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use std::collections::HashMap;
use std::ops::Add;

use nalgebra as na;
use pyo3::prelude::*;

use crate::{problem, LinearSolver};

pub trait Optimizer {
    fn optimize(
        &self,
        problem: &problem::Problem,
        initial_values: &HashMap<String, na::DVector<f64>>,
        optimizer_option: Option<OptimizerOptions>,
    ) -> HashMap<String, na::DVector<f64>>;
    fn apply_dx(
        &self,
        dx: &na::DVector<f64>,
        params: &mut HashMap<String, na::DVector<f64>>,
        variable_name_to_col_idx_dict: &HashMap<String, usize>,
    ) {
        for (key, param) in params.iter_mut() {
            if let Some(col_idx) = variable_name_to_col_idx_dict.get(key) {
                let var_size = param.shape().0;
                let updated_param = param.clone().add(dx.rows(*col_idx, var_size));
                param.copy_from(&updated_param);
            }
        }
    }
}

#[derive(PartialEq, Debug)]
#[pyclass]
pub enum SolverStatus {
    Running,
    // Resulting solution may be OK to use.
    GradientTooSmall,         // eps > max(J'*f(x))
    RelativeStepSizeTooSmall, // eps > ||dx|| / ||x||
    ErrorTooSmall,            // eps > ||f(x)||
    HitMaxIterations,
    // Numerical issues
    // FAILED_TO_EVALUATE_COST_FUNCTION,
    // FAILED_TO_SOLVER_LINEAR_SYSTEM,
}

#[pyclass]
#[derive(Clone)]
pub struct OptimizerOptions {
    pub max_iteration: usize,
    pub linear_solver_type: LinearSolver,
    pub verbosity_level: usize,
    pub min_abs_error_decrease_threshold: f64,
    pub min_rel_error_decrease_threshold: f64,
    pub min_error_threshold: f64,
    // pub relative_step_threshold: 1e-16,
}

impl Default for OptimizerOptions {
    fn default() -> Self {
        OptimizerOptions {
            max_iteration: 100,
            linear_solver_type: LinearSolver::SparseCholesky,
            verbosity_level: 0,
            min_abs_error_decrease_threshold: 1e-5,
            min_rel_error_decrease_threshold: 1e-5,
            min_error_threshold: 1e-10,
        }
    }
}