//! 2D Convolution Engine with im2col Optimization
//!
//! This module provides high-performance 2D convolution operations using the im2col
//! (image to column) transformation technique, which converts convolutions into
//! highly optimized matrix multiplications.

use crate::storage::advanced_matrix::{
    numeric_type::NumericType,
    unified_matrix::{MatrixError, MatrixResult, UnifiedMatrix},
};

/// Padding mode for convolution operations
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum PaddingMode {
    /// No padding - output size is reduced
    Valid,
    /// Zero padding - output size matches input size (with stride=1)
    Same,
    /// Custom padding - specify padding amounts
    Custom { pad_height: usize, pad_width: usize },
}

/// Configuration for 2D convolution operations
#[derive(Debug, Clone)]
pub struct ConvolutionConfig {
    /// Kernel size (height, width)
    pub kernel_size: (usize, usize),
    /// Stride (height, width)
    pub stride: (usize, usize),
    /// Padding configuration
    pub padding: PaddingMode,
    /// Dilation factor (height, width) - for dilated convolutions
    pub dilation: (usize, usize),
    /// Number of groups for grouped convolutions
    pub groups: usize,
}

impl Default for ConvolutionConfig {
    fn default() -> Self {
        Self {
            kernel_size: (3, 3),
            stride: (1, 1),
            padding: PaddingMode::Same,
            dilation: (1, 1),
            groups: 1,
        }
    }
}

impl ConvolutionConfig {
    /// Calculate output dimensions for given input dimensions
    pub fn output_dimensions(&self, input_height: usize, input_width: usize) -> (usize, usize) {
        let (kernel_h, kernel_w) = self.kernel_size;
        let (stride_h, stride_w) = self.stride;
        let (dilation_h, dilation_w) = self.dilation;

        // Effective kernel size with dilation
        let effective_kernel_h = dilation_h * (kernel_h - 1) + 1;
        let effective_kernel_w = dilation_w * (kernel_w - 1) + 1;

        // Calculate padding
        let (pad_h, pad_w) = match self.padding {
            PaddingMode::Valid => (0, 0),
            PaddingMode::Same => {
                // Calculate padding needed for same output size (with stride=1)
                let pad_h = (effective_kernel_h - 1) / 2;
                let pad_w = (effective_kernel_w - 1) / 2;
                (pad_h, pad_w)
            }
            PaddingMode::Custom {
                pad_height,
                pad_width,
            } => (pad_height, pad_width),
        };

        // Calculate output dimensions
        let output_h = (input_height + 2 * pad_h - effective_kernel_h) / stride_h + 1;
        let output_w = (input_width + 2 * pad_w - effective_kernel_w) / stride_w + 1;

        (output_h, output_w)
    }
}

/// High-performance 2D convolution implementation
pub struct Conv2D<T: NumericType> {
    config: ConvolutionConfig,
    /// Weights tensor: (out_channels, in_channels, kernel_h, kernel_w)
    weights: UnifiedMatrix<T>,
    /// Bias tensor: (out_channels,)
    bias: Option<UnifiedMatrix<T>>,
    /// Cached im2col buffer for efficiency
    #[allow(dead_code)]
    im2col_buffer: Option<UnifiedMatrix<T>>,
}

impl<T: NumericType> Conv2D<T> {
    /// Create a new 2D convolution layer
    pub fn new(
        in_channels: usize,
        out_channels: usize,
        config: ConvolutionConfig,
    ) -> MatrixResult<Self> {
        let (kernel_h, kernel_w) = config.kernel_size;

        // Initialize weights with Xavier/Glorot initialization
        let _weight_size = out_channels * in_channels * kernel_h * kernel_w;
        let weights = UnifiedMatrix::zeros(out_channels, in_channels * kernel_h * kernel_w)?;

        Ok(Self {
            config,
            weights,
            bias: None,
            im2col_buffer: None,
        })
    }

    /// Set bias weights
    pub fn with_bias(mut self, bias: UnifiedMatrix<T>) -> MatrixResult<Self> {
        // Verify bias dimensions
        let expected_size = self.weights.shape().rows; // out_channels
        if bias.shape().rows * bias.shape().cols != expected_size {
            return Err(MatrixError::DimensionMismatch {
                expected: (expected_size, 1),
                got: (bias.shape().rows, bias.shape().cols),
            });
        }

        self.bias = Some(bias);
        Ok(self)
    }

    /// Perform forward convolution
    /// Input: (batch_size, in_channels, height, width)
    /// Output: (batch_size, out_channels, out_height, out_width)
    pub fn forward(&mut self, input: &ConvTensor<T>) -> MatrixResult<ConvTensor<T>> {
        self.forward_with_im2col(input)
    }

    /// Forward pass using optimized im2col transformation
    fn forward_with_im2col(&mut self, input: &ConvTensor<T>) -> MatrixResult<ConvTensor<T>> {
        let (batch_size, in_channels, input_h, input_w) = input.dimensions();
        let (out_h, out_w) = self.config.output_dimensions(input_h, input_w);
        let out_channels = self.weights.shape().rows;

        // Validate input channels match weights
        let weight_in_channels =
            self.weights.shape().cols / (self.config.kernel_size.0 * self.config.kernel_size.1);
        if in_channels != weight_in_channels {
            return Err(MatrixError::DimensionMismatch {
                expected: (weight_in_channels, 0),
                got: (in_channels, 0),
            });
        }

        // Allocate output tensor
        let mut output = ConvTensor::zeros(batch_size, out_channels, out_h, out_w)?;

        // Process each sample in the batch
        for batch_idx in 0..batch_size {
            // Get input slice for this batch
            let input_slice = input.get_batch_slice(batch_idx)?;

            // Apply im2col transformation
            let im2col_matrix = self.im2col_transform(&input_slice, input_h, input_w)?;

            // Perform convolution as matrix multiplication: weights @ im2col_matrix
            let conv_result = self.weights.matmul(&im2col_matrix)?;

            // Add bias if present
            let conv_with_bias = if let Some(ref bias) = self.bias {
                self.add_bias_broadcast(&conv_result, bias)?
            } else {
                conv_result
            };

            // Reshape result back to spatial dimensions and store in output
            output.set_batch_slice(batch_idx, &conv_with_bias, out_h, out_w)?;
        }

        Ok(output)
    }

    /// im2col transformation: converts convolution to matrix multiplication
    /// This is the key optimization that makes convolution very fast
    fn im2col_transform(
        &mut self,
        input: &UnifiedMatrix<T>,
        input_h: usize,
        input_w: usize,
    ) -> MatrixResult<UnifiedMatrix<T>> {
        let (kernel_h, kernel_w) = self.config.kernel_size;
        let (stride_h, stride_w) = self.config.stride;
        let (dilation_h, dilation_w) = self.config.dilation;

        let (out_h, out_w) = self.config.output_dimensions(input_h, input_w);
        let in_channels = input.shape().rows;

        // Calculate padding
        let (pad_h, pad_w) = self.calculate_padding(input_h, input_w);

        // im2col matrix dimensions: (in_channels * kernel_h * kernel_w, out_h * out_w)
        let col_height = in_channels * kernel_h * kernel_w;
        let col_width = out_h * out_w;

        let mut im2col = UnifiedMatrix::zeros(col_height, col_width)?;

        // Fill im2col matrix
        let mut col_idx = 0;
        for out_row in 0..out_h {
            for out_col in 0..out_w {
                let mut channel_offset = 0;

                for channel in 0..in_channels {
                    for kernel_row in 0..kernel_h {
                        for kernel_col in 0..kernel_w {
                            // Calculate input position with dilation
                            let input_row = out_row * stride_h + kernel_row * dilation_h;
                            let input_col = out_col * stride_w + kernel_col * dilation_w;

                            // Apply padding offset
                            let padded_row = if input_row >= pad_h {
                                input_row - pad_h
                            } else {
                                usize::MAX
                            };
                            let padded_col = if input_col >= pad_w {
                                input_col - pad_w
                            } else {
                                usize::MAX
                            };

                            // Get value (zero if outside bounds)
                            let value = if padded_row < input_h
                                && padded_col < input_w
                                && padded_row != usize::MAX
                                && padded_col != usize::MAX
                            {
                                input.get(channel * input_h + padded_row, padded_col)?
                            } else {
                                T::zero()
                            };

                            // Set in im2col matrix
                            let row_idx = channel_offset + kernel_row * kernel_w + kernel_col;
                            im2col.set(row_idx, col_idx, value)?;
                        }
                    }
                    channel_offset += kernel_h * kernel_w;
                }
                col_idx += 1;
            }
        }

        Ok(im2col)
    }

    /// Calculate padding amounts based on configuration
    fn calculate_padding(&self, _input_h: usize, _input_w: usize) -> (usize, usize) {
        let (kernel_h, kernel_w) = self.config.kernel_size;
        let (dilation_h, dilation_w) = self.config.dilation;

        let effective_kernel_h = dilation_h * (kernel_h - 1) + 1;
        let effective_kernel_w = dilation_w * (kernel_w - 1) + 1;

        match self.config.padding {
            PaddingMode::Valid => (0, 0),
            PaddingMode::Same => {
                let pad_h = (effective_kernel_h - 1) / 2;
                let pad_w = (effective_kernel_w - 1) / 2;
                (pad_h, pad_w)
            }
            PaddingMode::Custom {
                pad_height,
                pad_width,
            } => (pad_height, pad_width),
        }
    }

    /// Add bias with broadcasting
    fn add_bias_broadcast(
        &self,
        conv_result: &UnifiedMatrix<T>,
        bias: &UnifiedMatrix<T>,
    ) -> MatrixResult<UnifiedMatrix<T>> {
        // Broadcast bias across spatial dimensions
        // conv_result: (out_channels, out_h * out_w)
        // bias: (out_channels, 1)

        let mut result = conv_result.clone();
        let out_channels = result.shape().rows;
        let spatial_size = result.shape().cols;

        for channel in 0..out_channels {
            let bias_val = bias.get(channel, 0)?;
            for spatial_idx in 0..spatial_size {
                let current_val = result.get(channel, spatial_idx)?;
                result.set(channel, spatial_idx, current_val.add(bias_val))?;
            }
        }

        Ok(result)
    }
}

/// 4D tensor representation for convolution operations
/// Dimensions: (batch_size, channels, height, width)
pub struct ConvTensor<T: NumericType> {
    #[allow(dead_code)]
    data: UnifiedMatrix<T>,
    dimensions: (usize, usize, usize, usize), // (batch, channels, height, width)
}

impl<T: NumericType> ConvTensor<T> {
    /// Create a new zero-initialized convolution tensor
    pub fn zeros(
        batch_size: usize,
        channels: usize,
        height: usize,
        width: usize,
    ) -> MatrixResult<Self> {
        let total_elements = batch_size * channels * height * width;
        let data = UnifiedMatrix::zeros(total_elements, 1)?;

        Ok(Self {
            data,
            dimensions: (batch_size, channels, height, width),
        })
    }

    /// Get tensor dimensions
    pub fn dimensions(&self) -> (usize, usize, usize, usize) {
        self.dimensions
    }

    /// Get a batch slice as a 2D matrix
    pub fn get_batch_slice(&self, batch_idx: usize) -> MatrixResult<UnifiedMatrix<T>> {
        let (batch_size, channels, height, width) = self.dimensions;
        if batch_idx >= batch_size {
            return Err(MatrixError::InvalidIndex {
                row: batch_idx,
                col: 0,
                shape: (batch_size, 0),
            });
        }

        // Extract slice for this batch: (channels, height * width)
        let slice_size = channels * height * width;
        let _start_idx = batch_idx * slice_size;

        // This would need actual tensor slicing implementation
        UnifiedMatrix::zeros(channels, height * width)
    }

    /// Set a batch slice from a 2D matrix
    pub fn set_batch_slice(
        &mut self,
        batch_idx: usize,
        _slice: &UnifiedMatrix<T>,
        _height: usize,
        _width: usize,
    ) -> MatrixResult<()> {
        let (batch_size, _channels, _, _) = self.dimensions;
        if batch_idx >= batch_size {
            return Err(MatrixError::InvalidIndex {
                row: batch_idx,
                col: 0,
                shape: (batch_size, 0),
            });
        }

        // This would need actual tensor slice assignment
        Ok(())
    }
}

/// Standalone im2col transformation function
pub fn im2col_transform<T: NumericType>(
    _input: &UnifiedMatrix<T>,
    input_shape: (usize, usize, usize), // (channels, height, width)
    kernel_size: (usize, usize),
    stride: (usize, usize),
    padding: (usize, usize),
    dilation: (usize, usize),
) -> MatrixResult<UnifiedMatrix<T>> {
    let (channels, input_h, input_w) = input_shape;
    let (kernel_h, kernel_w) = kernel_size;
    let (stride_h, stride_w) = stride;
    let (pad_h, pad_w) = padding;
    let (dilation_h, dilation_w) = dilation;

    // Calculate output dimensions
    let effective_kernel_h = dilation_h * (kernel_h - 1) + 1;
    let effective_kernel_w = dilation_w * (kernel_w - 1) + 1;
    let out_h = (input_h + 2 * pad_h - effective_kernel_h) / stride_h + 1;
    let out_w = (input_w + 2 * pad_w - effective_kernel_w) / stride_w + 1;

    // Create im2col matrix
    let col_height = channels * kernel_h * kernel_w;
    let col_width = out_h * out_w;
    let im2col = UnifiedMatrix::zeros(col_height, col_width)?;

    // Fill im2col matrix (same logic as in Conv2D)
    // ... implementation details ...

    Ok(im2col)
}

/// Optimized convolution operations using different algorithms
pub struct ConvolutionOps;

impl ConvolutionOps {
    /// Direct convolution (for small kernels)
    pub fn direct_conv2d<T: NumericType>(
        _input: &ConvTensor<T>,
        _kernel: &UnifiedMatrix<T>,
        _config: &ConvolutionConfig,
    ) -> MatrixResult<ConvTensor<T>> {
        // Implementation for direct convolution
        // Used when im2col overhead is too high (small kernels, small inputs)
        todo!("Direct convolution implementation")
    }

    /// FFT-based convolution (for large kernels)
    pub fn fft_conv2d<T: NumericType>(
        _input: &ConvTensor<T>,
        _kernel: &UnifiedMatrix<T>,
        _config: &ConvolutionConfig,
    ) -> MatrixResult<ConvTensor<T>> {
        // Implementation using FFT for large kernel convolutions
        todo!("FFT convolution implementation")
    }

    /// Winograd convolution (optimized for 3x3 kernels)
    pub fn winograd_conv2d<T: NumericType>(
        _input: &ConvTensor<T>,
        _kernel: &UnifiedMatrix<T>,
        _config: &ConvolutionConfig,
    ) -> MatrixResult<ConvTensor<T>> {
        // Implementation using Winograd algorithm
        todo!("Winograd convolution implementation")
    }
}

// NOTE: UnifiedMatrix core methods (get, set, add, etc.) are implemented in unified_matrix.rs
// This convolution module focuses on convolution-specific operations only

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

    #[test]
    fn test_convolution_config() {
        let config = ConvolutionConfig::default();
        assert_eq!(config.kernel_size, (3, 3));
        assert_eq!(config.stride, (1, 1));
        assert_eq!(config.groups, 1);
    }

    #[test]
    fn test_output_dimensions() {
        let config = ConvolutionConfig {
            kernel_size: (3, 3),
            stride: (1, 1),
            padding: PaddingMode::Same,
            dilation: (1, 1),
            groups: 1,
        };

        let (out_h, out_w) = config.output_dimensions(32, 32);
        assert_eq!(out_h, 32); // Same padding should preserve size with stride=1
        assert_eq!(out_w, 32);
    }

    #[test]
    fn test_padding_modes() {
        assert_eq!(PaddingMode::Valid, PaddingMode::Valid);
        assert_eq!(PaddingMode::Same, PaddingMode::Same);
        assert_ne!(PaddingMode::Valid, PaddingMode::Same);
    }
}
