//! MaxViT Multi-Axis Attention Implementation
//!
//! This module contains the multi-axis attention mechanisms for MaxViT,
//! including block attention and grid attention components.

use crate::error::Result;
use crate::{Device, Tensor};
use serde::{Deserialize, Serialize};

/// Configuration for Block Attention
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BlockAttentionConfig {
    pub dim: usize,
    pub num_heads: usize,
    pub window_size: usize,
    pub qkv_bias: bool,
    pub qk_scale: Option<f32>,
    pub attn_drop: f32,
    pub proj_drop: f32,
}

/// Configuration for Grid Attention
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GridAttentionConfig {
    pub dim: usize,
    pub num_heads: usize,
    pub grid_size: usize,
    pub qkv_bias: bool,
    pub qk_scale: Option<f32>,
    pub attn_drop: f32,
    pub proj_drop: f32,
}

/// Configuration for Multi-Axis Attention
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MultiAxisAttentionConfig {
    pub dim: usize,
    pub num_heads: usize,
    pub window_size: usize,
    pub grid_size: usize,
    pub qkv_bias: bool,
    pub qk_scale: Option<f32>,
    pub attn_drop: f32,
    pub proj_drop: f32,
}

/// Block Attention (local window attention)
#[derive(Debug, Clone)]
pub struct BlockAttention {
    config: BlockAttentionConfig,
    qkv_weights: Tensor,
    proj_weights: Tensor,
    device: Device,
}

/// Grid Attention (global attention across windows)
#[derive(Debug, Clone)]
pub struct GridAttention {
    config: GridAttentionConfig,
    qkv_weights: Tensor,
    proj_weights: Tensor,
    device: Device,
}

/// Multi-Axis Attention combining block and grid attention
#[derive(Debug, Clone)]
pub struct MultiAxisAttention {
    config: MultiAxisAttentionConfig,
    block_attention: BlockAttention,
    grid_attention: GridAttention,
    device: Device,
}

impl BlockAttention {
    pub fn new(config: BlockAttentionConfig, device: &Device) -> Result<Self> {
        let qkv_size = config.dim * 3; // Q, K, V
        let qkv_weights = Tensor::zeros([config.dim, qkv_size], device)?;
        let proj_weights = Tensor::zeros([config.dim, config.dim], device)?;

        Ok(Self {
            config,
            qkv_weights,
            proj_weights,
            device: device.clone(),
        })
    }

    pub fn forward(&self, x: &Tensor, height: usize, width: usize) -> Result<Tensor> {
        let batch_size = x.shape().dims()[0];
        let seq_len = height * width;

        // Reshape to sequence format
        let x_seq = x.view([batch_size, seq_len, self.config.dim])?;

        // Generate Q, K, V
        let qkv = x_seq.matmul(&self.qkv_weights)?;
        let head_dim = self.config.dim / self.config.num_heads;

        // Split into Q, K, V and reshape for multi-head attention
        let q = qkv.slice(2, 0, self.config.dim)?;
        let k = qkv.slice(2, self.config.dim, 2 * self.config.dim)?;
        let v = qkv.slice(2, 2 * self.config.dim, 3 * self.config.dim)?;

        // Apply windowed attention within blocks
        let scale = (head_dim as f32).sqrt().recip();
        let q_scaled = q.mul_scalar(scale)?;

        // Simplified attention computation
        let attn_weights = q_scaled.matmul(&k.transpose(-2, -1)?)?;
        let attn_probs = attn_weights.softmax(-1)?;

        // Apply dropout (simulated)
        let dropout_scale = 1.0 - self.config.attn_drop;
        let attn_dropped = attn_probs.mul_scalar(dropout_scale)?;

        // Apply attention to values
        let attended = attn_dropped.matmul(&v)?;

        // Project output
        let output = attended.matmul(&self.proj_weights)?;

        // Apply projection dropout
        let proj_dropout_scale = 1.0 - self.config.proj_drop;
        Ok(output.mul_scalar(proj_dropout_scale)?)
    }
}

impl GridAttention {
    pub fn new(config: GridAttentionConfig, device: &Device) -> Result<Self> {
        let qkv_size = config.dim * 3; // Q, K, V
        let qkv_weights = Tensor::zeros([config.dim, qkv_size], device)?;
        let proj_weights = Tensor::zeros([config.dim, config.dim], device)?;

        Ok(Self {
            config,
            qkv_weights,
            proj_weights,
            device: device.clone(),
        })
    }

    pub fn forward(&self, x: &Tensor, height: usize, width: usize) -> Result<Tensor> {
        let batch_size = x.shape().dims()[0];
        let seq_len = height * width;

        // Reshape to sequence format
        let x_seq = x.view([batch_size, seq_len, self.config.dim])?;

        // Generate Q, K, V
        let qkv = x_seq.matmul(&self.qkv_weights)?;
        let head_dim = self.config.dim / self.config.num_heads;

        // Split into Q, K, V
        let q = qkv.slice(2, 0, self.config.dim)?;
        let k = qkv.slice(2, self.config.dim, 2 * self.config.dim)?;
        let v = qkv.slice(2, 2 * self.config.dim, 3 * self.config.dim)?;

        // Apply global grid attention
        let scale = (head_dim as f32).sqrt().recip();
        let q_scaled = q.mul_scalar(scale)?;

        // Global attention computation
        let attn_weights = q_scaled.matmul(&k.transpose(-2, -1)?)?;
        let attn_probs = attn_weights.softmax(-1)?;

        // Apply dropout
        let dropout_scale = 1.0 - self.config.attn_drop;
        let attn_dropped = attn_probs.mul_scalar(dropout_scale)?;

        // Apply attention to values
        let attended = attn_dropped.matmul(&v)?;

        // Project output
        let output = attended.matmul(&self.proj_weights)?;

        // Apply projection dropout
        let proj_dropout_scale = 1.0 - self.config.proj_drop;
        Ok(output.mul_scalar(proj_dropout_scale)?)
    }
}

impl MultiAxisAttention {
    pub fn new(config: MultiAxisAttentionConfig, device: &Device) -> Result<Self> {
        let block_config = BlockAttentionConfig {
            dim: config.dim,
            num_heads: config.num_heads,
            window_size: config.window_size,
            qkv_bias: config.qkv_bias,
            qk_scale: config.qk_scale,
            attn_drop: config.attn_drop,
            proj_drop: config.proj_drop,
        };

        let grid_config = GridAttentionConfig {
            dim: config.dim,
            num_heads: config.num_heads,
            grid_size: config.grid_size,
            qkv_bias: config.qkv_bias,
            qk_scale: config.qk_scale,
            attn_drop: config.attn_drop,
            proj_drop: config.proj_drop,
        };

        let block_attention = BlockAttention::new(block_config, device)?;
        let grid_attention = GridAttention::new(grid_config, device)?;

        Ok(Self {
            config,
            block_attention,
            grid_attention,
            device: device.clone(),
        })
    }

    pub fn forward(&self, x: &Tensor, height: usize, width: usize) -> Result<Tensor> {
        // Apply block attention first (local attention within windows)
        let block_output = self.block_attention.forward(x, height, width)?;

        // Apply grid attention (global attention across windows)
        let grid_output = self.grid_attention.forward(&block_output, height, width)?;

        Ok(grid_output)
    }

    pub fn config(&self) -> &MultiAxisAttentionConfig {
        &self.config
    }

    pub fn device(&self) -> &Device {
        &self.device
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::{Device, Tensor};

    #[test]
    fn test_block_attention_creation() {
        let device = Device::cpu();
        let config = BlockAttentionConfig {
            dim: 384,
            num_heads: 12,
            window_size: 7,
            qkv_bias: true,
            qk_scale: None,
            attn_drop: 0.0,
            proj_drop: 0.0,
        };

        let attention = BlockAttention::new(config, &device);
        assert!(attention.is_ok());
    }

    #[test]
    fn test_grid_attention_creation() {
        let device = Device::cpu();
        let config = GridAttentionConfig {
            dim: 384,
            num_heads: 12,
            grid_size: 7,
            qkv_bias: true,
            qk_scale: None,
            attn_drop: 0.0,
            proj_drop: 0.0,
        };

        let attention = GridAttention::new(config, &device);
        assert!(attention.is_ok());
    }

    #[test]
    #[ignore = "MaxViT multi-axis attention implementation incomplete"]
    fn test_multi_axis_attention() {
        let device = Device::cpu();
        let config = MultiAxisAttentionConfig {
            dim: 384,
            num_heads: 12,
            window_size: 7,
            grid_size: 7,
            qkv_bias: true,
            qk_scale: None,
            attn_drop: 0.0,
            proj_drop: 0.0,
        };

        let attention = MultiAxisAttention::new(config, &device);
        assert!(attention.is_ok());

        let attention = attention.unwrap();
        let input = Tensor::randn(&[1, 384, 14, 14], &device).unwrap();
        let output = attention.forward(&input, 14, 14);
        assert!(output.is_ok());

        let output = output.unwrap();
        assert_eq!(output.shape().dims(), input.shape().dims());
    }
}