn cpu() -> Self {
        Self::Cpu
    }
}

/// Mock tensor for testing
#[derive(Debug, Clone)]
pub struct Tensor {
    data: Vec<f32>,
    shape: Vec<usize>,
}

impl Tensor {
    pub fn randn(shape: Vec<usize>, _device: &Device) -> Result<Self> {
        let numel: usize = shape.iter().product();
        let data = vec![0.5; numel]; // Simplified - use constant instead of random
        Ok(Self { data, shape })
    }

    pub fn shape(&self) -> &[usize] {
        &self.shape
    }
}

// ========== VISION ERROR TYPE ==========

#[derive(Debug)]
pub enum VisionError {
    InvalidDimensions { expected: String, got: String },
    ConfigurationError(String),
    TensorError(String),
    InvalidInput(String),
}

impl fmt::Display for VisionError {
    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
        match self {
            VisionError::InvalidDimensions { expected, got } =>
                write!(f, "Invalid dimensions: expected {}, got {}", expected, got),
            VisionError::ConfigurationError(msg) => write!(f, "Configuration error: {}", msg),
            VisionError::TensorError(msg) => write!(f, "Tensor operation error: {}", msg),
            VisionError::InvalidInput(msg) => write!(f, "Invalid input: {}", msg),
        }
    }
}

impl std::error::Error for VisionError {}

// Convert MockTensorError to VisionError
impl From<MockTensorError> for VisionError {
    fn from(err: MockTensorError) -> Self {
        match err {
            MockTensorError::ShapeMismatch(msg) => VisionError::InvalidDimensions {
                expected: "compatible shape".to_string(),
                got: msg
            },
            MockTensorError::DeviceMismatch(msg) => VisionError::TensorError(msg),
            MockTensorError::InvalidOperation(msg) => VisionError::TensorError(msg),
        }
    }
}

pub type VisionResult<T> = std::result::Result<T, VisionError>;

// ========== REGNET CONFIGURATION ==========

#[derive(Clone, Debug)]
pub struct RegNetConfig {
    w_a: f32,
    w_0: f32,
    w_m: f32,
    group_width: usize,
    depth_multiplier: f32,
    num_classes: usize,
    use_se: bool,
    initial_width: f32,
    num_stages: usize,
    width_multiplier: f32,
}

#[derive(Clone, Debug)]
pub struct RegNetBlockConfig {
    in_channels: usize,
    out_channels: usize,
    stride: usize,
    groups: usize,
    use_se: bool,
}

#[derive(Clone, Debug)]
pub struct SEModuleConfig {
    channels: usize,
    reduction: usize,
}

#[derive(Clone, Debug)]
pub struct RegNetStageConfig {
    in_channels: usize,
    out_channels: usize,
    num_blocks: usize,
    groups: usize,
    stride: usize,
    use_se: bool,
}

// ========== REGNET COMPONENTS ==========

pub struct RegNet {
    stem: RegNetStem,
    stages: Vec<RegNetStage>,
    head: RegNetHead,
    config: RegNetConfig,
}

pub struct RegNetBlock {
    config: RegNetBlockConfig,
    conv1_weight: Tensor,
    conv2_weight: Tensor,
    conv3_weight: Tensor,
    bn1_weight: Tensor,
    bn2_weight: Tensor,
    bn3_weight: Tensor,
    se_module: Option<SEModule>,
}

pub struct SEModule {
    config: SEModuleConfig,
    fc1_weight: Tensor,
    fc2_weight: Tensor,
}

pub struct RegNetStem {
    conv_weight: Tensor,
    bn_weight: Tensor,
}

pub struct RegNetStage {
    config: RegNetStageConfig,
    blocks: Vec<RegNetBlock>,
}

pub struct RegNetHead {
    pool_kernel_size: usize,
    fc_weight: Tensor,
    fc_bias: Tensor,
}

// ========== PLACEHOLDER IMPLEMENTATIONS (WILL FAIL) ==========

impl RegNetConfig {
    pub fn new(
        w_a: f32,
        w_0: f32,
        w_m: f32,
        group_width: usize,
        depth_multiplier: f32,
        num_classes: usize,
    ) -> VisionResult<Self> {
        Ok(Self {
            w_a,
            w_0,
            w_m,
            group_width,
            depth_multiplier,
            num_classes,
            use_se: false,
            initial_width: w_0,
            num_stages: 4,
            width_multiplier: w_m,
        })
    }

    pub fn regnetx_200mf() -> Self {
        Self {
            w_a: 36.44,
            w_0: 24.0,
            w_m: 2.24,
            group_width: 8,
            depth_multiplier: 1.0,
            num_classes: 1000,
            use_se: false,
            initial_width: 24.0,
            num_stages: 4,
            width_multiplier: 2.24,
        }
    }

    pub fn regnety_200mf() -> Self {
        Self {
            w_a: 36.44,
            w_0: 24.0,
            w_m: 2.24,
            group_width: 8,
            depth_multiplier: 1.0,
            num_classes: 1000,
            use_se: true,
            initial_width: 24.0,
            num_stages: 4,
            width_multiplier: 2.24,
        }
    }

    pub fn regnetx_400mf() -> Self {
        Self {
            w_a: 24.48,
            w_0: 24.0,
            w_m: 2.54,
            group_width: 16,
            depth_multiplier: 1.0,
            num_classes: 1000,
            use_se: false,
            initial_width: 24.0,
            num_stages: 4,
            width_multiplier: 2.54,
        }
    }

    pub fn regnety_400mf() -> Self {
        Self {
            w_a: 27.89,
            w_0: 48.0,
            w_m: 2.09,
            group_width: 8,
            depth_multiplier: 1.0,
            num_classes: 1000,
            use_se: true,
            initial_width: 48.0,
            num_stages: 4,
            width_multiplier: 2.09,
        }
    }

    pub fn regnetx_600mf() -> Self {
        Self {
            w_a: 36.97,
            w_0: 48.0,
            w_m: 2.24,
            group_width: 24,
            depth_multiplier: 1.0,
            num_classes: 1000,
            use_se: false,
            initial_width: 48.0,
            num_stages: 4,
            width_multiplier: 2.24,
        }
    }

    pub fn regnetx_800mf() -> Self {
        Self {
            w_a: 35.73,
            w_0: 56.0,
            w_m: 2.28,
            group_width: 16,
            depth_multiplier: 1.0,
            num_classes: 1000,
            use_se: false,
            initial_width: 56.0,
            num_stages: 4,
            width_multiplier: 2.28,
        }
    }

    pub fn w_a(&self) -> f32 { self.w_a }
    pub fn w_0(&self) -> f32 { self.w_0 }
    pub fn w_m(&self) -> f32 { self.w_m }
    pub fn group_width(&self) -> usize { self.group_width }
    pub fn depth_multiplier(&self) -> f32 { self.depth_multiplier }
    pub fn num_classes(&self) -> usize { self.num_classes }
    pub fn use_se(&self) -> bool { self.use_se }

    pub fn calculate_stage_widths(&self) -> Vec<usize> {
        // Calculate stage widths based on RegNet scaling rules
        let mut widths = Vec::new();
        let mut current_width = self.initial_width as f32;

        for _ in 0..self.num_stages {
            widths.push((current_width as usize).max(8)); // Minimum width of 8
            current_width *= self.width_multiplier;
        }

        // Round to nearest multiple of group_width for group convolutions
        widths.iter().map(|&w| {
            let remainder = w % self.group_width;
            if remainder == 0 {
                w
            } else {
                w + (self.group_width - remainder)
            }
        }).collect()
    }

    pub fn calculate_stage_depths(&self) -> Vec<usize> {
        // Calculate stage depths based on depth multiplier
        let base_depths = [2, 3, 4, 2]; // Common RegNet depth pattern

        base_depths.iter()
            .take(self.num_stages)
            .map(|&d| {
                let scaled_depth = (d as f32 * self.depth_multiplier).round() as usize;
                scaled_depth.max(1) // Minimum depth of 1
            })
            .collect()
    }
}

impl RegNet {
    pub fn new(config: &RegNetConfig, device: &Device) -> VisionResult<Self> {
        // Calculate stage configurations
        let stage_widths = config.calculate_stage_widths();
        let stage_depths = config.calculate_stage_depths();

        // Create stem (initial conv layer)
        let stem = RegNetStem {
            conv_weight: Tensor::randn(vec![64, 3, 3, 3], device)?,
            bn_weight: Tensor::randn(vec![64], device)?,
        };

        // Create stages
        let mut stages = Vec::new();
        let mut in_channels = 64;

        for (stage_idx, (&out_channels, &depth)) in stage_widths.iter().zip(stage_depths.iter()).enumerate() {
            let stride = if stage_idx == 0 { 1 } else { 2 };
            let stage_config = RegNetStageConfig {
                in_channels,
                out_channels,
                num_blocks: depth,
                groups: config.group_width,
                stride,
                use_se: config.use_se,
            };

            let stage = RegNetStage::new(&stage_config, device)?;
            stages.push(stage);
            in_channels = out_channels;
        }

        // Create classification head
        let head = RegNetHead {
            pool_kernel_size: 7,
            fc_weight: Tensor::randn(vec![config.num_classes, in_channels], device)?,
            fc_bias: Tensor::randn(vec![config.num_classes], device)?,
        };

        Ok(Self {
            stem,
            stages,
            head,
            config: config.clone(),
        })
    }

    pub fn forward(&self, input: &Tensor) -> VisionResult<Tensor> {
        // Validate input shape (B, C, H, W)
        if input.shape().len() != 4 {
            return Err(VisionError::InvalidInput(
                format!("Expected 4D input tensor (B,C,H,W), got {}D", input.shape().len())
            ));
        }

        if input.shape()[1] != 3 {
            return Err(VisionError::InvalidInput(
                format!("Expected 3 input channels, got {}", input.shape()[1])
            ));
        }

        // Forward through stem
        let mut x = self.stem.forward(input)?;

        // Forward through stages
        for stage in &self.stages {
            x = stage.forward(&x)?;
        }

        // Forward through head
        x = self.head.forward(&x)?;

        Ok(x)
    }
}

impl RegNetBlockConfig {
    pub fn new(in_channels: usize, out_channels: usize, stride: usize, groups: usize) -> Self {
        Self {
            in_channels,
            out_channels,
            stride,
            groups,
            use_se: false,
        }
    }
    pub fn groups(&self) -> usize { self.groups }
    pub fn out_channels(&self) -> usize { self.out_channels }
}

impl RegNetBlock {
    pub fn new(config: &RegNetBlockConfig, device: &Device) -> VisionResult<Self> {
        let se_module = if config.use_se {
            let se_config = SEModuleConfig::new(config.out_channels, 4);
            Some(SEModule::new(&se_config, device)?)
        } else {
            None
        };

        Ok(Self {
            config: config.clone(),
            conv1_weight: Tensor::randn(vec![config.out_channels, config.in_channels, 1, 1], device)?,
            conv2_weight: Tensor::randn(vec![config.out_channels, config.out_channels / config.groups, 3, 3], device)?,
            conv3_weight: Tensor::randn(vec![config.out_channels, config.out_channels, 1, 1], device)?,
            bn1_weight: Tensor::randn(vec![config.out_channels], device)?,
            bn2_weight: Tensor::randn(vec![config.out_channels], device)?,
            bn3_weight: Tensor::randn(vec![config.out_channels], device)?,
            se_module,
        })
    }

    pub fn forward(&self, input: &Tensor) -> VisionResult<Tensor> {
        // Simplified forward pass for testing
        // In real implementation, this would do:
        // 1. 1x1 conv + BN + ReLU
        // 2. 3x3 grouped conv + BN + ReLU
        // 3. 1x1 conv + BN
        // 4. SE module (if enabled)
        // 5. Residual connection + ReLU

        let output_shape = vec![
            input.shape()[0], // batch
            self.config.out_channels, // channels
            input.shape()[2] / self.config.stride, // height
            input.shape()[3] / self.config.stride, // width
        ];

        Ok(Tensor::randn(output_shape, &Device::cpu())?)
    }
}

impl SEModuleConfig {
    pub fn new(channels: usize, reduction: usize) -> Self {
        Self { channels, reduction }
    }
}

impl SEModule {
    pub fn new(config: &SEModuleConfig, device: &Device) -> VisionResult<Self> {
        let reduced_channels = config.channels / config.reduction;

        Ok(Self {
            config: config.clone(),
            fc1_weight: Tensor::randn(vec![reduced_channels, config.channels], device)?,
            fc2_weight: Tensor::randn(vec![config.channels, reduced_channels], device)?,
        })
    }

    pub fn forward(&self, input: &Tensor) -> VisionResult<Tensor> {
        // Simplified SE forward pass:
        // 1. Global average pooling
        // 2. FC1 + ReLU
        // 3. FC2 + Sigmoid
        // 4. Scale input

        // For testing, just return input unchanged
        Ok(input.clone())
    }
}

impl RegNetStageConfig {
    pub fn new(in_channels: usize, out_channels: usize, num_blocks: usize, groups: usize, stride: usize, use_se: bool) -> Self {
        Self { in_channels, out_channels, num_blocks, groups, stride, use_se }
    }
}

impl RegNetStage {
    pub fn new(config: &RegNetStageConfig, device: &Device) -> VisionResult<Self> {
        let mut blocks = Vec::new();
        let mut in_channels = config.in_channels;

        for i in 0..config.num_blocks {
            let stride = if i == 0 { config.stride } else { 1 };
            let block_config = RegNetBlockConfig {
                in_channels,
                out_channels: config.out_channels,
                stride,
                groups: config.groups,
                use_se: config.use_se,
            };

            blocks.push(RegNetBlock::new(&block_config, device)?);
            in_channels = config.out_channels;
        }

        Ok(Self {
            config: config.clone(),
            blocks,
        })
    }

    pub fn forward(&self, input: &Tensor) -> VisionResult<Tensor> {
        let mut x = input.clone();

        for block in &self.blocks {
            x = block.forward(&x)?;
        }

        Ok(x)
    }
}

impl RegNetStem {
    pub fn new(in_channels: usize, out_channels: usize, device: &Device) -> VisionResult<Self> {
        Ok(Self {
            conv_weight: Tensor::randn(vec![out_channels, in_channels, 3, 3], device)?,
            bn_weight: Tensor::randn(vec![out_channels], device)?,
        })
    }

    pub fn forward(&self, input: &Tensor) -> VisionResult<Tensor> {
        // Simplified stem forward pass:
        // 3x3 conv + BN + ReLU

        let output_shape = vec![
            input.shape()[0], // batch
            self.conv_weight.shape()[0], // out_channels
            input.shape()[2] / 2, // height (stride 2)
            input.shape()[3] / 2, // width (stride 2)
        ];

        Ok(Tensor::randn(output_shape, &Device::cpu())?)
    }
}

impl RegNetHead {
    pub fn new(in_channels: usize, num_classes: usize, device: &Device) -> VisionResult<Self> {
        Ok(Self {
            pool_kernel_size: 7,
            fc_weight: Tensor::randn(vec![num_classes, in_channels], device)?,
            fc_bias: Tensor::randn(vec![num_classes], device)?,
        })
    }

    pub fn forward(&self, input: &Tensor) -> VisionResult<Tensor> {
        // Simplified head forward pass:
        // 1. Global average pooling
        // 2. Fully connected layer

        let batch_size = input.shape()[0];
        let num_classes = self.fc_weight.shape()[0];

        Ok(Tensor::randn(vec![batch_size, num_classes], &Device::cpu())?)
    }
}

// ========== TDD RED PHASE TESTS (ALL SHOULD PANIC) ==========

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

    #[test]
    fn test_regnet_design_space_parameters() {
        let config = RegNetConfig::new(
            32.0,  // w_a
            2.25,  // w_0
            1.0,   // w_m
            8,     // group_width
            2.0,   // depth_multiplier
            1000,  // num_classes
        ).expect("Failed to create RegNet config");

        assert_eq!(config.w_a(), 32.0);
        assert_eq!(config.w_0(), 2.25);
        assert_eq!(config.w_m(), 1.0);
        assert_eq!(config.group_width(), 8);
        assert_eq!(config.depth_multiplier(), 2.0);
        assert_eq!(config.num_classes(), 1000);
        println!("✓ Basic RegNet config creation works");
    }

    #[test]
    fn test_quantized_linear_parameterization() {
        let config = RegNetConfig::regnetx_200mf();
        let stage_widths = config.calculate_stage_widths();
        assert!(!stage_widths.is_empty());
        println!("✓ Stage widths calculated: {:?}", stage_widths);
    }

    #[test]
    fn test_stage_depth_calculation() {
        let config = RegNetConfig::regnetx_200mf();
        let stage_depths = config.calculate_stage_depths();
        assert!(!stage_depths.is_empty());
        println!("✓ Stage depths calculated: {:?}", stage_depths);
    }

    #[test]
    fn test_regnet_block_creation() {
        let device = Device::cpu();
        let config = RegNetBlockConfig::new(64, 128, 1, 8);
        let block = RegNetBlock::new(&config, &device).unwrap();
        println!("✓ RegNet block created successfully");
        assert_eq!(block.config.in_channels, 64);
        assert_eq!(block.config.out_channels, 128);
    }

    #[test]
    fn test_squeeze_excitation_module() {
        let device = Device::cpu();
        let config = SEModuleConfig::new(128, 4);
        let se_module = SEModule::new(&config, &device).unwrap();
        println!("✓ SE module created successfully");
        assert_eq!(se_module.config.channels, 128);
        assert_eq!(se_module.config.reduction, 4);
    }

    #[test]
    fn test_regnet_stem() {
        let device = Device::cpu();
        let stem = RegNetStem::new(3, 32, &device).unwrap();
        println!("✓ RegNet stem created successfully");
        assert_eq!(stem.conv_weight.shape(), &[32, 3, 3, 3]);
    }

    #[test]
    fn test_regnet_stage() {
        let device = Device::cpu();
        let config = RegNetStageConfig::new(64, 128, 2, 8, 2, true);
        let stage = RegNetStage::new(&config, &device).unwrap();
        println!("✓ RegNet stage created successfully with {} blocks", stage.blocks.len());
        assert_eq!(stage.blocks.len(), 2);
    }

    #[test]
    fn test_regnet_head() {
        let device = Device::cpu();
        let head = RegNetHead::new(512, 1000, &device).unwrap();
        println!("✓ RegNet head created successfully");
        assert_eq!(head.fc_weight.shape(), &[1000, 512]);
    }

    #[test]
    fn test_regnet_complete_model() {
        let device = Device::cpu();
        let config = RegNetConfig::regnetx_200mf();
        let model = RegNet::new(&config, &device).unwrap();
        println!("✓ RegNet model created successfully with {} stages", model.stages.len());
    }

    #[test]
    fn test_regnet_multiple_scales_config() {
        let scales = vec![
            RegNetConfig::regnetx_200mf(),
            RegNetConfig::regnetx_400mf(),
            RegNetConfig::regnetx_600mf(),
            RegNetConfig::regnetx_800mf(),
            RegNetConfig::regnety_200mf(),
            RegNetConfig::regnety_400mf(),
        ];
        
        for config in scales {
            assert!(config.w_a() > 0.0);
            assert!(config.w_0() > 0.0);
            assert!(config.w_m() > 0.0);
            assert!(config.group_width() > 0);
            assert_eq!(config.num_classes(), 1000);
        }
        println!("✓ All RegNet scale configurations are valid");
    }

    #[test]
    fn test_regnet_x_vs_y_differences() {
        let regnetx = RegNetConfig::regnetx_200mf();
        let regnety = RegNetConfig::regnety_200mf();
        
        assert!(!regnetx.use_se());
        assert!(regnety.use_se());
        println!("✓ RegNetX/Y SE difference validated");
    }

    #[test]
    fn test_grouped_convolution_config() {
        let config = RegNetBlockConfig::new(64, 128, 2, 8);
        assert_eq!(config.groups(), 8);
        assert_eq!(config.out_channels() % config.groups(), 0);
        println!("✓ Grouped convolution configuration valid");
    }
}

fn main() {
    println!("=== RegNet TDD Red Phase ===");
    println!("Running comprehensive failing tests...");
    
    // Run a few basic tests manually to show they work
    let config = RegNetConfig::regnetx_200mf();
    println!("RegNetX-200MF: w_a={}, w_0={}, group_width={}, use_se={}", 
             config.w_a(), config.w_0(), config.group_width(), config.use_se());
    
    let config_y = RegNetConfig::regnety_200mf();
    println!("RegNetY-200MF: w_a={}, w_0={}, group_width={}, use_se={}", 
             config_y.w_a(), config_y.w_0(), config_y.group_width(), config_y.use_se());
    
    println!("\nRed phase complete! All implementation methods should panic when called.");
    println!("Next: Implement minimal functionality to make tests pass (Green phase)");
}