rrag 0.1.0-alpha.2

//! # Layout Analysis
//!
//! Advanced document layout analysis and structure detection.

use super::{
    ColumnLayout, DocumentLayout, DocumentSection, DocumentType, LayoutAnalysisConfig,
    LayoutAnalyzer, SectionType,
};
use crate::{RragError, RragResult};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::path::Path;

/// Default layout analyzer implementation
pub struct DefaultLayoutAnalyzer {
    /// Configuration
    config: LayoutAnalysisConfig,

    /// Structure detector
    structure_detector: StructureDetector,

    /// Section identifier
    section_identifier: SectionIdentifier,

    /// Reading order analyzer
    reading_order_analyzer: ReadingOrderAnalyzer,

    /// Column detector
    column_detector: ColumnDetector,

    /// Page analyzer
    page_analyzer: PageAnalyzer,
}

/// Document structure detection
pub struct StructureDetector {
    /// Hierarchy patterns
    hierarchy_patterns: Vec<HierarchyPattern>,

    /// Document type classifiers
    type_classifiers: HashMap<DocumentType, TypeClassifier>,

    /// Layout rules
    layout_rules: Vec<LayoutRule>,
}

/// Section identification component
pub struct SectionIdentifier {
    /// Section patterns by document type
    section_patterns: HashMap<DocumentType, Vec<SectionPattern>>,

    /// Header detection rules
    header_rules: Vec<HeaderRule>,

    /// Content classification
    content_classifier: ContentClassifier,
}

/// Reading order analysis
pub struct ReadingOrderAnalyzer {
    /// Layout strategies
    strategies: HashMap<LayoutType, ReadingStrategy>,

    /// Flow detection
    flow_detector: FlowDetector,

    /// Region analyzer
    region_analyzer: RegionAnalyzer,
}

/// Column detection component
pub struct ColumnDetector {
    /// Column detection algorithms
    algorithms: Vec<ColumnDetectionAlgorithm>,

    /// Layout classifier
    layout_classifier: LayoutClassifier,

    /// Spacing analyzer
    spacing_analyzer: SpacingAnalyzer,
}

/// Page analysis component
pub struct PageAnalyzer {
    /// Page classifiers
    classifiers: HashMap<DocumentType, PageClassifier>,

    /// Content distribution analyzer
    distribution_analyzer: ContentDistributionAnalyzer,

    /// Margin detector
    margin_detector: MarginDetector,
}

/// Layout analysis result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LayoutAnalysisResult {
    /// Detected layout
    pub layout: DocumentLayout,

    /// Analysis confidence
    pub confidence: f32,

    /// Processing time
    pub processing_time_ms: u64,

    /// Layout metrics
    pub metrics: LayoutMetrics,

    /// Detected features
    pub features: LayoutFeatures,

    /// Analysis warnings
    pub warnings: Vec<String>,
}

/// Layout metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LayoutMetrics {
    /// Text density
    pub text_density: f32,

    /// White space ratio
    pub white_space_ratio: f32,

    /// Column balance
    pub column_balance: f32,

    /// Reading flow score
    pub reading_flow_score: f32,

    /// Section organization score
    pub organization_score: f32,
}

/// Detected layout features
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LayoutFeatures {
    /// Has headers/footers
    pub has_headers_footers: bool,

    /// Has multiple columns
    pub has_columns: bool,

    /// Has nested sections
    pub has_nested_sections: bool,

    /// Has consistent formatting
    pub consistent_formatting: bool,

    /// Text-heavy vs visual-heavy
    pub content_balance: ContentBalance,

    /// Layout complexity
    pub complexity_level: ComplexityLevel,
}

/// Hierarchy detection patterns
#[derive(Debug, Clone)]
pub struct HierarchyPattern {
    /// Pattern identifier
    pub id: String,

    /// Pattern regex
    pub pattern: String,

    /// Hierarchy level
    pub level: usize,

    /// Pattern weight
    pub weight: f32,

    /// Document types where applicable
    pub applicable_types: Vec<DocumentType>,
}

/// Document type-specific classifiers
pub struct TypeClassifier {
    /// Classification rules
    rules: Vec<ClassificationRule>,

    /// Feature extractors
    feature_extractors: Vec<FeatureExtractor>,

    /// Confidence threshold
    confidence_threshold: f32,
}

/// Layout rules for structure detection
#[derive(Debug, Clone)]
pub struct LayoutRule {
    /// Rule name
    pub name: String,

    /// Rule condition
    pub condition: RuleCondition,

    /// Rule action
    pub action: RuleAction,

    /// Rule priority
    pub priority: u32,
}

/// Section detection patterns
#[derive(Debug, Clone)]
pub struct SectionPattern {
    /// Section type
    pub section_type: SectionType,

    /// Detection patterns
    pub patterns: Vec<String>,

    /// Context requirements
    pub context_requirements: Vec<ContextRequirement>,

    /// Confidence score
    pub confidence: f32,
}

/// Header detection rules
#[derive(Debug, Clone)]
pub struct HeaderRule {
    /// Rule type
    pub rule_type: HeaderRuleType,

    /// Pattern or criteria
    pub criteria: String,

    /// Minimum confidence
    pub min_confidence: f32,
}

/// Content classification component
pub struct ContentClassifier {
    /// Classification models
    models: HashMap<String, ClassificationModel>,

    /// Feature vectors
    feature_extractors: Vec<TextFeatureExtractor>,
}

/// Layout types for reading order
#[derive(Debug, Clone, Copy, Hash, PartialEq, Eq)]
pub enum LayoutType {
    SingleColumn,
    MultiColumn,
    Magazine,
    Newspaper,
    Academic,
    Technical,
    Web,
}

/// Reading strategies
pub struct ReadingStrategy {
    /// Strategy name
    name: String,

    /// Flow patterns
    flow_patterns: Vec<FlowPattern>,

    /// Priority rules
    priority_rules: Vec<PriorityRule>,
}

/// Flow detection component
pub struct FlowDetector {
    /// Detection algorithms
    algorithms: Vec<FlowDetectionAlgorithm>,

    /// Pattern matchers
    pattern_matchers: Vec<FlowPatternMatcher>,
}

/// Region analysis component
pub struct RegionAnalyzer {
    /// Region classifiers
    classifiers: Vec<RegionClassifier>,

    /// Relationship detectors
    relationship_detectors: Vec<RelationshipDetector>,
}

/// Column detection algorithms
#[derive(Debug, Clone)]
pub struct ColumnDetectionAlgorithm {
    /// Algorithm name
    pub name: String,

    /// Algorithm type
    pub algorithm_type: ColumnAlgorithmType,

    /// Parameters
    pub parameters: HashMap<String, f32>,
}

/// Layout classification component
pub struct LayoutClassifier {
    /// Classification features
    features: Vec<LayoutFeature>,

    /// Decision trees
    decision_trees: Vec<DecisionTree>,
}

/// Spacing analysis component
pub struct SpacingAnalyzer {
    /// Spacing metrics
    metrics: Vec<SpacingMetric>,

    /// Threshold calculator
    threshold_calculator: ThresholdCalculator,
}

/// Page classifiers by document type
pub struct PageClassifier {
    /// Page type patterns
    patterns: Vec<PagePattern>,

    /// Feature weights
    feature_weights: HashMap<String, f32>,
}

/// Content distribution analysis
pub struct ContentDistributionAnalyzer {
    /// Distribution metrics
    metrics: Vec<DistributionMetric>,

    /// Balance calculators
    balance_calculators: Vec<BalanceCalculator>,
}

/// Margin detection component
pub struct MarginDetector {
    /// Detection methods
    methods: Vec<MarginDetectionMethod>,

    /// Consistency checker
    consistency_checker: ConsistencyChecker,
}

impl DefaultLayoutAnalyzer {
    /// Create new layout analyzer
    pub fn new(config: LayoutAnalysisConfig) -> RragResult<Self> {
        let structure_detector = StructureDetector::new()?;
        let section_identifier = SectionIdentifier::new()?;
        let reading_order_analyzer = ReadingOrderAnalyzer::new()?;
        let column_detector = ColumnDetector::new()?;
        let page_analyzer = PageAnalyzer::new()?;

        Ok(Self {
            config,
            structure_detector,
            section_identifier,
            reading_order_analyzer,
            column_detector,
            page_analyzer,
        })
    }

    /// Perform comprehensive layout analysis
    pub async fn analyze_layout_comprehensive(
        &self,
        document_path: &Path,
    ) -> RragResult<LayoutAnalysisResult> {
        let start_time = std::time::Instant::now();

        // Extract content and metadata
        let content = self.extract_document_content(document_path).await?;

        // Detect document structure
        let structure = if self.config.detect_structure {
            self.structure_detector.detect_structure(&content).await?
        } else {
            DocumentStructure::default()
        };

        // Identify sections
        let sections = if self.config.identify_sections {
            self.section_identifier
                .identify_sections(&content, &structure)
                .await?
        } else {
            vec![]
        };

        // Analyze reading order
        let reading_order = if self.config.extract_reading_order {
            self.reading_order_analyzer
                .analyze_reading_order(&content, &sections)
                .await?
        } else {
            (0..sections.len()).map(|i| i.to_string()).collect()
        };

        // Detect columns
        let columns = if self.config.detect_columns {
            self.column_detector.detect_columns(&content).await?
        } else {
            None
        };

        // Analyze pages
        let page_analysis = self.page_analyzer.analyze_pages(&content).await?;

        // Create document layout
        let layout = DocumentLayout {
            pages: page_analysis.page_count,
            sections,
            reading_order,
            columns,
            document_type: content.document_type,
        };

        // Calculate metrics
        let metrics = self.calculate_layout_metrics(&content, &layout)?;

        // Extract features
        let features = self.extract_layout_features(&content, &layout)?;

        // Calculate confidence
        let confidence = self.calculate_analysis_confidence(&structure, &metrics, &features)?;

        let processing_time = start_time.elapsed().as_millis() as u64;

        Ok(LayoutAnalysisResult {
            layout,
            confidence,
            processing_time_ms: processing_time,
            metrics,
            features,
            warnings: vec![],
        })
    }

    /// Extract document content for analysis
    async fn extract_document_content(&self, document_path: &Path) -> RragResult<DocumentContent> {
        // Detect document type
        let doc_type = self.detect_document_type(document_path)?;

        // Extract content based on type
        match doc_type {
            DocumentType::PDF => self.extract_pdf_content(document_path).await,
            DocumentType::Word => self.extract_word_content(document_path).await,
            DocumentType::HTML => self.extract_html_content(document_path).await,
            DocumentType::Markdown => self.extract_markdown_content(document_path).await,
            DocumentType::PlainText => self.extract_text_content(document_path).await,
            _ => self.extract_generic_content(document_path).await,
        }
    }

    /// Calculate layout metrics
    fn calculate_layout_metrics(
        &self,
        content: &DocumentContent,
        layout: &DocumentLayout,
    ) -> RragResult<LayoutMetrics> {
        let total_chars = content.text.len() as f32;
        let total_area = content.page_width * content.page_height;

        // Text density
        let text_density = total_chars / total_area;

        // White space ratio (estimated)
        let text_area = total_chars * 0.01; // Rough estimate
        let white_space_ratio = 1.0 - (text_area / total_area).min(1.0);

        // Column balance
        let column_balance = if let Some(ref columns) = layout.columns {
            self.calculate_column_balance(columns, &layout.sections)?
        } else {
            1.0
        };

        // Reading flow score
        let reading_flow_score =
            self.calculate_reading_flow_score(&layout.reading_order, &layout.sections)?;

        // Organization score
        let organization_score = self.calculate_organization_score(&layout.sections)?;

        Ok(LayoutMetrics {
            text_density,
            white_space_ratio,
            column_balance,
            reading_flow_score,
            organization_score,
        })
    }

    /// Extract layout features
    fn extract_layout_features(
        &self,
        content: &DocumentContent,
        layout: &DocumentLayout,
    ) -> RragResult<LayoutFeatures> {
        let has_headers_footers = content.has_headers || content.has_footers;
        let has_columns = layout.columns.is_some();
        let has_nested_sections = self.has_nested_sections(&layout.sections);
        let consistent_formatting = self.check_formatting_consistency(content)?;
        let content_balance = self.analyze_content_balance(content)?;
        let complexity_level = self.assess_complexity_level(layout, content)?;

        Ok(LayoutFeatures {
            has_headers_footers,
            has_columns,
            has_nested_sections,
            consistent_formatting,
            content_balance,
            complexity_level,
        })
    }

    /// Calculate analysis confidence
    fn calculate_analysis_confidence(
        &self,
        structure: &DocumentStructure,
        metrics: &LayoutMetrics,
        features: &LayoutFeatures,
    ) -> RragResult<f32> {
        let mut confidence = 0.8; // Base confidence

        // Adjust based on structure detection confidence
        confidence *= structure.detection_confidence;

        // Adjust based on metrics quality
        if metrics.organization_score > 0.8 {
            confidence += 0.1;
        }
        if metrics.reading_flow_score > 0.8 {
            confidence += 0.05;
        }

        // Adjust based on feature consistency
        if features.consistent_formatting {
            confidence += 0.05;
        }

        Ok(confidence.min(1.0))
    }

    /// Helper methods for specific document types
    async fn extract_pdf_content(&self, _path: &Path) -> RragResult<DocumentContent> {
        // Simplified PDF content extraction
        Ok(DocumentContent {
            text: "PDF content".to_string(),
            document_type: DocumentType::PDF,
            page_count: 3,
            page_width: 8.5,
            page_height: 11.0,
            has_headers: true,
            has_footers: true,
            formatting_info: FormattingInfo::default(),
        })
    }

    async fn extract_word_content(&self, _path: &Path) -> RragResult<DocumentContent> {
        Ok(DocumentContent {
            text: "Word document content".to_string(),
            document_type: DocumentType::Word,
            page_count: 2,
            page_width: 8.5,
            page_height: 11.0,
            has_headers: false,
            has_footers: false,
            formatting_info: FormattingInfo::default(),
        })
    }

    async fn extract_html_content(&self, path: &Path) -> RragResult<DocumentContent> {
        let html_content =
            std::fs::read_to_string(path).map_err(|e| RragError::io_error(e.to_string()))?;

        Ok(DocumentContent {
            text: html_content,
            document_type: DocumentType::HTML,
            page_count: 1,
            page_width: 12.0,
            page_height: 16.0,
            has_headers: false,
            has_footers: false,
            formatting_info: FormattingInfo::default(),
        })
    }

    async fn extract_markdown_content(&self, path: &Path) -> RragResult<DocumentContent> {
        let md_content =
            std::fs::read_to_string(path).map_err(|e| RragError::io_error(e.to_string()))?;

        Ok(DocumentContent {
            text: md_content,
            document_type: DocumentType::Markdown,
            page_count: 1,
            page_width: 10.0,
            page_height: 12.0,
            has_headers: false,
            has_footers: false,
            formatting_info: FormattingInfo::default(),
        })
    }

    async fn extract_text_content(&self, path: &Path) -> RragResult<DocumentContent> {
        let text_content =
            std::fs::read_to_string(path).map_err(|e| RragError::io_error(e.to_string()))?;

        Ok(DocumentContent {
            text: text_content,
            document_type: DocumentType::PlainText,
            page_count: 1,
            page_width: 8.0,
            page_height: 10.0,
            has_headers: false,
            has_footers: false,
            formatting_info: FormattingInfo::default(),
        })
    }

    async fn extract_generic_content(&self, path: &Path) -> RragResult<DocumentContent> {
        self.extract_text_content(path).await
    }

    /// Helper methods for analysis
    fn detect_document_type(&self, file_path: &Path) -> RragResult<DocumentType> {
        let extension = file_path
            .extension()
            .and_then(|ext| ext.to_str())
            .unwrap_or("")
            .to_lowercase();

        match extension.as_str() {
            "pdf" => Ok(DocumentType::PDF),
            "doc" | "docx" => Ok(DocumentType::Word),
            "ppt" | "pptx" => Ok(DocumentType::PowerPoint),
            "html" | "htm" => Ok(DocumentType::HTML),
            "md" => Ok(DocumentType::Markdown),
            "txt" => Ok(DocumentType::PlainText),
            _ => Ok(DocumentType::Mixed),
        }
    }

    fn calculate_column_balance(
        &self,
        columns: &ColumnLayout,
        sections: &[DocumentSection],
    ) -> RragResult<f32> {
        if columns.column_count <= 1 {
            return Ok(1.0);
        }

        // Calculate content distribution across columns
        let mut column_content_lengths = vec![0; columns.column_count];

        for section in sections {
            // Simplified: assume equal distribution
            let content_per_column = section.content.len() / columns.column_count;
            for i in 0..columns.column_count {
                column_content_lengths[i] += content_per_column;
            }
        }

        // Calculate balance as inverse of variance
        let total_content: usize = column_content_lengths.iter().sum();
        let mean_content = total_content as f32 / columns.column_count as f32;

        let variance = column_content_lengths
            .iter()
            .map(|&len| (len as f32 - mean_content).powi(2))
            .sum::<f32>()
            / columns.column_count as f32;

        let balance = 1.0 / (1.0 + variance / (mean_content * mean_content));
        Ok(balance)
    }

    fn calculate_reading_flow_score(
        &self,
        reading_order: &[String],
        sections: &[DocumentSection],
    ) -> RragResult<f32> {
        if reading_order.len() != sections.len() {
            return Ok(0.5); // Partial score for mismatched orders
        }

        // Check for logical section progression
        let mut flow_score: f32 = 1.0;
        let mut has_title = false;
        let mut _has_abstract = false;
        let mut has_intro = false;
        let mut has_conclusion = false;

        for section_id in reading_order {
            if let Some(section) = sections.iter().find(|s| s.id == *section_id) {
                match section.section_type {
                    SectionType::Title => has_title = true,
                    SectionType::Abstract => {
                        if !has_title {
                            flow_score -= 0.1; // Abstract should come after title
                        }
                        _has_abstract = true;
                    }
                    SectionType::Introduction => {
                        if has_conclusion {
                            flow_score -= 0.2; // Introduction after conclusion is unusual
                        }
                        has_intro = true;
                    }
                    SectionType::Conclusion => has_conclusion = true,
                    _ => {}
                }
            }
        }

        // Bonus for having expected sections
        if has_title {
            flow_score += 0.1;
        }
        if has_intro {
            flow_score += 0.1;
        }
        if has_conclusion {
            flow_score += 0.1;
        }

        Ok(flow_score.max(0.0).min(1.0))
    }

    fn calculate_organization_score(&self, sections: &[DocumentSection]) -> RragResult<f32> {
        if sections.is_empty() {
            return Ok(0.0);
        }

        let mut score = 0.8; // Base score

        // Check for hierarchical organization
        let has_hierarchy = sections.iter().any(|s| s.level > 1);
        if has_hierarchy {
            score += 0.1;
        }

        // Check for section type diversity
        let section_types: std::collections::HashSet<SectionType> =
            sections.iter().map(|s| s.section_type).collect();

        let type_diversity = section_types.len() as f32 / 6.0; // Assuming 6 possible types
        score += type_diversity * 0.1;

        Ok(score.min(1.0))
    }

    fn has_nested_sections(&self, sections: &[DocumentSection]) -> bool {
        sections.iter().any(|s| s.level > 1)
    }

    fn check_formatting_consistency(&self, content: &DocumentContent) -> RragResult<bool> {
        // Simplified consistency check
        Ok(content.formatting_info.has_consistent_fonts
            && content.formatting_info.has_consistent_spacing)
    }

    fn analyze_content_balance(&self, content: &DocumentContent) -> RragResult<ContentBalance> {
        let text_length = content.text.len();

        // Simple heuristic based on text length
        if text_length > 10000 {
            Ok(ContentBalance::TextHeavy)
        } else if text_length < 1000 {
            Ok(ContentBalance::VisualHeavy)
        } else {
            Ok(ContentBalance::Balanced)
        }
    }

    fn assess_complexity_level(
        &self,
        layout: &DocumentLayout,
        content: &DocumentContent,
    ) -> RragResult<ComplexityLevel> {
        let mut complexity_score = 0;

        // Section count contributes to complexity
        complexity_score += layout.sections.len();

        // Column layout adds complexity
        if let Some(ref columns) = layout.columns {
            complexity_score += columns.column_count * 2;
        }

        // Nested sections add complexity
        let max_level = layout.sections.iter().map(|s| s.level).max().unwrap_or(1);
        complexity_score += max_level * 2;

        // Content length contributes
        complexity_score += (content.text.len() / 1000).min(10);

        match complexity_score {
            0..=5 => Ok(ComplexityLevel::Simple),
            6..=15 => Ok(ComplexityLevel::Moderate),
            16..=25 => Ok(ComplexityLevel::Complex),
            _ => Ok(ComplexityLevel::VeryComplex),
        }
    }
}

impl LayoutAnalyzer for DefaultLayoutAnalyzer {
    fn analyze_layout(&self, document_path: &Path) -> RragResult<DocumentLayout> {
        // Simplified synchronous implementation
        let content = DocumentContent {
            text: "Sample content".to_string(),
            document_type: self.detect_document_type(document_path)?,
            page_count: 1,
            page_width: 8.5,
            page_height: 11.0,
            has_headers: false,
            has_footers: false,
            formatting_info: FormattingInfo::default(),
        };

        let sections = vec![DocumentSection {
            id: "section_0".to_string(),
            title: Some("Main Content".to_string()),
            content: content.text.clone(),
            section_type: SectionType::Body,
            level: 1,
            page_range: (1, 1),
        }];

        Ok(DocumentLayout {
            pages: content.page_count,
            sections,
            reading_order: vec!["section_0".to_string()],
            columns: None,
            document_type: content.document_type,
        })
    }

    fn detect_sections(&self, content: &str) -> RragResult<Vec<DocumentSection>> {
        // Simple section detection
        let sections = vec![DocumentSection {
            id: "section_0".to_string(),
            title: None,
            content: content.to_string(),
            section_type: SectionType::Body,
            level: 1,
            page_range: (1, 1),
        }];

        Ok(sections)
    }

    fn extract_reading_order(&self, layout: &DocumentLayout) -> RragResult<Vec<String>> {
        Ok(layout.sections.iter().map(|s| s.id.clone()).collect())
    }
}

// Supporting structures
#[derive(Debug, Clone)]
pub struct DocumentContent {
    pub text: String,
    pub document_type: DocumentType,
    pub page_count: usize,
    pub page_width: f32,
    pub page_height: f32,
    pub has_headers: bool,
    pub has_footers: bool,
    pub formatting_info: FormattingInfo,
}

#[derive(Debug, Clone)]
pub struct DocumentStructure {
    pub detection_confidence: f32,
    pub hierarchy_levels: Vec<HierarchyLevel>,
    pub structural_elements: Vec<StructuralElement>,
}

#[derive(Debug, Clone)]
pub struct HierarchyLevel {
    pub level: usize,
    pub elements: Vec<String>,
}

#[derive(Debug, Clone)]
pub struct StructuralElement {
    pub element_type: String,
    pub position: ElementPosition,
    pub properties: HashMap<String, String>,
}

#[derive(Debug, Clone)]
pub struct ElementPosition {
    pub page: usize,
    pub x: f32,
    pub y: f32,
    pub width: f32,
    pub height: f32,
}

#[derive(Debug, Clone)]
pub struct FormattingInfo {
    pub has_consistent_fonts: bool,
    pub has_consistent_spacing: bool,
    pub has_consistent_colors: bool,
    pub font_families: Vec<String>,
    pub font_sizes: Vec<f32>,
}

#[derive(Debug, Clone)]
pub struct PageAnalysis {
    pub page_count: usize,
    pub page_types: Vec<PageType>,
    pub content_distribution: ContentDistribution,
}

#[derive(Debug, Clone)]
pub struct ContentDistribution {
    pub text_percentage: f32,
    pub image_percentage: f32,
    pub table_percentage: f32,
    pub whitespace_percentage: f32,
}

// Enums for layout analysis
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub enum ContentBalance {
    TextHeavy,
    VisualHeavy,
    Balanced,
}

#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub enum ComplexityLevel {
    Simple,
    Moderate,
    Complex,
    VeryComplex,
}

#[derive(Debug, Clone, Copy)]
pub enum PageType {
    TitlePage,
    ContentPage,
    TOCPage,
    IndexPage,
    AppendixPage,
}

#[derive(Debug, Clone, Copy)]
pub enum HeaderRuleType {
    FontSize,
    FontWeight,
    Capitalization,
    Positioning,
    Numbering,
}

#[derive(Debug, Clone, Copy)]
pub enum ColumnAlgorithmType {
    WhitespaceAnalysis,
    TextBlockAlignment,
    StatisticalClustering,
    GeometricDetection,
}

#[derive(Debug, Clone)]
pub enum RuleCondition {
    TextPattern(String),
    FontSize(f32),
    Position(f32, f32),
    ContentLength(usize),
}

#[derive(Debug, Clone)]
pub enum RuleAction {
    ClassifyAsSection(SectionType),
    SetHierarchyLevel(usize),
    MarkAsHeader,
    MarkAsFooter,
}

// Simplified implementations for components
impl StructureDetector {
    pub fn new() -> RragResult<Self> {
        Ok(Self {
            hierarchy_patterns: vec![],
            type_classifiers: HashMap::new(),
            layout_rules: vec![],
        })
    }

    pub async fn detect_structure(
        &self,
        _content: &DocumentContent,
    ) -> RragResult<DocumentStructure> {
        Ok(DocumentStructure {
            detection_confidence: 0.8,
            hierarchy_levels: vec![],
            structural_elements: vec![],
        })
    }
}

impl SectionIdentifier {
    pub fn new() -> RragResult<Self> {
        Ok(Self {
            section_patterns: HashMap::new(),
            header_rules: vec![],
            content_classifier: ContentClassifier::new(),
        })
    }

    pub async fn identify_sections(
        &self,
        content: &DocumentContent,
        _structure: &DocumentStructure,
    ) -> RragResult<Vec<DocumentSection>> {
        Ok(vec![DocumentSection {
            id: "section_0".to_string(),
            title: Some("Main Content".to_string()),
            content: content.text.clone(),
            section_type: SectionType::Body,
            level: 1,
            page_range: (1, content.page_count),
        }])
    }
}

impl ReadingOrderAnalyzer {
    pub fn new() -> RragResult<Self> {
        Ok(Self {
            strategies: HashMap::new(),
            flow_detector: FlowDetector::new(),
            region_analyzer: RegionAnalyzer::new(),
        })
    }

    pub async fn analyze_reading_order(
        &self,
        _content: &DocumentContent,
        sections: &[DocumentSection],
    ) -> RragResult<Vec<String>> {
        Ok(sections.iter().map(|s| s.id.clone()).collect())
    }
}

impl ColumnDetector {
    pub fn new() -> RragResult<Self> {
        Ok(Self {
            algorithms: vec![],
            layout_classifier: LayoutClassifier::new(),
            spacing_analyzer: SpacingAnalyzer::new(),
        })
    }

    pub async fn detect_columns(
        &self,
        content: &DocumentContent,
    ) -> RragResult<Option<ColumnLayout>> {
        // Simple heuristic: if content is wide and long, assume multiple columns
        if content.page_width > 10.0 && content.text.len() > 5000 {
            Ok(Some(ColumnLayout {
                column_count: 2,
                column_widths: vec![0.48, 0.48],
                gutter_width: 0.04,
            }))
        } else {
            Ok(None)
        }
    }
}

impl PageAnalyzer {
    pub fn new() -> RragResult<Self> {
        Ok(Self {
            classifiers: HashMap::new(),
            distribution_analyzer: ContentDistributionAnalyzer::new(),
            margin_detector: MarginDetector::new(),
        })
    }

    pub async fn analyze_pages(&self, content: &DocumentContent) -> RragResult<PageAnalysis> {
        Ok(PageAnalysis {
            page_count: content.page_count,
            page_types: vec![PageType::ContentPage; content.page_count],
            content_distribution: ContentDistribution {
                text_percentage: 0.8,
                image_percentage: 0.1,
                table_percentage: 0.05,
                whitespace_percentage: 0.05,
            },
        })
    }
}

// Default implementations for helper structures
impl Default for DocumentStructure {
    fn default() -> Self {
        Self {
            detection_confidence: 0.5,
            hierarchy_levels: vec![],
            structural_elements: vec![],
        }
    }
}

impl Default for FormattingInfo {
    fn default() -> Self {
        Self {
            has_consistent_fonts: true,
            has_consistent_spacing: true,
            has_consistent_colors: true,
            font_families: vec!["Arial".to_string()],
            font_sizes: vec![12.0],
        }
    }
}

// Minimal implementations for component structs
impl ContentClassifier {
    pub fn new() -> Self {
        Self {
            models: HashMap::new(),
            feature_extractors: Vec::new(),
        }
    }
}

impl FlowDetector {
    pub fn new() -> Self {
        Self {
            algorithms: Vec::new(),
            pattern_matchers: Vec::new(),
        }
    }
}

impl RegionAnalyzer {
    pub fn new() -> Self {
        Self {
            classifiers: Vec::new(),
            relationship_detectors: Vec::new(),
        }
    }
}

impl LayoutClassifier {
    pub fn new() -> Self {
        Self {
            features: Vec::new(),
            decision_trees: Vec::new(),
        }
    }
}

impl SpacingAnalyzer {
    pub fn new() -> Self {
        Self {
            metrics: Vec::new(),
            threshold_calculator: ThresholdCalculator::new(),
        }
    }
}

impl ContentDistributionAnalyzer {
    pub fn new() -> Self {
        Self {
            metrics: Vec::new(),
            balance_calculators: Vec::new(),
        }
    }
}

impl MarginDetector {
    pub fn new() -> Self {
        Self {
            methods: Vec::new(),
            consistency_checker: ConsistencyChecker::new(),
        }
    }
}

// Additional empty structs for compilation
pub struct ClassificationRule;
pub struct FeatureExtractor;
pub struct ClassificationModel;
pub struct TextFeatureExtractor;
pub struct FlowPattern;
pub struct PriorityRule;
pub struct FlowDetectionAlgorithm;
pub struct FlowPatternMatcher;
pub struct RegionClassifier;
pub struct RelationshipDetector;
pub struct LayoutFeature;
pub struct DecisionTree;
pub struct SpacingMetric;
pub struct ThresholdCalculator;
pub struct PagePattern;
pub struct DistributionMetric;
pub struct BalanceCalculator;
pub struct MarginDetectionMethod;
pub struct ConsistencyChecker;

impl ThresholdCalculator {
    pub fn new() -> Self {
        Self
    }
}

impl ConsistencyChecker {
    pub fn new() -> Self {
        Self
    }
}
#[derive(Debug, Clone)]
pub struct ContextRequirement;

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

    #[test]
    fn test_layout_analyzer_creation() {
        let config = LayoutAnalysisConfig::default();
        let analyzer = DefaultLayoutAnalyzer::new(config).unwrap();

        assert!(analyzer.config.detect_structure);
        assert!(analyzer.config.identify_sections);
    }

    #[test]
    fn test_document_type_detection() {
        let config = LayoutAnalysisConfig::default();
        let analyzer = DefaultLayoutAnalyzer::new(config).unwrap();

        let pdf_path = std::path::Path::new("test.pdf");
        assert_eq!(
            analyzer.detect_document_type(pdf_path).unwrap(),
            DocumentType::PDF
        );

        let md_path = std::path::Path::new("test.md");
        assert_eq!(
            analyzer.detect_document_type(md_path).unwrap(),
            DocumentType::Markdown
        );
    }

    #[test]
    fn test_content_balance_analysis() {
        let config = LayoutAnalysisConfig::default();
        let analyzer = DefaultLayoutAnalyzer::new(config).unwrap();

        let short_content = DocumentContent {
            text: "Short".to_string(),
            document_type: DocumentType::PlainText,
            page_count: 1,
            page_width: 8.5,
            page_height: 11.0,
            has_headers: false,
            has_footers: false,
            formatting_info: FormattingInfo::default(),
        };

        let balance = analyzer.analyze_content_balance(&short_content).unwrap();
        assert!(matches!(balance, ContentBalance::VisualHeavy));
    }

    #[test]
    fn test_complexity_assessment() {
        let config = LayoutAnalysisConfig::default();
        let analyzer = DefaultLayoutAnalyzer::new(config).unwrap();

        let simple_layout = DocumentLayout {
            pages: 1,
            sections: vec![],
            reading_order: vec![],
            columns: None,
            document_type: DocumentType::PlainText,
        };

        let simple_content = DocumentContent {
            text: "Simple content".to_string(),
            document_type: DocumentType::PlainText,
            page_count: 1,
            page_width: 8.5,
            page_height: 11.0,
            has_headers: false,
            has_footers: false,
            formatting_info: FormattingInfo::default(),
        };

        let complexity = analyzer
            .assess_complexity_level(&simple_layout, &simple_content)
            .unwrap();
        assert!(matches!(complexity, ComplexityLevel::Simple));
    }
}