use std::borrow::Cow;
use std::collections::{HashMap, HashSet};
use std::fmt;

use anyhow::{bail, Context};
use fst::{IntoStreamer, Streamer};
use levenshtein_automata::DFA;
use levenshtein_automata::LevenshteinAutomatonBuilder as LevBuilder;
use log::debug;
use once_cell::sync::Lazy;
use roaring::bitmap::RoaringBitmap;

use crate::facet::FacetType;
use crate::heed_codec::facet::{FacetLevelValueF64Codec, FacetLevelValueI64Codec};
use crate::mdfs::Mdfs;
use crate::query_tokens::{QueryTokens, QueryToken};
use crate::{Index, FieldId, DocumentId, Criterion};

pub use self::facet::{FacetCondition, FacetNumberOperator, FacetStringOperator, Order};
pub use self::facet::facet_number_recurse;

// Building these factories is not free.
static LEVDIST0: Lazy<LevBuilder> = Lazy::new(|| LevBuilder::new(0, true));
static LEVDIST1: Lazy<LevBuilder> = Lazy::new(|| LevBuilder::new(1, true));
static LEVDIST2: Lazy<LevBuilder> = Lazy::new(|| LevBuilder::new(2, true));

mod facet;

pub struct Search<'a> {
    query: Option<String>,
    facet_condition: Option<FacetCondition>,
    offset: usize,
    limit: usize,
    rtxn: &'a heed::RoTxn<'a>,
    index: &'a Index,
}

impl<'a> Search<'a> {
    pub fn new(rtxn: &'a heed::RoTxn, index: &'a Index) -> Search<'a> {
        Search { query: None, facet_condition: None, offset: 0, limit: 20, rtxn, index }
    }

    pub fn query(&mut self, query: impl Into<String>) -> &mut Search<'a> {
        self.query = Some(query.into());
        self
    }

    pub fn offset(&mut self, offset: usize) -> &mut Search<'a> {
        self.offset = offset;
        self
    }

    pub fn limit(&mut self, limit: usize) -> &mut Search<'a> {
        self.limit = limit;
        self
    }

    pub fn facet_condition(&mut self, condition: FacetCondition) -> &mut Search<'a> {
        self.facet_condition = Some(condition);
        self
    }

    /// Extracts the query words from the query string and returns the DFAs accordingly.
    /// TODO introduce settings for the number of typos regarding the words lengths.
    fn generate_query_dfas(query: &str) -> Vec<(String, bool, DFA)> {
        let (lev0, lev1, lev2) = (&LEVDIST0, &LEVDIST1, &LEVDIST2);

        let words: Vec<_> = QueryTokens::new(query).collect();
        let ends_with_whitespace = query.chars().last().map_or(false, char::is_whitespace);
        let number_of_words = words.len();

        words.into_iter().enumerate().map(|(i, word)| {
            let (word, quoted) = match word {
                QueryToken::Free(word) => (word.to_lowercase(), word.len() <= 3),
                QueryToken::Quoted(word) => (word.to_lowercase(), true),
            };
            let is_last = i + 1 == number_of_words;
            let is_prefix = is_last && !ends_with_whitespace && !quoted;
            let lev = match word.len() {
                0..=4 => if quoted { lev0 } else { lev0 },
                5..=8 => if quoted { lev0 } else { lev1 },
                _     => if quoted { lev0 } else { lev2 },
            };

            let dfa = if is_prefix {
                lev.build_prefix_dfa(&word)
            } else {
                lev.build_dfa(&word)
            };

            (word, is_prefix, dfa)
        })
        .collect()
    }

    /// Fetch the words from the given FST related to the given DFAs along with
    /// the associated documents ids.
    fn fetch_words_docids(
        &self,
        fst: &fst::Set<Cow<[u8]>>,
        dfas: Vec<(String, bool, DFA)>,
    ) -> anyhow::Result<Vec<(HashMap<String, (u8, RoaringBitmap)>, RoaringBitmap)>>
    {
        // A Vec storing all the derived words from the original query words, associated
        // with the distance from the original word and the docids where the words appears.
        let mut derived_words = Vec::<(HashMap::<String, (u8, RoaringBitmap)>, RoaringBitmap)>::with_capacity(dfas.len());

        for (_word, _is_prefix, dfa) in dfas {

            let mut acc_derived_words = HashMap::new();
            let mut unions_docids = RoaringBitmap::new();
            let mut stream = fst.search_with_state(&dfa).into_stream();
            while let Some((word, state)) = stream.next() {

                let word = std::str::from_utf8(word)?;
                let docids = self.index.word_docids.get(self.rtxn, word)?.unwrap();
                let distance = dfa.distance(state);
                unions_docids.union_with(&docids);
                acc_derived_words.insert(word.to_string(), (distance.to_u8(), docids));
            }
            derived_words.push((acc_derived_words, unions_docids));
        }

        Ok(derived_words)
    }

    /// Returns the set of docids that contains all of the query words.
    fn compute_candidates(
        derived_words: &[(HashMap<String, (u8, RoaringBitmap)>, RoaringBitmap)],
    ) -> RoaringBitmap
    {
        // We sort the derived words by inverse popularity, this way intersections are faster.
        let mut derived_words: Vec<_> = derived_words.iter().collect();
        derived_words.sort_unstable_by_key(|(_, docids)| docids.len());

        // we do a union between all the docids of each of the derived words,
        // we got N unions (the number of original query words), we then intersect them.
        let mut candidates = RoaringBitmap::new();

        for (i, (_, union_docids)) in derived_words.iter().enumerate() {
            if i == 0 {
                candidates = union_docids.clone();
            } else {
                candidates.intersect_with(&union_docids);
            }
        }

        candidates
    }

    fn facet_ordered(
        &self,
        field_id: FieldId,
        facet_type: FacetType,
        order: Order,
        documents_ids: RoaringBitmap,
        limit: usize,
    ) -> anyhow::Result<Vec<DocumentId>>
    {
        let mut output = Vec::new();
        match facet_type {
            FacetType::Float => {
                facet_number_recurse::<f64, FacetLevelValueF64Codec, _>(
                    self.rtxn,
                    self.index,
                    field_id,
                    order,
                    documents_ids,
                    |_val, docids| {
                        output.push(docids);
                        // Returns `true` if we must continue iterating
                        output.iter().map(|ids| ids.len()).sum::<u64>() < limit as u64
                    }
                )?;
            },
            FacetType::Integer => {
                facet_number_recurse::<i64, FacetLevelValueI64Codec, _>(
                    self.rtxn,
                    self.index,
                    field_id,
                    order,
                    documents_ids,
                    |_val, docids| {
                        output.push(docids);
                        // Returns `true` if we must continue iterating
                        output.iter().map(|ids| ids.len()).sum::<u64>() < limit as u64
                    }
                )?;
            },
            FacetType::String => bail!("criteria facet type must be a number"),
        }
        Ok(output.into_iter().flatten().take(limit).collect())
    }

    pub fn execute(&self) -> anyhow::Result<SearchResult> {
        let limit = self.limit;
        let fst = self.index.words_fst(self.rtxn)?;

        // Construct the DFAs related to the query words.
        let derived_words = match self.query.as_deref().map(Self::generate_query_dfas) {
            Some(dfas) if !dfas.is_empty() => Some(self.fetch_words_docids(&fst, dfas)?),
            _otherwise => None,
        };

        // We create the original candidates with the facet conditions results.
        let facet_candidates = match &self.facet_condition {
            Some(condition) => Some(condition.evaluate(self.rtxn, self.index)?),
            None => None,
        };

        let order_by_facet = {
            let criteria = self.index.criteria(self.rtxn)?;
            let result = criteria.into_iter().flat_map(|criterion| {
                match criterion {
                    Criterion::Asc(fid) => Some((fid, Order::Asc)),
                    Criterion::Desc(fid) => Some((fid, Order::Desc)),
                    _ => None
                }
            }).next();
            match result {
                Some((fid, order)) => {
                    let faceted_fields = self.index.faceted_fields(self.rtxn)?;
                    let ftype = *faceted_fields.get(&fid).context("unknown field id")?;
                    Some((fid, ftype, order))
                },
                None => None,
            }
        };

        debug!("facet candidates: {:?}", facet_candidates);

        let (candidates, derived_words) = match (facet_candidates, derived_words) {
            (Some(mut facet_candidates), Some(derived_words)) => {
                let words_candidates = Self::compute_candidates(&derived_words);
                facet_candidates.intersect_with(&words_candidates);
                (facet_candidates, derived_words)
            },
            (None, Some(derived_words)) => {
                (Self::compute_candidates(&derived_words), derived_words)
            },
            (Some(facet_candidates), None) => {
                // If the query is not set or results in no DFAs but
                // there is some facet conditions we return a placeholder.
                let documents_ids = match order_by_facet {
                    Some((fid, ftype, order)) => self.facet_ordered(fid, ftype, order, facet_candidates, limit)?,
                    None => facet_candidates.iter().take(limit).collect(),
                };
                return Ok(SearchResult { documents_ids, ..Default::default() })
            },
            (None, None) => {
                // If the query is not set or results in no DFAs we return a placeholder.
                let documents_ids = self.index.documents_ids(self.rtxn)?;
                let documents_ids = match order_by_facet {
                    Some((fid, ftype, order)) => self.facet_ordered(fid, ftype, order, documents_ids, limit)?,
                    None => documents_ids.iter().take(limit).collect(),
                };
                return Ok(SearchResult { documents_ids, ..Default::default() })
            },
        };

        debug!("candidates: {:?}", candidates);

        // The mana depth first search is a revised DFS that explore
        // solutions in the order of their proximities.
        let mut mdfs = Mdfs::new(self.index, self.rtxn, &derived_words, candidates);
        let mut documents = Vec::new();

        // We execute the Mdfs iterator until we find enough documents.
        while documents.iter().map(RoaringBitmap::len).sum::<u64>() < limit as u64 {
            match mdfs.next().transpose()? {
                Some((proximity, answer)) => {
                    debug!("answer with a proximity of {}: {:?}", proximity, answer);
                    documents.push(answer);
                },
                None => break,
            }
        }

        let found_words = derived_words.into_iter().flat_map(|(w, _)| w).map(|(w, _)| w).collect();
        let documents_ids = documents.into_iter().flatten().take(limit).collect();
        Ok(SearchResult { found_words, documents_ids })
    }
}

impl fmt::Debug for Search<'_> {
    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
        let Search { query, facet_condition, offset, limit, rtxn: _, index: _ } = self;
        f.debug_struct("Search")
            .field("query", query)
            .field("facet_condition", facet_condition)
            .field("offset", offset)
            .field("limit", limit)
            .finish()
    }
}

#[derive(Default)]
pub struct SearchResult {
    pub found_words: HashSet<String>,
    // TODO those documents ids should be associated with their criteria scores.
    pub documents_ids: Vec<DocumentId>,
}