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use indexmap::map::IndexMap;
use itertools::Itertools;

use crate::{base, proto, Warnable};
use crate::base::{DataType, IndexKey, NodeProperties, SensitivitySpace, Value, ValueProperties, ArrayProperties};
use crate::components::{Accuracy, Component, Expandable, Mechanism, Sensitivity};
use crate::errors::*;
use crate::utilities::{expand_mechanism, prepend};
use crate::utilities::privacy::{get_epsilon, privacy_usage_check, spread_privacy_usage};

impl Component for proto::LaplaceMechanism {
    fn propagate_property(
        &self,
        privacy_definition: &Option<proto::PrivacyDefinition>,
        _public_arguments: IndexMap<base::IndexKey, &Value>,
        properties: base::NodeProperties,
        _node_id: u32,
    ) -> Result<Warnable<ValueProperties>> {

        let privacy_definition = privacy_definition.as_ref()
            .ok_or_else(|| "privacy_definition must be defined")?;

        if privacy_definition.protect_floating_point {
            return Err("Floating-point protections are enabled. The laplace mechanism is susceptible to floating-point attacks.".into())
        }

        let mut data_property: ArrayProperties = properties.get::<IndexKey>(&"data".into())
            .ok_or("data: missing")?.array()
            .map_err(prepend("data:"))?.clone();

        if data_property.data_type != DataType::Float && data_property.data_type != DataType::Int {
            return Err("data: atomic type must be numeric".into());
        }

        let aggregator = data_property.aggregator.clone()
            .ok_or_else(|| Error::from("aggregator: missing"))?;

        // sensitivity must be computable
        aggregator.component.compute_sensitivity(
            privacy_definition,
            &aggregator.properties,
            &SensitivitySpace::KNorm(1))?.array()?.cast_float()?;

        // make sure lipschitz constants are available as a float array
        aggregator.lipschitz_constants.array()?.cast_float()?;

        let privacy_usage = self.privacy_usage.iter().cloned().map(Ok)
            .fold1(|l, r| l? + r?).ok_or_else(|| "privacy_usage: must be defined")??;

        let warnings = privacy_usage_check(
            &privacy_usage,
            data_property.num_records,
            privacy_definition.strict_parameter_checks)?;

        data_property.releasable = true;
        data_property.aggregator = None;

        Ok(Warnable(data_property.into(), warnings))
    }
}


impl Expandable for proto::LaplaceMechanism {
    fn expand_component(
        &self,
        privacy_definition: &Option<proto::PrivacyDefinition>,
        component: &proto::Component,
        _public_arguments: &IndexMap<IndexKey, &Value>,
        properties: &base::NodeProperties,
        component_id: u32,
        maximum_id: u32,
    ) -> Result<base::ComponentExpansion> {
        expand_mechanism(
            &SensitivitySpace::KNorm(1),
            privacy_definition,
            self.privacy_usage.as_ref(),
            component,
            properties,
            component_id,
            maximum_id
        )
    }
}

impl Mechanism for proto::LaplaceMechanism {
    fn get_privacy_usage(
        &self,
        privacy_definition: &proto::PrivacyDefinition,
        release_usage: Option<&Vec<proto::PrivacyUsage>>,
        properties: &NodeProperties
    ) -> Result<Option<Vec<proto::PrivacyUsage>>> {
        let data_property = properties.get::<IndexKey>(&"data".into())
            .ok_or("data: missing")?.array()
            .map_err(prepend("data:"))?;

        Some(release_usage.unwrap_or_else(|| &self.privacy_usage).iter()
            .map(|usage| usage.effective_to_actual(
                data_property.sample_proportion.unwrap_or(1.),
                data_property.c_stability,
                privacy_definition.group_size))
            .collect::<Result<Vec<proto::PrivacyUsage>>>()).transpose()
    }
}


impl Accuracy for proto::LaplaceMechanism {
    fn accuracy_to_privacy_usage(
        &self,
        accuracies: &proto::Accuracies,
        mut public_arguments: IndexMap<base::IndexKey, &Value>
    ) -> Result<Option<Vec<proto::PrivacyUsage>>> {
        // take max sensitivity of each column
        let sensitivities: Vec<_> = public_arguments.remove(&IndexKey::from("sensitivity"))
            .ok_or_else(|| Error::from("sensitivity: missing in accuracy"))?.clone()
            .array()?.cast_float()?
            .gencolumns().into_iter()
            .map(|sensitivity_col| sensitivity_col.into_iter().copied().fold1(|l, r| l.max(r)).unwrap())
            .collect();

        Ok(Some(sensitivities.into_iter().zip(accuracies.values.iter())
            .map(|(sensitivity, accuracy)| proto::PrivacyUsage {
                distance: Some(proto::privacy_usage::Distance::Approximate(proto::privacy_usage::DistanceApproximate {
                    epsilon: (1. / accuracy.alpha).ln() * (sensitivity as f64 / accuracy.value),
                    delta: 0.,
                }))
            })
            .collect()))
    }

    fn privacy_usage_to_accuracy(
        &self,
        mut public_arguments: IndexMap<base::IndexKey, &Value>,
        alpha: f64
    ) -> Result<Option<Vec<proto::Accuracy>>> {

        // take max sensitivity of each column
        let sensitivities: Vec<_> = public_arguments.remove(&IndexKey::from("sensitivity"))
            .ok_or_else(|| Error::from("sensitivity: missing in accuracy"))?.clone()
            .array()?.cast_float()?
            .gencolumns().into_iter()
            .map(|sensitivity_col| sensitivity_col.into_iter().copied().fold1(|l, r| l.max(r)).unwrap())
            .collect();

        let usages = spread_privacy_usage(&self.privacy_usage, sensitivities.len())?;
        let epsilons = usages.iter().map(get_epsilon).collect::<Result<Vec<f64>>>()?;

        Ok(Some(sensitivities.into_iter().zip(epsilons.into_iter())
            .map(|(sensitivity, epsilon)| proto::Accuracy {
                value: (1. / alpha).ln() * (sensitivity as f64 / epsilon),
                alpha,
            })
            .collect()))
    }
}