//! Functional API for neural network operations

use crate::Result;
use rtx_tensor::Tensor;

/// Linear transformation: y = xW^T + b
pub fn linear(input: &Tensor, weight: &Tensor, bias: Option<&Tensor>) -> Result<Tensor> {
    let output = input.matmul(&weight.transpose(0, 1)?)?;
    if let Some(bias) = bias {
        output.add(bias).map_err(Into::into)
    } else {
        Ok(output)
    }
}

/// ReLU activation function
pub fn relu(input: &Tensor) -> Result<Tensor> {
    input
        .maximum(&Tensor::zeros_like(input)?)
        .map_err(Into::into)
}

/// GELU activation function (simplified)
pub fn gelu(input: &Tensor) -> Result<Tensor> {
    // Simplified: just return input for now
    Ok(input.clone())
}

/// Softmax function
pub fn softmax(input: &Tensor, dim: i32) -> Result<Tensor> {
    Ok(input.softmax(dim)?)
}

/// 2D convolution operation (placeholder)
pub fn conv2d(
    input: &Tensor,
    weight: &Tensor,
    bias: Option<&Tensor>,
    _stride: (usize, usize),
    _padding: (usize, usize),
) -> Result<Tensor> {
    // Placeholder: just do matrix multiplication
    let output = input.matmul(weight)?;
    if let Some(bias) = bias {
        output.add(bias).map_err(Into::into)
    } else {
        Ok(output)
    }
}

/// Dropout function (placeholder)
pub fn dropout(input: &Tensor, _p: f32, training: bool) -> Result<Tensor> {
    if training {
        // Placeholder: just return input for now
        Ok(input.clone())
    } else {
        Ok(input.clone())
    }
}