//! Structure module for 3D coordinate prediction.
//!
//! Implements SE(3)-equivariant attention (Invariant Point Attention)
//! for predicting backbone atom coordinates.

use crate::encoder::SequenceEmbedding;
use alphafold_shared::{AtomCoord, AtomName};

/// Number of IPA iterations.
pub const NUM_IPA_ITERATIONS: usize = 8;

/// Backbone frame representation.
#[derive(Debug, Clone, Copy)]
pub struct BackboneFrame {
    /// Rotation matrix (3x3)
    pub rotation: [[f32; 3]; 3],
    /// Translation vector
    pub translation: [f32; 3],
}

impl Default for BackboneFrame {
    fn default() -> Self {
        Self {
            rotation: [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]],
            translation: [0.0, 0.0, 0.0],
        }
    }
}

impl BackboneFrame {
    /// Apply frame transformation to a point.
    #[must_use]
    pub fn apply(&self, point: [f32; 3]) -> [f32; 3] {
        let rotated = [
            self.rotation[0][0] * point[0]
                + self.rotation[0][1] * point[1]
                + self.rotation[0][2] * point[2],
            self.rotation[1][0] * point[0]
                + self.rotation[1][1] * point[1]
                + self.rotation[1][2] * point[2],
            self.rotation[2][0] * point[0]
                + self.rotation[2][1] * point[1]
                + self.rotation[2][2] * point[2],
        ];
        [
            rotated[0] + self.translation[0],
            rotated[1] + self.translation[1],
            rotated[2] + self.translation[2],
        ]
    }

    /// Compose two frames: self * other.
    #[must_use]
    pub fn compose(&self, other: &BackboneFrame) -> BackboneFrame {
        // R_new = R_self * R_other
        let mut rotation = [[0.0_f32; 3]; 3];
        for i in 0..3 {
            for j in 0..3 {
                for k in 0..3 {
                    rotation[i][j] += self.rotation[i][k] * other.rotation[k][j];
                }
            }
        }

        // t_new = R_self * t_other + t_self
        let translation = self.apply(other.translation);

        BackboneFrame {
            rotation,
            translation,
        }
    }

    /// Create frame from axis-angle rotation.
    #[must_use]
    pub fn from_axis_angle(axis: [f32; 3], angle: f32) -> Self {
        let c = angle.cos();
        let s = angle.sin();
        let t = 1.0 - c;

        let rotation = [
            [
                t * axis[0] * axis[0] + c,
                t * axis[0] * axis[1] - s * axis[2],
                t * axis[0] * axis[2] + s * axis[1],
            ],
            [
                t * axis[0] * axis[1] + s * axis[2],
                t * axis[1] * axis[1] + c,
                t * axis[1] * axis[2] - s * axis[0],
            ],
            [
                t * axis[0] * axis[2] - s * axis[1],
                t * axis[1] * axis[2] + s * axis[0],
                t * axis[2] * axis[2] + c,
            ],
        ];

        BackboneFrame {
            rotation,
            translation: [0.0, 0.0, 0.0],
        }
    }
}

/// Invariant Point Attention module.
#[derive(Debug, Clone)]
pub struct InvariantPointAttention {
    /// Number of query points
    num_query_points: usize,
    /// Number of attention heads
    num_heads: usize,
    /// Head dimension
    head_dim: usize,
}

impl Default for InvariantPointAttention {
    fn default() -> Self {
        Self::new(4, 8, 16)
    }
}

impl InvariantPointAttention {
    /// Create a new IPA module.
    #[must_use]
    pub fn new(num_query_points: usize, num_heads: usize, head_dim: usize) -> Self {
        Self {
            num_query_points,
            num_heads,
            head_dim,
        }
    }

    /// Apply IPA to update sequence embeddings and frames.
    pub fn forward(
        &self,
        seq_emb: &SequenceEmbedding,
        frames: &mut [BackboneFrame],
    ) -> Vec<Vec<f32>> {
        let seq_len = seq_emb.sequence_length;
        let mut outputs = vec![vec![0.0_f32; seq_emb.embedding_dim]; seq_len];

        // Simplified IPA: compute attention based on spatial distance
        for i in 0..seq_len {
            for j in 0..seq_len {
                // Compute spatial distance between frames
                let dist = distance(&frames[i].translation, &frames[j].translation);

                // Distance-based attention (simplified)
                let attention = (-dist / 10.0).exp();

                // Accumulate weighted embeddings
                if let Some(emb_j) = seq_emb.get(j) {
                    for (k, &v) in emb_j.iter().enumerate() {
                        outputs[i][k] += attention * v;
                    }
                }
            }

            // Normalize
            let norm: f32 = outputs[i].iter().map(|x| x * x).sum::<f32>().sqrt() + 1e-6;
            for x in &mut outputs[i] {
                *x /= norm;
            }
        }

        outputs
    }
}

/// Structure module that predicts 3D coordinates.
#[derive(Debug, Clone)]
pub struct StructureModule {
    /// IPA layers
    ipa_layers: Vec<InvariantPointAttention>,
    /// Number of recycles
    num_recycles: usize,
}

impl StructureModule {
    /// Create a new structure module.
    #[must_use]
    pub fn new(num_layers: usize, num_recycles: usize) -> Self {
        let ipa_layers = (0..num_layers)
            .map(|_| InvariantPointAttention::default())
            .collect();

        Self {
            ipa_layers,
            num_recycles,
        }
    }

    /// Predict 3D structure from sequence embeddings.
    #[must_use]
    pub fn predict(
        &self,
        seq_emb: &SequenceEmbedding,
        sequence: &str,
    ) -> (Vec<AtomCoord>, Vec<f32>) {
        let seq_len = seq_emb.sequence_length;

        // Initialize frames (identity)
        let mut frames: Vec<BackboneFrame> =
            (0..seq_len).map(|_| BackboneFrame::default()).collect();

        // Initialize frame positions along a line
        for (i, frame) in frames.iter_mut().enumerate() {
            frame.translation = [i as f32 * 3.8, 0.0, 0.0]; // ~3.8Å per residue
        }

        // Recycle through IPA layers
        for _ in 0..self.num_recycles {
            for ipa in &self.ipa_layers {
                let updates = ipa.forward(seq_emb, &mut frames);

                // Update frames based on IPA output
                for (i, update) in updates.iter().enumerate() {
                    // Convert update to frame adjustment (simplified)
                    let angle_x = update.first().copied().unwrap_or(0.0) * 0.1;
                    let angle_y = update.get(1).copied().unwrap_or(0.0) * 0.1;

                    let delta_frame = BackboneFrame::from_axis_angle([1.0, 0.0, 0.0], angle_x);
                    let delta_frame2 = BackboneFrame::from_axis_angle([0.0, 1.0, 0.0], angle_y);

                    frames[i] = frames[i].compose(&delta_frame).compose(&delta_frame2);
                }
            }
        }

        // Convert frames to atom coordinates
        self.frames_to_atoms(&frames, sequence)
    }

    /// Convert backbone frames to atom coordinates.
    fn frames_to_atoms(
        &self,
        frames: &[BackboneFrame],
        sequence: &str,
    ) -> (Vec<AtomCoord>, Vec<f32>) {
        // Standard backbone atom positions in local frame (Angstroms)
        let n_local = [-1.458, 0.0, 0.0];
        let ca_local = [0.0, 0.0, 0.0];
        let c_local = [1.523, 0.0, 0.0];
        let o_local = [2.0, 1.0, 0.0];
        let cb_local = [-0.5, 1.5, 0.0];

        let mut atoms = Vec::with_capacity(frames.len() * 5);
        let mut plddt_scores = Vec::with_capacity(frames.len());

        for (i, (frame, residue)) in frames.iter().zip(sequence.chars()).enumerate() {
            // Generate pLDDT based on frame stability (simplified)
            let stability = frame.rotation[0][0] + frame.rotation[1][1] + frame.rotation[2][2];
            let plddt = (50.0 + stability * 15.0).clamp(20.0, 100.0);
            plddt_scores.push(plddt);

            // Transform backbone atoms
            let n_pos = frame.apply(n_local);
            atoms.push(AtomCoord {
                residue_idx: i,
                atom_name: AtomName::N,
                x: n_pos[0],
                y: n_pos[1],
                z: n_pos[2],
                b_factor: plddt,
            });

            let ca_pos = frame.apply(ca_local);
            atoms.push(AtomCoord {
                residue_idx: i,
                atom_name: AtomName::Ca,
                x: ca_pos[0],
                y: ca_pos[1],
                z: ca_pos[2],
                b_factor: plddt,
            });

            let c_pos = frame.apply(c_local);
            atoms.push(AtomCoord {
                residue_idx: i,
                atom_name: AtomName::C,
                x: c_pos[0],
                y: c_pos[1],
                z: c_pos[2],
                b_factor: plddt,
            });

            let o_pos = frame.apply(o_local);
            atoms.push(AtomCoord {
                residue_idx: i,
                atom_name: AtomName::O,
                x: o_pos[0],
                y: o_pos[1],
                z: o_pos[2],
                b_factor: plddt,
            });

            // CB for non-glycine
            if residue != 'G' {
                let cb_pos = frame.apply(cb_local);
                atoms.push(AtomCoord {
                    residue_idx: i,
                    atom_name: AtomName::Cb,
                    x: cb_pos[0],
                    y: cb_pos[1],
                    z: cb_pos[2],
                    b_factor: plddt,
                });
            }
        }

        (atoms, plddt_scores)
    }
}

/// Compute Euclidean distance between two points.
fn distance(a: &[f32; 3], b: &[f32; 3]) -> f32 {
    let dx = a[0] - b[0];
    let dy = a[1] - b[1];
    let dz = a[2] - b[2];
    (dx * dx + dy * dy + dz * dz).sqrt()
}

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

    #[test]
    fn test_backbone_frame_default() {
        let frame = BackboneFrame::default();
        let point = [1.0, 2.0, 3.0];
        let transformed = frame.apply(point);
        assert_eq!(transformed, point); // Identity transform
    }

    #[test]
    fn test_frame_translation() {
        let mut frame = BackboneFrame::default();
        frame.translation = [1.0, 2.0, 3.0];

        let point = [0.0, 0.0, 0.0];
        let transformed = frame.apply(point);
        assert_eq!(transformed, [1.0, 2.0, 3.0]);
    }

    #[test]
    fn test_frame_composition() {
        let frame1 = BackboneFrame {
            rotation: [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]],
            translation: [1.0, 0.0, 0.0],
        };
        let frame2 = BackboneFrame {
            rotation: [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]],
            translation: [0.0, 1.0, 0.0],
        };

        let composed = frame1.compose(&frame2);
        assert!((composed.translation[0] - 1.0).abs() < 0.001);
        assert!((composed.translation[1] - 1.0).abs() < 0.001);
    }

    #[test]
    fn test_structure_module() {
        use crate::encoder::ProteinEncoder;

        let encoder = ProteinEncoder::new();
        let seq_emb = encoder.encode("ACDEF");

        let structure_module = StructureModule::new(2, 1);
        let (atoms, plddt) = structure_module.predict(&seq_emb, "ACDEF");

        assert!(!atoms.is_empty());
        assert_eq!(plddt.len(), 5);
    }

    #[test]
    fn test_ipa() {
        use crate::encoder::ProteinEncoder;

        let encoder = ProteinEncoder::new();
        let seq_emb = encoder.encode("ACDEF");
        let mut frames: Vec<BackboneFrame> = (0..5).map(|_| BackboneFrame::default()).collect();

        let ipa = InvariantPointAttention::default();
        let output = ipa.forward(&seq_emb, &mut frames);

        assert_eq!(output.len(), 5);
    }
}