//! RegNet architecture implementation
//!
//! RegNet is a family of efficient convolutional neural networks designed using network design spaces.

use rtx_tensor::{Device, Result, Tensor};

/// RegNet configuration
#[derive(Debug, Clone)]
pub struct RegNetConfig {
    pub depth: usize,
    pub width: usize,
    pub num_classes: usize,
    pub stem_width: usize,
}

impl Default for RegNetConfig {
    fn default() -> Self {
        Self {
            depth: 22,
            width: 48,
            num_classes: 1000,
            stem_width: 32,
        }
    }
}

/// RegNet Block configuration
#[derive(Debug, Clone)]
pub struct RegNetBlockConfig {
    pub in_channels: usize,
    pub out_channels: usize,
    pub stride: usize,
    pub groups: usize,
}

impl RegNetBlockConfig {
    pub fn new(in_channels: usize, out_channels: usize, stride: usize, groups: usize) -> Self {
        Self {
            in_channels,
            out_channels,
            stride,
            groups,
        }
    }
}

/// RegNet Block
#[derive(Debug)]
pub struct RegNetBlock {
    config: RegNetBlockConfig,
    device: Device,
}

impl RegNetBlock {
    pub fn new(config: RegNetBlockConfig) -> Self {
        Self {
            config,
            device: Device::default(),
        }
    }

    pub fn forward(&self, input: &Tensor) -> Result<Tensor> {
        Ok(input.clone())
    }
}

/// RegNet Stage configuration
#[derive(Debug, Clone)]
pub struct RegNetStageConfig {
    pub in_channels: usize,
    pub out_channels: usize,
    pub num_blocks: usize,
    pub stride: usize,
}

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

/// RegNet Stage
#[derive(Debug)]
pub struct RegNetStage {
    config: RegNetStageConfig,
    device: Device,
}

impl RegNetStage {
    pub fn new(config: RegNetStageConfig) -> Self {
        Self {
            config,
            device: Device::default(),
        }
    }

    pub fn forward(&self, input: &Tensor) -> Result<Tensor> {
        Ok(input.clone())
    }
}

/// RegNet Stem
#[derive(Debug)]
pub struct RegNetStem {
    out_channels: usize,
    device: Device,
}

impl RegNetStem {
    pub fn new(out_channels: usize) -> Self {
        Self {
            out_channels,
            device: Device::default(),
        }
    }

    pub fn forward(&self, input: &Tensor) -> Result<Tensor> {
        Ok(input.clone())
    }
}

/// RegNet Head
#[derive(Debug)]
pub struct RegNetHead {
    in_features: usize,
    num_classes: usize,
    device: Device,
}

impl RegNetHead {
    pub fn new(in_features: usize, num_classes: usize) -> Self {
        Self {
            in_features,
            num_classes,
            device: Device::default(),
        }
    }

    pub fn forward(&self, input: &Tensor) -> Result<Tensor> {
        Ok(input.clone())
    }
}

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

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

/// Squeeze-and-Excitation Module
#[derive(Debug)]
pub struct SEModule {
    config: SEModuleConfig,
    device: Device,
}

impl SEModule {
    pub fn new(config: SEModuleConfig) -> Self {
        Self {
            config,
            device: Device::default(),
        }
    }

    pub fn forward(&self, input: &Tensor) -> Result<Tensor> {
        Ok(input.clone())
    }
}

/// RegNet architecture
pub struct RegNet {
    config: RegNetConfig,
    device: Device,
}

impl RegNet {
    /// Create new RegNet model
    pub fn new(config: RegNetConfig) -> Result<Self> {
        Ok(RegNet {
            config,
            device: Device::default(),
        })
    }

    /// Create new RegNet model with device
    pub fn with_device(config: RegNetConfig, device: Device) -> Result<Self> {
        Ok(RegNet { config, device })
    }

    /// Forward pass through the network
    pub fn forward(&self, input: &Tensor) -> Result<Tensor> {
        // Placeholder implementation
        Ok(input.clone())
    }
}

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

    #[test]
    #[ignore = "RegNet requires CUDA device"]
    fn test_regnet() -> Result<()> {
        let device = Device::cuda(0)?;
        let config = RegNetConfig::default();
        let _model = RegNet::new(config)?;
        Ok(())
    }
}