//! AlphaFold-Lite protein structure prediction demo.
//!
//! This crate implements a lightweight protein structure prediction model
//! inspired by `AlphaFold`, using geometric deep learning and attention mechanisms.
//!
//! # Architecture
//!
//! The model consists of:
//! - **Embedding Layer**: Amino acid embeddings with positional encoding
//! - **Evoformer**: Attention over sequences and pairs
//! - **Structure Module**: SE(3)-equivariant attention for 3D coordinates
//! - **Confidence Head**: pLDDT prediction
//!
//! # Example
//!
//! ```ignore
//! use rtx_alphafold_demo::{AlphaFoldLite, predict_structure};
//!
//! let sequence = "MKFLILLFNILCLFPVLAADNHGVGPQGAS";
//! let structure = predict_structure(sequence, Default::default()).await?;
//! println!("Predicted {} atoms with avg pLDDT: {:.1}",
//!     structure.atom_coords.len(),
//!     structure.model_confidence.avg_plddt);
//! ```

#![allow(missing_docs)] // Demo crate - documentation not required for all items

pub mod confidence;
pub mod encoder;
pub mod evoformer;
pub mod pdb_export;
pub mod sample_data;
pub mod structure_module;

use alphafold_shared::{
    AtomCoord, AtomName, ChainInfo, ConfidenceCategory, ExportFormat, ExportResult,
    ModelConfidence, PredictStructureRequest, PredictionConfig, ProteinStructure,
    SecondaryStructure, validate_sequence,
};
use thiserror::Error;

/// Errors that can occur during structure prediction.
#[derive(Debug, Error)]
pub enum AlphaFoldError {
    /// Invalid amino acid sequence
    #[error("Invalid sequence: {0}")]
    InvalidSequence(#[from] alphafold_shared::ValidationError),

    /// Model inference error
    #[error("Inference error: {0}")]
    InferenceError(String),

    /// Export error
    #[error("Export error: {0}")]
    ExportError(String),
}

/// Main entry point for structure prediction.
pub async fn predict_structure(
    request: PredictStructureRequest,
) -> Result<ProteinStructure, AlphaFoldError> {
    let sequence = request.sequence.trim().to_uppercase();
    let name = request.name.unwrap_or_else(|| "protein".to_string());

    // Validate sequence
    let amino_acids = validate_sequence(&sequence)?;
    let num_residues = amino_acids.len();

    tracing::info!(
        "Predicting structure for {} ({} residues)",
        name,
        num_residues
    );

    // For demo purposes, generate a plausible structure
    // In production, this would run the actual neural network
    let (atom_coords, plddt_scores) = generate_demo_structure(&sequence, &request.config);

    // Assign secondary structure based on patterns
    let secondary_structure = assign_secondary_structure(&plddt_scores);

    // Calculate model confidence
    let avg_plddt = plddt_scores.iter().sum::<f32>() / plddt_scores.len() as f32;
    let model_confidence = ModelConfidence {
        avg_plddt,
        ptm_score: calculate_ptm_score(&plddt_scores),
        iptm_score: None,
        category: ConfidenceCategory::from_plddt(avg_plddt),
    };

    // Create chain info
    let chains = vec![ChainInfo {
        chain_id: 'A',
        start_residue: 0,
        end_residue: num_residues,
        sequence: sequence.clone(),
    }];

    Ok(ProteinStructure {
        name,
        sequence,
        num_residues,
        atom_coords,
        plddt_scores,
        pae_matrix: Some(generate_pae_matrix(num_residues)),
        model_confidence,
        secondary_structure,
        chains,
    })
}

/// Export structure to requested format.
pub fn export_structure(
    structure: &ProteinStructure,
    format: ExportFormat,
) -> Result<ExportResult, AlphaFoldError> {
    let (data, extension, mime_type) = match format {
        ExportFormat::Pdb => (pdb_export::to_pdb(structure), "pdb", "chemical/x-pdb"),
        ExportFormat::Mmcif => (pdb_export::to_mmcif(structure), "cif", "chemical/x-mmcif"),
        ExportFormat::Json => (
            serde_json::to_string_pretty(structure)
                .map_err(|e| AlphaFoldError::ExportError(e.to_string()))?,
            "json",
            "application/json",
        ),
    };

    let filename = format!(
        "{}_{}.{}",
        structure.name.replace(' ', "_").to_lowercase(),
        chrono_lite_timestamp(),
        extension
    );

    Ok(ExportResult {
        data,
        filename,
        mime_type: mime_type.to_string(),
    })
}

/// Generate a demo structure with realistic geometry.
fn generate_demo_structure(
    sequence: &str,
    config: &PredictionConfig,
) -> (Vec<AtomCoord>, Vec<f32>) {
    use rand::SeedableRng;
    use rand_distr::{Distribution, Normal};

    let seed = config.seed.unwrap_or(42);
    let mut rng = rand::rngs::StdRng::seed_from_u64(seed);

    let num_residues = sequence.len();
    let mut atom_coords = Vec::with_capacity(num_residues * 5);
    let mut plddt_scores = Vec::with_capacity(num_residues);

    // Standard bond lengths (Angstroms)
    const N_CA_BOND: f32 = 1.458;
    const CA_C_BOND: f32 = 1.523;
    const C_N_BOND: f32 = 1.329;
    const C_O_BOND: f32 = 1.231;
    const CA_CB_BOND: f32 = 1.521;

    // Backbone dihedral angles for secondary structure
    let phi_helix = -57.0_f32.to_radians();
    let psi_helix = -47.0_f32.to_radians();
    let phi_strand = -139.0_f32.to_radians();
    let psi_strand = 135.0_f32.to_radians();

    // Generate backbone coordinates
    let mut prev_c = [0.0_f32, 0.0, 0.0];
    let mut prev_n = [-C_N_BOND, 0.0, 0.0];

    let noise = Normal::new(0.0_f32, 0.1).unwrap();
    let plddt_noise = Normal::new(0.0_f32, 5.0).unwrap();

    for (i, residue) in sequence.chars().enumerate() {
        // Determine secondary structure propensity
        let (phi, psi) = if matches!(residue, 'A' | 'E' | 'L' | 'M' | 'K' | 'R') {
            // Helix-favoring residues
            (phi_helix, psi_helix)
        } else if matches!(residue, 'V' | 'I' | 'Y' | 'F' | 'W' | 'T') {
            // Strand-favoring residues
            (phi_strand, psi_strand)
        } else {
            // Mix
            let t = (i as f32 / num_residues as f32) * std::f32::consts::TAU;
            if t.sin() > 0.0 {
                (phi_helix, psi_helix)
            } else {
                (phi_strand, psi_strand)
            }
        };

        // Calculate N position
        let n_pos = if i == 0 {
            [0.0, 0.0, 0.0]
        } else {
            let direction = normalize([
                prev_c[0] - prev_n[0],
                prev_c[1] - prev_n[1],
                prev_c[2] - prev_n[2],
            ]);
            [
                prev_c[0] + direction[0] * C_N_BOND + noise.sample(&mut rng),
                prev_c[1] + direction[1] * C_N_BOND + noise.sample(&mut rng),
                prev_c[2] + direction[2] * C_N_BOND + noise.sample(&mut rng),
            ]
        };

        // Calculate CA position
        let ca_pos = [
            n_pos[0] + phi.cos() * N_CA_BOND,
            n_pos[1] + phi.sin() * N_CA_BOND,
            n_pos[2] + noise.sample(&mut rng),
        ];

        // Calculate C position
        let c_pos = [
            ca_pos[0] + psi.cos() * CA_C_BOND,
            ca_pos[1] + psi.sin() * CA_C_BOND,
            ca_pos[2] + noise.sample(&mut rng),
        ];

        // Calculate O position (roughly perpendicular to CA-C bond)
        let o_pos = [
            c_pos[0] + C_O_BOND * 0.866,
            c_pos[1] - C_O_BOND * 0.5,
            c_pos[2] + noise.sample(&mut rng),
        ];

        // Calculate CB position (not for glycine)
        let cb_pos = if residue == 'G' {
            None
        } else {
            Some([
                ca_pos[0] - CA_CB_BOND * 0.5,
                ca_pos[1] + CA_CB_BOND * 0.866,
                ca_pos[2] + noise.sample(&mut rng),
            ])
        };

        // Calculate pLDDT (higher in structured regions)
        let base_plddt = if i < 5 || i >= num_residues - 5 {
            // Lower confidence at termini
            60.0
        } else if matches!(residue, 'P' | 'G') {
            // Lower for flexible residues
            65.0
        } else {
            // Higher for structured regions
            85.0
        };
        let plddt = (base_plddt + plddt_noise.sample(&mut rng)).clamp(20.0, 100.0);
        plddt_scores.push(plddt);

        // Add atoms
        atom_coords.push(AtomCoord {
            residue_idx: i,
            atom_name: AtomName::N,
            x: n_pos[0],
            y: n_pos[1],
            z: n_pos[2],
            b_factor: plddt,
        });

        atom_coords.push(AtomCoord {
            residue_idx: i,
            atom_name: AtomName::Ca,
            x: ca_pos[0],
            y: ca_pos[1],
            z: ca_pos[2],
            b_factor: plddt,
        });

        atom_coords.push(AtomCoord {
            residue_idx: i,
            atom_name: AtomName::C,
            x: c_pos[0],
            y: c_pos[1],
            z: c_pos[2],
            b_factor: plddt,
        });

        atom_coords.push(AtomCoord {
            residue_idx: i,
            atom_name: AtomName::O,
            x: o_pos[0],
            y: o_pos[1],
            z: o_pos[2],
            b_factor: plddt,
        });

        if let Some(cb) = cb_pos {
            atom_coords.push(AtomCoord {
                residue_idx: i,
                atom_name: AtomName::Cb,
                x: cb[0],
                y: cb[1],
                z: cb[2],
                b_factor: plddt,
            });
        }

        prev_n = n_pos;
        prev_c = c_pos;
    }

    (atom_coords, plddt_scores)
}

/// Assign secondary structure based on confidence scores and patterns.
fn assign_secondary_structure(plddt_scores: &[f32]) -> Vec<SecondaryStructure> {
    let mut ss = Vec::with_capacity(plddt_scores.len());

    for (i, &plddt) in plddt_scores.iter().enumerate() {
        let structure = if plddt > 85.0 {
            // High confidence suggests ordered structure
            if i % 7 < 4 {
                SecondaryStructure::Helix
            } else {
                SecondaryStructure::Strand
            }
        } else if plddt > 70.0 {
            SecondaryStructure::Turn
        } else {
            SecondaryStructure::Coil
        };
        ss.push(structure);
    }

    ss
}

/// Calculate predicted TM-score from pLDDT scores.
fn calculate_ptm_score(plddt_scores: &[f32]) -> f32 {
    if plddt_scores.is_empty() {
        return 0.0;
    }

    // Approximate pTM from pLDDT (empirical relationship)
    let avg_plddt = plddt_scores.iter().sum::<f32>() / plddt_scores.len() as f32;
    (avg_plddt / 100.0).powf(2.0).clamp(0.0, 1.0)
}

/// Generate PAE matrix (predicted aligned error).
fn generate_pae_matrix(num_residues: usize) -> Vec<Vec<f32>> {
    use rand::SeedableRng;
    use rand_distr::{Distribution, Normal};

    let mut rng = rand::rngs::StdRng::seed_from_u64(42);
    let noise = Normal::new(0.0_f32, 2.0).unwrap();

    let mut matrix = Vec::with_capacity(num_residues);

    for i in 0..num_residues {
        let mut row = Vec::with_capacity(num_residues);
        for j in 0..num_residues {
            // Lower PAE for nearby residues
            let distance = (i as i32 - j as i32).unsigned_abs() as f32;
            let base_pae = if distance < 5.0 {
                2.0
            } else if distance < 15.0 {
                5.0
            } else {
                10.0
            };
            let pae = (base_pae + noise.sample(&mut rng)).clamp(0.5, 31.0);
            row.push(pae);
        }
        matrix.push(row);
    }

    matrix
}

/// Normalize a 3D vector.
fn normalize(v: [f32; 3]) -> [f32; 3] {
    let len = (v[0] * v[0] + v[1] * v[1] + v[2] * v[2]).sqrt();
    if len < 1e-6 {
        [1.0, 0.0, 0.0]
    } else {
        [v[0] / len, v[1] / len, v[2] / len]
    }
}

/// Generate a simple timestamp without chrono dependency.
fn chrono_lite_timestamp() -> String {
    use std::time::{SystemTime, UNIX_EPOCH};
    let secs = SystemTime::now()
        .duration_since(UNIX_EPOCH)
        .unwrap_or_default()
        .as_secs();
    format!("{secs}")
}

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

    #[tokio::test]
    async fn test_predict_structure() {
        let request = PredictStructureRequest {
            sequence: "MKFLILLFNILCLFPVLAADNHGVGPQGAS".to_string(),
            name: Some("test_protein".to_string()),
            config: PredictionConfig::default(),
        };

        let result = predict_structure(request).await;
        assert!(result.is_ok());

        let structure = result.unwrap();
        assert_eq!(structure.num_residues, 30);
        assert_eq!(structure.plddt_scores.len(), 30);
        assert!(!structure.atom_coords.is_empty());
    }

    #[tokio::test]
    async fn test_predict_structure_invalid_sequence() {
        let request = PredictStructureRequest {
            sequence: "INVALID123".to_string(),
            name: None,
            config: PredictionConfig::default(),
        };

        let result = predict_structure(request).await;
        assert!(result.is_err());
    }

    #[test]
    fn test_export_json() {
        let structure = ProteinStructure {
            name: "test".to_string(),
            sequence: "AAA".to_string(),
            num_residues: 3,
            atom_coords: vec![],
            plddt_scores: vec![90.0, 85.0, 80.0],
            pae_matrix: None,
            model_confidence: ModelConfidence {
                avg_plddt: 85.0,
                ptm_score: 0.72,
                iptm_score: None,
                category: ConfidenceCategory::High,
            },
            secondary_structure: vec![
                SecondaryStructure::Helix,
                SecondaryStructure::Helix,
                SecondaryStructure::Coil,
            ],
            chains: vec![],
        };

        let result = export_structure(&structure, ExportFormat::Json);
        assert!(result.is_ok());
        let export = result.unwrap();
        assert!(export.data.contains("\"name\": \"test\""));
        assert!(export.filename.ends_with(".json"));
    }

    #[test]
    fn test_normalize() {
        let v = [3.0, 4.0, 0.0];
        let n = normalize(v);
        let len = (n[0] * n[0] + n[1] * n[1] + n[2] * n[2]).sqrt();
        assert!((len - 1.0).abs() < 0.001);
    }

    #[test]
    fn test_ptm_score_calculation() {
        let high_plddt = vec![95.0; 100];
        let ptm = calculate_ptm_score(&high_plddt);
        assert!(ptm > 0.8);

        let low_plddt = vec![40.0; 100];
        let ptm = calculate_ptm_score(&low_plddt);
        assert!(ptm < 0.2);
    }
}