//! BM25 scoring algorithm.
//!
//! Implements the BM25 probabilistic ranking function.
//!
//! # BM25 Formula Breakdown
//!
//! ```text
//! BM25(q, d) = Σ IDF(qᵢ) · (f(qᵢ, d) · (k₁ + 1)) / (f(qᵢ, d) + k₁ · (1 - b + b · |d| / avgdl))
//!                ↑           ↑                        ↑
//!           Term weight   Numerator             Denominator
//!                                            (length normalization)
//!
//! IDF(qᵢ) = log((N - n(qᵢ) + 0.5) / (n(qᵢ) + 0.5))
//!
//! Where:
//! - qᵢ: query term i
//! - d: document
//! - f(qᵢ, d): term frequency of qᵢ in d
//! - |d|: document length (number of terms)
//! - avgdl: average document length in collection
//! - N: total number of documents
//! - n(qᵢ): number of documents containing qᵢ
//! - k₁: term frequency saturation parameter (typical: 1.2)
//! - b: length normalization parameter (typical: 0.75)
//! ```
//!
//! # Parameter Tuning
//!
//! | Parameter | Range | Effect |
//! |-----------|-------|--------|
//! | k₁ | 1.2-2.0 | Higher = less saturation (more weight to high TF) |
//! | b | 0.0-1.0 | 0 = no length norm, 1 = full length norm |
//!
//! **Recommended defaults:**
//! - k₁ = 1.2 (works well for most text)
//! - b = 0.75 (balanced length normalization)

use crate::index::bm25::inverted_index::{InvertedIndex, PostingList};
use crate::types::error::Result;

/// BM25 scoring parameters.
#[derive(Debug, Clone, Copy)]
pub struct BM25Params {
    /// Term frequency saturation parameter (k₁).
    ///
    /// Controls how quickly term frequency saturates.
    /// - Higher (2.0): More weight to repeated terms
    /// - Lower (1.0): Faster saturation
    /// - Default: 1.2
    pub k1: f64,

    /// Length normalization parameter (b).
    ///
    /// Controls how much document length affects scoring.
    /// - 0.0: No normalization (short/long docs treated equally)
    /// - 1.0: Full normalization (penalize long docs heavily)
    /// - Default: 0.75
    pub b: f64,
}

impl Default for BM25Params {
    fn default() -> Self {
        Self { k1: 1.2, b: 0.75 }
    }
}

/// BM25 scorer with precomputed statistics.
#[derive(Debug, Clone)]
pub struct BM25Scorer {
    /// BM25 parameters
    params: BM25Params,

    /// Total number of documents
    num_docs: u32,

    /// Average document length
    avg_doc_length: f64,
}

impl BM25Scorer {
    /// Create a new BM25 scorer.
    ///
    /// # Arguments
    ///
    /// * `params` - BM25 parameters (k₁, b)
    /// * `num_docs` - Total number of documents in collection
    /// * `avg_doc_length` - Average document length (in tokens)
    ///
    /// # Example
    ///
    /// ```rust,ignore
    /// let scorer = BM25Scorer::new(
    ///     BM25Params::default(),
    ///     1000,  // 1000 documents
    ///     50.0,  // avg 50 tokens per document
    /// );
    /// ```
    pub fn new(params: BM25Params, num_docs: u32, avg_doc_length: f64) -> Self {
        Self {
            params,
            num_docs,
            avg_doc_length,
        }
    }

    /// Compute IDF (Inverse Document Frequency) for a term.
    ///
    /// Uses the Robertson-Sparck Jones IDF formulation:
    /// `IDF(t) = log((N - df(t) + 0.5) / (df(t) + 0.5))`
    ///
    /// # Arguments
    ///
    /// * `doc_freq` - Number of documents containing the term
    ///
    /// # Returns
    ///
    /// IDF score (can be negative for very common terms)
    ///
    /// # Example
    ///
    /// ```rust,ignore
    /// let idf = scorer.idf(10);  // Term appears in 10 of 1000 docs
    /// assert!(idf > 0.0);
    /// ```
    pub fn idf(&self, doc_freq: u32) -> f64 {
        let n = self.num_docs as f64;
        let df = doc_freq as f64;
        ((n - df + 0.5) / (df + 0.5)).ln()
    }

    /// Compute BM25 term score.
    ///
    /// Scores a single term's contribution to document relevance.
    ///
    /// # Arguments
    ///
    /// * `term_freq` - Term frequency in document
    /// * `doc_length` - Document length (number of tokens)
    /// * `doc_freq` - Document frequency (number of docs containing term)
    ///
    /// # Returns
    ///
    /// BM25 score for this term
    fn term_score(&self, term_freq: u32, doc_length: usize, doc_freq: u32) -> f64 {
        let tf = term_freq as f64;
        let dl = doc_length as f64;
        let avgdl = self.avg_doc_length;

        let idf = self.idf(doc_freq);
        let numerator = tf * (self.params.k1 + 1.0);
        let denominator = tf + self.params.k1 * (1.0 - self.params.b + self.params.b * dl / avgdl);

        idf * (numerator / denominator)
    }

    /// Score a document for a query.
    ///
    /// Sums BM25 scores for all query terms found in the document.
    ///
    /// # Arguments
    ///
    /// * `query_terms` - Query terms (with counts if term appears multiple times)
    /// * `doc_id` - Document ID to score
    /// * `doc_length` - Document length (number of tokens)
    /// * `inverted_index` - Inverted index for term lookups
    ///
    /// # Returns
    ///
    /// BM25 score (higher = more relevant)
    ///
    /// # Example
    ///
    /// ```rust,ignore
    /// let query_terms = vec!["rust".to_string(), "programming".to_string()];
    /// let score = scorer.score(&query_terms, doc_id, doc_length, &index);
    /// ```
    pub fn score(
        &self,
        query_terms: &[String],
        doc_id: u32,
        doc_length: usize,
        inverted_index: &InvertedIndex,
    ) -> f64 {
        todo!("Sum term scores for all query terms")
    }

    /// Score all documents for a query.
    ///
    /// Returns scores for all documents containing at least one query term.
    ///
    /// # Arguments
    ///
    /// * `query_terms` - Query terms
    /// * `doc_lengths` - Document lengths (indexed by doc_id)
    /// * `inverted_index` - Inverted index
    ///
    /// # Returns
    ///
    /// HashMap of (doc_id -> score) for matching documents
    pub fn score_all(
        &self,
        query_terms: &[String],
        doc_lengths: &[usize],
        inverted_index: &InvertedIndex,
    ) -> std::collections::HashMap<u32, f64> {
        todo!("Score all documents, return only non-zero scores")
    }
}

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

    #[test]
    fn test_idf_calculation() {
        todo!("Test IDF for common and rare terms")
    }

    #[test]
    fn test_term_score() {
        todo!("Test BM25 term score calculation")
    }

    #[test]
    fn test_score_single_term() {
        todo!("Test scoring document with one query term")
    }

    #[test]
    fn test_score_multiple_terms() {
        todo!("Test scoring with multiple query terms")
    }

    #[test]
    fn test_length_normalization() {
        todo!("Test that longer docs are penalized when b > 0")
    }

    #[test]
    fn test_term_saturation() {
        todo!("Test that repeated terms saturate (not linear)")
    }

    #[test]
    fn test_score_all() {
        todo!("Test scoring all documents")
    }
}
