use super::crt_decomposition;
use crate::dag::operator::Precision;
use crate::noise_estimator::error::{
    error_probability_of_sigma_scale, safe_variance_bound_product_1padbit,
    sigma_scale_of_error_probability,
};
use crate::noise_estimator::operators::atomic_pattern as noise_atomic_pattern;
use crate::optimization::atomic_pattern;
use crate::optimization::atomic_pattern::OptimizationDecompositionsConsts;

use crate::optimization::config::{Config, SearchSpace};
use crate::optimization::decomposition::circuit_bootstrap::CbComplexityNoise;
use crate::optimization::decomposition::{DecompCaches, PersistDecompCaches};
use crate::parameters::{BrDecompositionParameters, GlweParameters};
use concrete_commons::dispersion::{DispersionParameter, Variance};

pub fn find_p_error(kappa: f64, variance_bound: f64, current_maximum_noise: f64) -> f64 {
    let sigma = Variance(variance_bound).get_standard_dev() * kappa;
    let sigma_scale = sigma / Variance(current_maximum_noise).get_standard_dev();
    error_probability_of_sigma_scale(sigma_scale)
}

#[derive(Clone, Debug)]
pub struct OptimizationState {
    pub best_solution: Option<Solution>,
}

#[derive(Clone, Debug)]
pub struct Solution {
    pub input_lwe_dimension: u64,
    //n_big
    pub internal_ks_output_lwe_dimension: u64,
    //n_small
    pub ks_decomposition_level_count: u64,
    //l(KS)
    pub ks_decomposition_base_log: u64,
    //b(KS)
    pub glwe_polynomial_size: u64,
    //N
    pub glwe_dimension: u64,
    //k
    pub br_decomposition_level_count: u64,
    //l(BR)
    pub br_decomposition_base_log: u64,
    //b(BR)
    pub complexity: f64,
    pub noise_max: f64,
    pub p_error: f64,
    pub global_p_error: f64,
    // error probability
    pub cb_decomposition_level_count: u64,
    pub cb_decomposition_base_log: u64,
    pub crt_decomposition: Vec<u64>,
}

impl Solution {
    pub fn init() -> Self {
        Self {
            input_lwe_dimension: 0,
            internal_ks_output_lwe_dimension: 0,
            ks_decomposition_level_count: 0,
            ks_decomposition_base_log: 0,
            glwe_polynomial_size: 0,
            glwe_dimension: 0,
            br_decomposition_level_count: 0,
            br_decomposition_base_log: 0,
            complexity: 0.,
            noise_max: 0.0,
            p_error: 0.0,
            global_p_error: 0.0,
            cb_decomposition_level_count: 0,
            cb_decomposition_base_log: 0,
            crt_decomposition: vec![],
        }
    }
}

impl From<Solution> for atomic_pattern::Solution {
    fn from(sol: Solution) -> Self {
        Self {
            input_lwe_dimension: sol.input_lwe_dimension,
            internal_ks_output_lwe_dimension: sol.internal_ks_output_lwe_dimension,
            ks_decomposition_level_count: sol.ks_decomposition_level_count,
            ks_decomposition_base_log: sol.ks_decomposition_base_log,
            glwe_polynomial_size: sol.glwe_polynomial_size,
            glwe_dimension: sol.glwe_dimension,
            br_decomposition_level_count: sol.br_decomposition_level_count,
            br_decomposition_base_log: sol.br_decomposition_base_log,
            complexity: sol.complexity,
            noise_max: sol.noise_max,
            p_error: sol.p_error,
            global_p_error: sol.global_p_error,
        }
    }
}

fn estimate_variance(
    br_variance: f64,
    pp_variance: f64,
    cb_decomp: &CbComplexityNoise,
    ks_variance: f64,
    variance_modulus_switching: f64,
    log_norm: f64,
    precisions_sum: u64,
    max_precision: u64,
) -> f64 {
    assert!(max_precision <= precisions_sum);
    let variance_ggsw = pp_variance + br_variance / 2.;
    let variance_coeff_1_cmux_tree =
        2_f64.powf(2. * log_norm)            // variance_coeff for the multisum
        * (precisions_sum                    // for hybrid packing
        << (2 * (max_precision - 1))) as f64 // for left shift
    ;
    let variance_one_external_product_for_cmux_tree = cb_decomp.variance_from_ggsw(variance_ggsw);
    variance_modulus_switching
        + variance_coeff_1_cmux_tree * variance_one_external_product_for_cmux_tree
        + ks_variance
}

fn estimate_complexity(
    glwe_params: &GlweParameters,
    br_cost: f64,
    pp_cost: f64,
    cb_decomp: &CbComplexityNoise,
    ks_cost: f64,
    precisions_sum: u64,
    nb_blocks: u64,
    n_functions: u64,
) -> f64 {
    // Pbs dans BitExtract et Circuit BS et FP-KS (partagés)
    // Hybrid packing
    let cb_level = cb_decomp.decomp.level;
    let complexity_1_cmux_hp = cb_decomp.complexity_one_cmux_hp;
    let complexity_1_ggsw_to_fft = cb_decomp.complexity_one_ggsw_to_fft;
    // BitExtract use br
    let complexity_bit_extract_1_pbs = br_cost;
    let complexity_bit_extract_wo_ks =
        (precisions_sum - nb_blocks) as f64 * complexity_bit_extract_1_pbs;

    // Hybrid packing
    // Circuit bs: fp-ks
    let complexity_ppks = pp_cost;
    let complexity_all_ppks =
        ((glwe_params.glwe_dimension + 1) * cb_level * precisions_sum) as f64 * complexity_ppks;

    // Circuit bs: pbs
    let complexity_all_pbs = (precisions_sum * cb_level) as f64 * br_cost;

    let complexity_circuit_bs = complexity_all_pbs + complexity_all_ppks;

    // Hybrid packing (Do we have 1 or 2 groups)
    let log2_polynomial_size = glwe_params.log2_polynomial_size;
    // Size of cmux_group, can be zero
    let cmux_group_count = if precisions_sum > log2_polynomial_size {
        2f64.powi((precisions_sum - log2_polynomial_size - 1) as i32)
    } else {
        0.0
    };
    let complexity_cmux_tree = cmux_group_count * complexity_1_cmux_hp;

    let complexity_all_ggsw_to_fft = precisions_sum as f64 * complexity_1_ggsw_to_fft;

    // Hybrid packing blind rotate
    let complexity_g_br =
        complexity_1_cmux_hp * u64::min(glwe_params.log2_polynomial_size, precisions_sum) as f64;

    let complexity_hybrid_packing = complexity_cmux_tree + complexity_g_br;

    let complexity_multi_hybrid_packing =
        n_functions as f64 * complexity_hybrid_packing + complexity_all_ggsw_to_fft;

    let complexity_all_ks = precisions_sum as f64 * ks_cost;

    complexity_bit_extract_wo_ks
        + complexity_circuit_bs
        + complexity_multi_hybrid_packing
        + complexity_all_ks
}

#[allow(clippy::too_many_lines)]
fn update_state_with_best_decompositions(
    state: &mut OptimizationState,
    consts: &OptimizationDecompositionsConsts,
    glwe_params: GlweParameters,
    internal_dim: u64,
    n_functions: u64,
    partitionning: &[u64],
    caches: &mut DecompCaches,
) {
    let ciphertext_modulus_log = consts.config.ciphertext_modulus_log;
    let precisions_sum = partitionning.iter().copied().sum();
    let max_precision = partitionning.iter().copied().max().unwrap();
    let nb_blocks = partitionning.len() as u64;

    let safe_variance_bound = consts.safe_variance;
    let log_norm = consts.noise_factor.log2();

    let variance_modulus_switching =
        noise_atomic_pattern::estimate_modulus_switching_noise_with_binary_key(
            internal_dim,
            glwe_params.polynomial_size(),
            ciphertext_modulus_log,
        )
        .get_variance();

    if variance_modulus_switching > consts.safe_variance {
        return;
    }

    let mut best_complexity = state
        .best_solution
        .as_ref()
        .map_or(f64::INFINITY, |s| s.complexity);
    let mut best_variance = state
        .best_solution
        .as_ref()
        .map_or(f64::INFINITY, |s| s.noise_max);

    let pareto_blind_rotate = caches
        .blind_rotate
        .pareto_quantities(glwe_params, internal_dim);

    let pareto_keyswitch = caches
        .keyswitch
        .pareto_quantities(glwe_params, internal_dim);

    let pp_switch = caches
        .pp_switch
        .pareto_quantities(glwe_params, internal_dim);

    let pareto_cb = caches.cb_pbs.pareto_quantities(glwe_params);

    let lower_bound_variance_blind_rotate = pareto_blind_rotate.last().unwrap().noise;
    let lower_bound_variance_keyswitch = pareto_keyswitch.last().unwrap().noise;
    let lower_bound_variance_private_packing = pp_switch.last().unwrap().noise;
    let lower_pareto_cb_bias = pareto_cb
        .iter()
        .map(|cb| cb.variance_bias)
        .reduce(f64::min)
        .unwrap();
    let lower_pareto_cb_slope = pareto_cb
        .iter()
        .map(|cb| cb.variance_ggsw_factor)
        .reduce(f64::min)
        .unwrap();
    let lower_bound_cost_blind_rotate = pareto_blind_rotate[0].complexity;
    let lower_bound_cost_keyswitch = pareto_keyswitch[0].complexity;
    let lower_bound_cost_pp = pp_switch[0].complexity;
    let lower_bound_cost_cb_complexity_1_cmux_hp = pareto_cb
        .iter()
        .map(|cb| cb.complexity_one_cmux_hp)
        .reduce(f64::min)
        .unwrap_or(0.0);
    let lower_bound_cost_cb_complexity_1_ggsw_to_fft = pareto_cb
        .iter()
        .map(|cb| cb.complexity_one_ggsw_to_fft)
        .reduce(f64::min)
        .unwrap_or(0.0);
    let lower_bound_cb = CbComplexityNoise {
        decomp: BrDecompositionParameters {
            level: 1,
            log2_base: 1,
        },
        complexity_one_cmux_hp: lower_bound_cost_cb_complexity_1_cmux_hp,
        complexity_one_ggsw_to_fft: lower_bound_cost_cb_complexity_1_ggsw_to_fft,
        variance_bias: lower_pareto_cb_bias,
        variance_ggsw_factor: lower_pareto_cb_slope,
    };

    let variance = |br_variance: Option<_>,
                    pp_variance: Option<_>,
                    cb_decomp: Option<&CbComplexityNoise>,
                    ks_variance: Option<_>| {
        let br_variance = br_variance.unwrap_or(lower_bound_variance_blind_rotate);
        let pp_variance = pp_variance.unwrap_or(lower_bound_variance_private_packing);
        let cb_decomp = cb_decomp.unwrap_or(&lower_bound_cb);
        let ks_variance = ks_variance.unwrap_or(lower_bound_variance_keyswitch);
        estimate_variance(
            br_variance,
            pp_variance,
            cb_decomp,
            ks_variance,
            variance_modulus_switching,
            log_norm,
            precisions_sum,
            max_precision,
        )
    };

    let lower_bound_variance = variance(None, None, None, None);
    if lower_bound_variance > consts.safe_variance {
        // saves 20%
        return;
    }

    let complexity = |br_cost: Option<_>,
                      pp_cost: Option<_>,
                      cb_decomp: Option<&CbComplexityNoise>,
                      ks_cost: Option<_>| {
        // Pbs dans BitExtract et Circuit BS et FP-KS (partagés)
        let br_cost = br_cost.unwrap_or(lower_bound_cost_blind_rotate);
        let ks_cost = ks_cost.unwrap_or(lower_bound_cost_keyswitch);
        let pp_cost = pp_cost.unwrap_or(lower_bound_cost_pp);
        let cb_decomp = cb_decomp.unwrap_or(&lower_bound_cb);
        estimate_complexity(
            &glwe_params,
            br_cost,
            pp_cost,
            cb_decomp,
            ks_cost,
            precisions_sum,
            nb_blocks,
            n_functions,
        )
    };

    // BlindRotate dans Circuit BS
    for (br_i, shared_br_decomp) in pareto_blind_rotate.iter().enumerate() {
        let lower_bound_variance = variance(Some(shared_br_decomp.noise), None, None, None);

        if lower_bound_variance > consts.safe_variance {
            // saves 20%
            continue;
        }

        // Circuit Boostrap
        // private packing keyswitch, <=> FP-KS
        let pp_switching_index = br_i;
        let pp_switching = pp_switch[pp_switching_index];

        let lower_bound_variance = variance(
            Some(shared_br_decomp.noise),
            Some(pp_switching.noise),
            None,
            None,
        );
        if lower_bound_variance > safe_variance_bound {
            continue;
        }
        let lower_bound_complexity = complexity(
            Some(shared_br_decomp.complexity),
            Some(pp_switching.complexity),
            None,
            None,
        );
        if lower_bound_complexity > best_complexity {
            // saves ?? TODO
            // next br_decomp are at least as costly
            break;
        }

        // CircuitBootstrap: new parameters l,b
        // for &circuit_pbs_decomposition in pareto_circuit_pbs {
        for cb_decomp in pareto_cb {
            let lower_bound_variance = variance(
                Some(shared_br_decomp.noise),
                Some(pp_switching.noise),
                Some(cb_decomp),
                None,
            );
            if lower_bound_variance > safe_variance_bound {
                continue;
            }
            let lower_bound_complexity = complexity(
                Some(shared_br_decomp.complexity),
                Some(pp_switching.complexity),
                Some(cb_decomp),
                None,
            );
            if lower_bound_complexity > best_complexity {
                // saves 50%
                // next circuit_pbs_decomposition_parameter are at least as costly
                break;
            }
            // Shared by all pbs (like brs)
            for ks_decomp in pareto_keyswitch.iter().rev() {
                let variance_max = variance(
                    Some(shared_br_decomp.noise),
                    Some(pp_switching.noise),
                    Some(cb_decomp),
                    Some(ks_decomp.noise),
                );
                if variance_max > safe_variance_bound {
                    // saves 40%
                    break;
                }

                let complexity = complexity(
                    Some(shared_br_decomp.complexity),
                    Some(pp_switching.complexity),
                    Some(cb_decomp),
                    Some(ks_decomp.complexity),
                );

                if complexity > best_complexity {
                    continue;
                }

                #[allow(clippy::float_cmp)]
                if complexity == best_complexity && variance_max > best_variance {
                    continue;
                }

                let kappa = consts.kappa;
                best_complexity = complexity;
                best_variance = variance_max;
                let p_error = find_p_error(kappa, safe_variance_bound, variance_max);
                state.best_solution = Some(Solution {
                    input_lwe_dimension: glwe_params.sample_extract_lwe_dimension(),
                    internal_ks_output_lwe_dimension: internal_dim,
                    ks_decomposition_level_count: ks_decomp.decomp.level,
                    ks_decomposition_base_log: ks_decomp.decomp.log2_base,
                    glwe_polynomial_size: glwe_params.polynomial_size(),
                    glwe_dimension: glwe_params.glwe_dimension,
                    br_decomposition_level_count: shared_br_decomp.decomp.level,
                    br_decomposition_base_log: shared_br_decomp.decomp.log2_base,
                    noise_max: variance_max,
                    complexity,
                    p_error,
                    global_p_error: f64::NAN,
                    cb_decomposition_level_count: cb_decomp.decomp.level,
                    cb_decomposition_base_log: cb_decomp.decomp.log2_base,
                    crt_decomposition: vec![],
                });
            }
        }
    }
}

fn optimize_raw(
    log_norm: f64, // ?? norm2 of noise multisum, complexity of multisum is neglected
    config: Config,
    search_space: &SearchSpace,
    n_functions: u64, // Many functions at the same time, stay at 1 for start
    partitionning: &[u64],
    persistent_caches: &PersistDecompCaches,
) -> OptimizationState {
    assert!(0.0 < config.maximum_acceptable_error_probability);
    assert!(config.maximum_acceptable_error_probability < 1.0);
    assert!(!partitionning.is_empty());

    let ciphertext_modulus_log = config.ciphertext_modulus_log;

    // Circuit BS bound
    // 1 bit of message only here =)
    // Bound for first bit extract in BitExtract (dominate others)
    let max_block_precision = *partitionning.iter().max().unwrap();
    let safe_variance_bound = safe_variance_bound_product_1padbit(
        max_block_precision,
        ciphertext_modulus_log,
        config.maximum_acceptable_error_probability,
    );
    let kappa: f64 = sigma_scale_of_error_probability(config.maximum_acceptable_error_probability);

    let mut state = OptimizationState {
        best_solution: None,
    };

    let consts = OptimizationDecompositionsConsts {
        config,
        kappa,
        sum_size: 1, // Ignored
        noise_factor: log_norm.exp2(),
        safe_variance: safe_variance_bound,
    };

    let mut caches = persistent_caches.caches();

    for &glwe_dim in &search_space.glwe_dimensions {
        for &glwe_log_poly_size in &search_space.glwe_log_polynomial_sizes {
            let input_lwe_dimension = glwe_dim << glwe_log_poly_size;
            // Manual experimental CUT
            if input_lwe_dimension > 1 << 13 {
                continue;
            }

            let glwe_params = GlweParameters {
                log2_polynomial_size: glwe_log_poly_size,
                glwe_dimension: glwe_dim,
            };

            for &internal_dim in &search_space.internal_lwe_dimensions {
                update_state_with_best_decompositions(
                    &mut state,
                    &consts,
                    glwe_params,
                    internal_dim,
                    n_functions,
                    partitionning,
                    &mut caches,
                );
            }
        }
    }
    persistent_caches.backport(caches);

    state
}

pub fn optimize_one(
    precision: u64,
    config: Config,
    log_norm: f64,
    search_space: &SearchSpace,
    caches: &PersistDecompCaches,
) -> OptimizationState {
    let coprimes = crt_decomposition::default_coprimes(precision as Precision);
    let partitionning = crt_decomposition::precisions_from_coprimes(&coprimes);
    let n_functions = 1;
    let mut state = optimize_raw(
        log_norm,
        config,
        search_space,
        n_functions,
        &partitionning,
        caches,
    );
    state.best_solution = state.best_solution.map(|mut sol| -> Solution {
        sol.crt_decomposition = coprimes;
        sol
    });
    state
}

pub fn optimize_one_compat(
    _sum_size: u64,
    precision: u64,
    config: Config,
    noise_factor: f64,
    search_space: &SearchSpace,
    cache: &PersistDecompCaches,
) -> atomic_pattern::OptimizationState {
    let log_norm = noise_factor.log2();
    let result = optimize_one(precision, config, log_norm, search_space, cache);
    atomic_pattern::OptimizationState {
        best_solution: result.best_solution.map(Solution::into),
    }
}