//! Ground truth-based PDF table detection and markdown quality tests.
//!
//! These tests establish baselines for table detection and markdown output quality.
//! Run after each substantial change to measure improvement or regression.
//!
//! Usage:
//!   # Non-OCR tests (fast, oxide path):
//!   cargo test -p kreuzberg --features "pdf" --test pdf_table_ground_truth -- --nocapture
//!
//!   # Full tests including table detection (needs ocr feature for HocrWord):
//!   cargo test -p kreuzberg --features "pdf,ocr" --test pdf_table_ground_truth -- --nocapture
//!
//!   # Comprehensive baseline snapshot:
//!   cargo test -p kreuzberg --features "pdf,ocr" --test pdf_table_ground_truth -- --ignored --nocapture

#![cfg(feature = "pdf")]

mod helpers;

use helpers::*;
use kreuzberg::core::config::{ExtractionConfig, OutputFormat};
use kreuzberg::extract_file_sync;

/// Compute word-level Jaccard similarity between two strings.
fn word_similarity(a: &str, b: &str) -> f64 {
    let words_a: std::collections::HashSet<&str> = a.split_whitespace().collect();
    let words_b: std::collections::HashSet<&str> = b.split_whitespace().collect();

    if words_a.is_empty() && words_b.is_empty() {
        return 1.0;
    }
    if words_a.is_empty() || words_b.is_empty() {
        return 0.0;
    }

    let intersection = words_a.intersection(&words_b).count();
    let union = words_a.union(&words_b).count();

    intersection as f64 / union as f64
}

/// Extract markdown from a PDF file (oxide path, no OCR).
fn extract_markdown(relative_path: &str) -> Option<kreuzberg::types::ExtractionResult> {
    let path = get_test_file_path(relative_path);
    if !path.exists() {
        return None;
    }

    let config = ExtractionConfig {
        output_format: OutputFormat::Markdown,
        ..Default::default()
    };

    extract_file_sync(&path, None, &config).ok()
}

fn print_table_summary(result: &kreuzberg::types::ExtractionResult) {
    println!("  Tables detected: {}", result.tables.len());
    println!("  Content length: {} chars", result.content.len());
    for (i, table) in result.tables.iter().enumerate() {
        let rows = table.cells.len();
        let cols = if rows > 0 { table.cells[0].len() } else { 0 };
        println!("  Table {}: {}x{} (page {})", i + 1, rows, cols, table.page_number);
        if let Some(first_row) = table.cells.first() {
            let preview: Vec<String> = first_row
                .iter()
                .take(3)
                .map(|c| {
                    let s = c.trim();
                    if s.len() > 40 {
                        format!("{}...", &s[..s.floor_char_boundary(40)])
                    } else {
                        s.to_string()
                    }
                })
                .collect();
            println!("    First row: {:?}", preview);
        }
    }
}

// ═══════════════════════════════════════════════════════════════════
// Section 1: False Positive Regression Tests
// Non-table PDFs must NOT have tables detected.
// These are the hard gate — they must pass for a commit.
// ═══════════════════════════════════════════════════════════════════

/// Helper to run a false-positive check for a non-table PDF.
/// Only checks when the ocr feature is enabled (table detection requires it).
#[cfg(feature = "ocr")]
fn assert_no_tables(pdf_name: &str) {
    let rel = format!("pdf/{}", pdf_name);
    if skip_if_missing(&rel) {
        return;
    }

    let result = extract_markdown(&rel).expect("extraction should succeed");

    println!("=== {} false positive check ===", pdf_name);
    print_table_summary(&result);

    assert!(
        result.tables.is_empty(),
        "{} should not have tables detected (got {})",
        pdf_name,
        result.tables.len()
    );
}

#[cfg(feature = "ocr")]
#[ignore = "TODO: pdf_oxide upstream — table detector false-positive driven by pdf_oxide span geometry; https://github.com/yfedoseev/pdf_oxide/issues/484"]
#[test]
fn test_false_positive_simple_pdf() {
    assert_no_tables("simple.pdf");
}

#[cfg(feature = "ocr")]
#[test]
fn test_false_positive_fake_memo() {
    assert_no_tables("fake_memo.pdf");
}

#[cfg(feature = "ocr")]
#[test]
fn test_false_positive_searchable() {
    assert_no_tables("searchable.pdf");
}

#[cfg(feature = "ocr")]
#[ignore = "TODO: pdf_oxide upstream — table detector false-positive driven by pdf_oxide span geometry; https://github.com/yfedoseev/pdf_oxide/issues/484"]
#[test]
fn test_false_positive_google_doc() {
    assert_no_tables("google_doc_document.pdf");
}

// ═══════════════════════════════════════════════════════════════════
// Section 2: Markdown Quality Tests (oxide path, no OCR needed)
// Tests that text-bearing PDFs produce reasonable markdown.
// ═══════════════════════════════════════════════════════════════════

#[test]
fn test_markdown_quality_fake_memo() {
    if skip_if_missing("pdf/fake_memo.pdf") {
        return;
    }

    let result = extract_markdown("pdf/fake_memo.pdf").expect("extraction should succeed");

    println!("=== fake_memo.pdf markdown quality ===");
    println!("Content length: {} chars", result.content.len());

    assert!(
        result.content.len() > 100,
        "fake_memo.pdf should produce >100 chars of markdown (got {})",
        result.content.len()
    );
}

#[test]
fn test_markdown_quality_simple() {
    if skip_if_missing("pdf/simple.pdf") {
        return;
    }

    let result = extract_markdown("pdf/simple.pdf").expect("extraction should succeed");

    println!("=== simple.pdf markdown quality ===");
    println!("Content length: {} chars", result.content.len());

    assert!(
        result.content.len() > 1000,
        "simple.pdf should produce >1000 chars of markdown (got {})",
        result.content.len()
    );
}

#[test]
fn test_markdown_quality_multi_page() {
    if skip_if_missing("pdf/multi_page.pdf") {
        return;
    }

    let result = extract_markdown("pdf/multi_page.pdf").expect("extraction should succeed");

    println!("=== multi_page.pdf markdown quality ===");
    println!("Content length: {} chars", result.content.len());

    // multi_page.pdf is text-based and should produce substantial markdown
    assert!(
        result.content.len() > 5000,
        "multi_page.pdf should produce >5000 chars (got {})",
        result.content.len()
    );
}

#[test]
fn test_markdown_quality_vs_ground_truth_simple() {
    if skip_if_missing("pdf/table_document.pdf") {
        return;
    }

    let gt_path = get_test_file_path("ground_truth/pdf/pdf_tables.txt");
    if !gt_path.exists() {
        println!("Skipping: ground truth file not found");
        return;
    }

    let ground_truth = std::fs::read_to_string(&gt_path).expect("should read ground truth");
    let result = extract_markdown("pdf/table_document.pdf").expect("extraction should succeed");

    let similarity = word_similarity(&result.content, &ground_truth);

    println!("=== table_document.pdf vs ground truth ===");
    println!("Extraction length: {} chars", result.content.len());
    println!("Ground truth length: {} chars", ground_truth.len());
    println!("Word similarity: {:.1}%", similarity * 100.0);

    // table_document.pdf is image-only, so the PDF extractor finds almost no text.
    // This test tracks progress — similarity should increase with OCR improvements.
    // Currently: ~3% (only image placeholder matches a few words).
    println!("NOTE: table_document.pdf is image-only; low similarity expected without OCR.");
}

// ═══════════════════════════════════════════════════════════════════
// Section 3: OCR Path Table Detection (slow, run with --ignored)
// ═══════════════════════════════════════════════════════════════════

#[cfg(feature = "ocr")]
#[test]
#[ignore] // Slow OCR tests, run explicitly
fn test_ocr_path_table_document() {
    use kreuzberg::core::config::OcrConfig;

    if skip_if_missing("pdf/table_document.pdf") {
        return;
    }

    let path = get_test_file_path("pdf/table_document.pdf");
    let config = ExtractionConfig {
        output_format: OutputFormat::Markdown,
        ocr: Some(OcrConfig {
            backend: "tesseract".to_string(),
            language: "eng".to_string(),
            ..Default::default()
        }),
        force_ocr: true, // Force OCR since this is image-only
        ..Default::default()
    };

    let result = extract_file_sync(&path, None, &config).expect("extraction should succeed");

    println!("=== table_document.pdf (forced OCR path) ===");
    print_table_summary(&result);
    println!("\n--- Content (first 2000 chars) ---");
    println!("{}", &result.content[..result.content.len().min(2000)]);

    assert!(
        result.content.len() > 100,
        "table_document.pdf OCR path should produce substantial content (got {})",
        result.content.len()
    );
}

// ═══════════════════════════════════════════════════════════════════
// Section 4: Comprehensive Baseline Snapshot
// Full scan of all PDFs — run with --ignored for complete picture.
// ═══════════════════════════════════════════════════════════════════

#[test]
#[ignore]
fn test_comprehensive_table_detection_baseline() {
    if !test_documents_available() {
        println!("Skipping: test_documents not available");
        return;
    }

    let image_table_pdfs = [
        "table_document.pdf",
        "multi_page_tables.pdf",
        "embedded_images_tables.pdf",
    ];

    let text_table_pdfs = [
        "multi_page.pdf",
        "medium.pdf",
        "large.pdf",
        "a_comparison_of_programming_languages_in_economics_16_jun_2014.pdf",
        "tiny.pdf",
        "tatr.pdf",
    ];

    let non_table_pdfs = [
        "simple.pdf",
        "fake_memo.pdf",
        "google_doc_document.pdf",
        "searchable.pdf",
        "test_article.pdf",
        "code_and_formula.pdf",
    ];

    println!("\n╔══════════════════════════════════════════════════╗");
    println!("║     Table Detection Baseline Snapshot             ║");
    println!("╚══════════════════════════════════════════════════╝\n");

    println!("--- Image-Only Table PDFs (need OCR) ---");
    for pdf in &image_table_pdfs {
        let rel = format!("pdf/{}", pdf);
        if skip_if_missing(&rel) {
            continue;
        }
        match extract_markdown(&rel) {
            Some(result) => {
                let table_count = result.tables.len();
                let status = if result.content.len() < 50 { "IMG" } else { "OK" };
                println!(
                    "  [{:4}] {:<55} tables={} md_len={}",
                    status,
                    pdf,
                    table_count,
                    result.content.len()
                );
            }
            None => println!("  [ERR ] {}", pdf),
        }
    }

    println!("\n--- Text-Based PDFs Expected to Have Tables ---");
    let mut true_positives = 0;
    let mut false_negatives = 0;
    for pdf in &text_table_pdfs {
        let rel = format!("pdf/{}", pdf);
        if skip_if_missing(&rel) {
            continue;
        }
        match extract_markdown(&rel) {
            Some(result) => {
                let table_count = result.tables.len();
                let status = if table_count > 0 {
                    true_positives += 1;
                    "OK"
                } else {
                    false_negatives += 1;
                    "MISS"
                };
                println!(
                    "  [{:4}] {:<55} tables={} md_len={}",
                    status,
                    pdf,
                    table_count,
                    result.content.len()
                );
            }
            None => println!("  [ERR ] {}", pdf),
        }
    }

    println!("\n--- Expected Non-Table PDFs ---");
    let mut true_negatives = 0;
    let mut false_positives = 0;
    for pdf in &non_table_pdfs {
        let rel = format!("pdf/{}", pdf);
        if skip_if_missing(&rel) {
            continue;
        }
        match extract_markdown(&rel) {
            Some(result) => {
                let table_count = result.tables.len();
                let status = if table_count == 0 {
                    true_negatives += 1;
                    "OK"
                } else {
                    false_positives += 1;
                    "FP"
                };
                println!(
                    "  [{:4}] {:<55} tables={} md_len={}",
                    status,
                    pdf,
                    table_count,
                    result.content.len()
                );
            }
            None => println!("  [ERR ] {}", pdf),
        }
    }

    println!("\n--- Summary ---");
    println!("True positives:  {}", true_positives);
    println!("False negatives: {}", false_negatives);
    println!("True negatives:  {}", true_negatives);
    println!("False positives: {}", false_positives);

    let precision = if true_positives + false_positives > 0 {
        true_positives as f64 / (true_positives + false_positives) as f64
    } else {
        0.0
    };
    let recall = if true_positives + false_negatives > 0 {
        true_positives as f64 / (true_positives + false_negatives) as f64
    } else {
        0.0
    };

    println!("Precision: {:.1}%", precision * 100.0);
    println!("Recall:    {:.1}%", recall * 100.0);
}