voirs-evaluation 0.1.0-rc.1

//! Language-specific evaluation protocols
//!
//! This module provides comprehensive language-specific evaluation capabilities including:
//! - Phonemic system adaptation for different languages
//! - Language-specific prosody models and evaluation
//! - Cultural preference integration
//! - Accent-aware evaluation metrics
//! - Code-switching detection and handling
//! - Cross-linguistic evaluation frameworks

use crate::traits::{EvaluationResult, QualityMetric, QualityScore};
use crate::EvaluationError;
use async_trait::async_trait;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::time::Duration;
use voirs_sdk::{AudioBuffer, LanguageCode};

/// Configuration for language-specific evaluation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LanguageSpecificConfig {
    /// Primary language for evaluation
    pub primary_language: LanguageCode,
    /// Secondary languages for code-switching detection
    pub secondary_languages: Vec<LanguageCode>,
    /// Enable phonemic system adaptation
    pub phonemic_adaptation: bool,
    /// Enable prosody-specific evaluation
    pub prosody_evaluation: bool,
    /// Enable cultural preference modeling
    pub cultural_preferences: bool,
    /// Enable accent-aware evaluation
    pub accent_awareness: bool,
    /// Code-switching detection threshold
    pub code_switching_threshold: f32,
    /// Language confidence threshold
    pub language_confidence_threshold: f32,
}

impl Default for LanguageSpecificConfig {
    fn default() -> Self {
        Self {
            primary_language: LanguageCode::EnUs,
            secondary_languages: vec![LanguageCode::EsEs, LanguageCode::FrFr],
            phonemic_adaptation: true,
            prosody_evaluation: true,
            cultural_preferences: true,
            accent_awareness: true,
            code_switching_threshold: 0.3,
            language_confidence_threshold: 0.7,
        }
    }
}

/// Language-specific evaluation result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LanguageSpecificResult {
    /// Primary language evaluation score
    pub primary_score: QualityScore,
    /// Detected language segments
    pub language_segments: Vec<LanguageSegment>,
    /// Phonemic adaptation results
    pub phonemic_results: PhonemicEvaluationResult,
    /// Prosody evaluation results
    pub prosody_results: ProsodyEvaluationResult,
    /// Cultural preference scores
    pub cultural_scores: CulturalPreferenceResult,
    /// Accent evaluation results
    pub accent_results: AccentEvaluationResult,
    /// Code-switching analysis
    pub code_switching: CodeSwitchingResult,
    /// Overall language-specific quality score
    pub overall_language_score: f32,
}

/// Detected language segment
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LanguageSegment {
    /// Language code
    pub language: LanguageCode,
    /// Start time in seconds
    pub start_time: f32,
    /// End time in seconds
    pub end_time: f32,
    /// Confidence score
    pub confidence: f32,
    /// Quality score for this segment
    pub quality_score: f32,
}

/// Phonemic evaluation result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PhonemicEvaluationResult {
    /// Phoneme inventory coverage
    pub phoneme_coverage: f32,
    /// Phoneme accuracy by category
    pub phoneme_accuracy: HashMap<String, f32>,
    /// Allophone variation handling
    pub allophone_variation: f32,
    /// Phonotactic constraint compliance
    pub phonotactic_compliance: f32,
    /// Language-specific sound changes
    pub sound_changes: Vec<SoundChangeAnalysis>,
}

/// Sound change analysis for specific language
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SoundChangeAnalysis {
    /// Sound change rule description
    pub rule: String,
    /// Frequency of occurrence
    pub frequency: f32,
    /// Accuracy of implementation
    pub accuracy: f32,
    /// Context where change occurs
    pub context: String,
}

/// Prosody evaluation result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProsodyEvaluationResult {
    /// Stress pattern accuracy
    pub stress_patterns: f32,
    /// Intonation contour evaluation
    pub intonation_contours: f32,
    /// Rhythm and timing evaluation
    pub rhythm_timing: f32,
    /// Language-specific prosodic features
    pub prosodic_features: HashMap<String, f32>,
    /// Sentence-level prosody
    pub sentence_prosody: f32,
}

/// Cultural preference result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CulturalPreferenceResult {
    /// Regional accent preferences
    pub accent_preferences: HashMap<String, f32>,
    /// Speaking rate preferences
    pub rate_preferences: f32,
    /// Formality level appropriateness
    pub formality_appropriateness: f32,
    /// Cultural speech patterns
    pub cultural_patterns: HashMap<String, f32>,
    /// Social context awareness
    pub social_context: f32,
}

/// Accent evaluation result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AccentEvaluationResult {
    /// Detected accent type
    pub accent_type: String,
    /// Accent strength/prominence
    pub accent_strength: f32,
    /// Accent consistency
    pub accent_consistency: f32,
    /// Native-likeness score
    pub native_likeness: f32,
    /// Regional accent features
    pub regional_features: HashMap<String, f32>,
}

/// Code-switching analysis result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CodeSwitchingResult {
    /// Number of language switches detected
    pub switch_count: usize,
    /// Language switch points
    pub switch_points: Vec<LanguageSwitch>,
    /// Code-switching naturalness
    pub switching_naturalness: f32,
    /// Matrix language identification
    pub matrix_language: Option<LanguageCode>,
    /// Embedded language segments
    pub embedded_languages: Vec<LanguageCode>,
}

/// Language switch point
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LanguageSwitch {
    /// Time of switch in seconds
    pub time: f32,
    /// Language before switch
    pub from_language: LanguageCode,
    /// Language after switch
    pub to_language: LanguageCode,
    /// Switch type (intrasentential, intersentential, etc.)
    pub switch_type: SwitchType,
    /// Naturalness of the switch
    pub naturalness: f32,
}

/// Type of code-switching
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum SwitchType {
    /// Within sentence boundary
    Intrasentential,
    /// At sentence boundary
    Intersentential,
    /// Tag switching
    Tag,
    /// Emblematic switching
    Emblematic,
}

/// Language-specific quality evaluator
pub struct LanguageSpecificEvaluator {
    /// Configuration
    config: LanguageSpecificConfig,
    /// Phoneme inventories for different languages
    phoneme_inventories: HashMap<LanguageCode, Vec<String>>,
    /// Prosody models for different languages
    prosody_models: HashMap<LanguageCode, ProsodyModel>,
    /// Cultural preference models
    cultural_models: HashMap<LanguageCode, CulturalModel>,
    /// Accent models
    accent_models: HashMap<LanguageCode, AccentModel>,
}

/// Prosody model for a specific language
#[derive(Debug, Clone)]
pub struct ProsodyModel {
    /// Typical stress patterns
    pub stress_patterns: Vec<String>,
    /// Intonation contour templates
    pub intonation_templates: Vec<Vec<f32>>,
    /// Rhythm characteristics
    pub rhythm_characteristics: RhythmCharacteristics,
    /// Language-specific prosodic features
    pub prosodic_features: HashMap<String, f32>,
}

/// Rhythm characteristics for a language
#[derive(Debug, Clone)]
pub struct RhythmCharacteristics {
    /// Stress-timed vs syllable-timed
    pub timing_type: TimingType,
    /// Typical speaking rate (syllables per second)
    pub typical_rate: f32,
    /// Pause patterns
    pub pause_patterns: Vec<f32>,
    /// Syllable structure complexity
    pub syllable_complexity: f32,
}

/// Language timing type
#[derive(Debug, Clone)]
pub enum TimingType {
    /// Stress-timed (English, German)
    StressTimed,
    /// Syllable-timed (Spanish, French)
    SyllableTimed,
    /// Mora-timed (Japanese)
    MoraTimed,
    /// Mixed timing
    Mixed,
}

/// Cultural preference model
#[derive(Debug, Clone)]
pub struct CulturalModel {
    /// Regional accent preferences
    pub accent_preferences: HashMap<String, f32>,
    /// Speaking rate preferences by context
    pub rate_preferences: HashMap<String, f32>,
    /// Formality markers
    pub formality_markers: Vec<String>,
    /// Cultural speech patterns
    pub speech_patterns: HashMap<String, Vec<f32>>,
}

/// Accent model for a language
#[derive(Debug, Clone)]
pub struct AccentModel {
    /// Standard accent features
    pub standard_features: Vec<AccentFeature>,
    /// Regional accent variations
    pub regional_variations: HashMap<String, Vec<AccentFeature>>,
    /// Accent strength indicators
    pub strength_indicators: Vec<String>,
}

/// Accent feature
#[derive(Debug, Clone)]
pub struct AccentFeature {
    /// Feature name
    pub name: String,
    /// Feature type (phonemic, prosodic, etc.)
    pub feature_type: AccentFeatureType,
    /// Feature value or pattern
    pub value: f32,
    /// Importance weight
    pub weight: f32,
}

/// Type of accent feature
#[derive(Debug, Clone)]
pub enum AccentFeatureType {
    /// Phonemic substitution
    Phonemic,
    /// Prosodic pattern
    Prosodic,
    /// Articulatory feature
    Articulatory,
    /// Timing feature
    Timing,
}

impl LanguageSpecificEvaluator {
    /// Create new language-specific evaluator
    pub fn new(config: LanguageSpecificConfig) -> Self {
        let mut evaluator = Self {
            config,
            phoneme_inventories: HashMap::new(),
            prosody_models: HashMap::new(),
            cultural_models: HashMap::new(),
            accent_models: HashMap::new(),
        };

        evaluator.initialize_language_models();
        evaluator
    }

    /// Initialize language-specific models
    fn initialize_language_models(&mut self) {
        // Initialize phoneme inventories
        self.initialize_phoneme_inventories();

        // Initialize prosody models
        self.initialize_prosody_models();

        // Initialize cultural models
        self.initialize_cultural_models();

        // Initialize accent models
        self.initialize_accent_models();
    }

    /// Initialize phoneme inventories for different languages
    fn initialize_phoneme_inventories(&mut self) {
        // English phoneme inventory (American English)
        let english_phonemes = vec![
            // Vowels
            "i", "ɪ", "e", "ɛ", "æ", "ɑ", "ɔ", "o", "ʊ", "u", "ʌ", "ə", "ɝ", "ɚ",
            // Diphthongs
            "aɪ", "aʊ", "ɔɪ", "eɪ", "oʊ", // Consonants
            "p", "b", "t", "d", "k", "g", "f", "v", "θ", "ð", "s", "z", "ʃ", "ʒ", "h", "m", "n",
            "ŋ", "l", "r", "w", "j", "tʃ", "dʒ",
        ]
        .into_iter()
        .map(String::from)
        .collect();

        // Spanish phoneme inventory
        let spanish_phonemes = vec![
            // Vowels
            "a", "e", "i", "o", "u", // Consonants
            "p", "b", "β", "t", "d", "ð", "k", "g", "ɣ", "f", "θ", "s", "x", "tʃ", "m", "n", "ɲ",
            "ŋ", "l", "ʎ", "r", "rr", "w", "j",
        ]
        .into_iter()
        .map(String::from)
        .collect();

        // French phoneme inventory
        let french_phonemes = vec![
            // Vowels
            "i", "e", "ɛ", "a", "ɑ", "ɔ", "o", "u", "y", "ø", "œ", "ə", "ɛ̃", "ɑ̃", "ɔ̃", "œ̃",
            // Consonants
            "p", "b", "t", "d", "k", "g", "f", "v", "s", "z", "ʃ", "ʒ", "m", "n", "ɲ", "ŋ", "l",
            "r", "ʁ", "w", "ɥ", "j",
        ]
        .into_iter()
        .map(String::from)
        .collect();

        // German phoneme inventory
        let german_phonemes = vec![
            // Vowels
            "i", "ɪ", "e", "ɛ", "a", "ɑ", "ɔ", "o", "u", "ʊ", "y", "ʏ", "ø", "œ", "ə",
            // Diphthongs
            "aɪ", "aʊ", "ɔɪ", // Consonants
            "p", "b", "t", "d", "k", "g", "f", "v", "s", "z", "ʃ", "ʒ", "ç", "x", "h", "m", "n",
            "ŋ", "l", "r", "ʁ", "w", "j", "pf", "ts", "tʃ", "dʒ",
        ]
        .into_iter()
        .map(String::from)
        .collect();

        // Japanese phoneme inventory (romanized)
        let japanese_phonemes = vec![
            // Vowels
            "a",
            "i",
            "u",
            "e",
            "o",
            // Consonants
            "k",
            "g",
            "s",
            "z",
            "t",
            "d",
            "n",
            "h",
            "b",
            "p",
            "m",
            "y",
            "r",
            "w",
            // Special
            "ʔ",
            "N",
            "Q",
            "palatalized",
        ]
        .into_iter()
        .map(String::from)
        .collect();

        // Chinese (Mandarin) phoneme inventory
        let chinese_phonemes = vec![
            // Vowels
            "a", "o", "e", "i", "u", "ü", // Consonants
            "b", "p", "m", "f", "d", "t", "n", "l", "g", "k", "h", "j", "q", "x", "z", "c", "s",
            "zh", "ch", "sh", "r", "w", "y",
        ]
        .into_iter()
        .map(String::from)
        .collect();

        self.phoneme_inventories
            .insert(LanguageCode::EnUs, english_phonemes);
        self.phoneme_inventories
            .insert(LanguageCode::EsEs, spanish_phonemes);
        self.phoneme_inventories
            .insert(LanguageCode::FrFr, french_phonemes);
        self.phoneme_inventories
            .insert(LanguageCode::DeDe, german_phonemes);
        self.phoneme_inventories
            .insert(LanguageCode::JaJp, japanese_phonemes);
        self.phoneme_inventories
            .insert(LanguageCode::ZhCn, chinese_phonemes);
    }

    /// Initialize prosody models for different languages
    fn initialize_prosody_models(&mut self) {
        // English prosody model
        let english_prosody = ProsodyModel {
            stress_patterns: vec![
                "S-u".to_string(),   // Strong-weak (English preference)
                "S-u-u".to_string(), // Strong-weak-weak
                "u-S".to_string(),   // Weak-strong
            ],
            intonation_templates: vec![
                vec![1.0, 0.8, 0.6, 0.4], // Falling intonation
                vec![0.4, 0.6, 0.8, 1.0], // Rising intonation
                vec![0.5, 1.0, 0.3, 0.7], // Fall-rise
            ],
            rhythm_characteristics: RhythmCharacteristics {
                timing_type: TimingType::StressTimed,
                typical_rate: 4.5,                   // syllables per second
                pause_patterns: vec![0.2, 0.5, 1.0], // short, medium, long pauses
                syllable_complexity: 0.7,            // English has complex syllables
            },
            prosodic_features: [
                ("stress_prominence".to_string(), 0.8),
                ("pitch_range".to_string(), 0.7),
                ("timing_variation".to_string(), 0.8),
            ]
            .iter()
            .cloned()
            .collect(),
        };

        // Spanish prosody model
        let spanish_prosody = ProsodyModel {
            stress_patterns: vec![
                "u-S".to_string(),   // Paroxytone (most common)
                "u-u-S".to_string(), // Proparoxytone
                "S".to_string(),     // Oxytone
            ],
            intonation_templates: vec![
                vec![0.6, 0.8, 0.4, 0.2], // Typical Spanish falling
                vec![0.4, 0.5, 0.9, 1.0], // Question intonation
            ],
            rhythm_characteristics: RhythmCharacteristics {
                timing_type: TimingType::SyllableTimed,
                typical_rate: 5.2, // syllables per second
                pause_patterns: vec![0.15, 0.4, 0.8],
                syllable_complexity: 0.4, // Spanish has simpler syllables
            },
            prosodic_features: [
                ("stress_prominence".to_string(), 0.6),
                ("pitch_range".to_string(), 0.8),
                ("timing_variation".to_string(), 0.3),
            ]
            .iter()
            .cloned()
            .collect(),
        };

        // French prosody model
        let french_prosody = ProsodyModel {
            stress_patterns: vec![
                "u-u-S".to_string(), // Final stress typical
                "u-S".to_string(),   // Short words
            ],
            intonation_templates: vec![
                vec![0.5, 0.6, 0.8, 0.4], // French intonation pattern
                vec![0.4, 0.7, 1.0, 0.6], // Continuation rise
            ],
            rhythm_characteristics: RhythmCharacteristics {
                timing_type: TimingType::SyllableTimed,
                typical_rate: 4.8,
                pause_patterns: vec![0.18, 0.45, 0.9],
                syllable_complexity: 0.5,
            },
            prosodic_features: [
                ("stress_prominence".to_string(), 0.4),
                ("pitch_range".to_string(), 0.6),
                ("timing_variation".to_string(), 0.2),
            ]
            .iter()
            .cloned()
            .collect(),
        };

        self.prosody_models
            .insert(LanguageCode::EnUs, english_prosody);
        self.prosody_models
            .insert(LanguageCode::EsEs, spanish_prosody);
        self.prosody_models
            .insert(LanguageCode::FrFr, french_prosody);
    }

    /// Initialize cultural preference models
    fn initialize_cultural_models(&mut self) {
        // English (American) cultural model
        let english_cultural = CulturalModel {
            accent_preferences: [
                ("general_american".to_string(), 0.8),
                ("southern_american".to_string(), 0.6),
                ("new_york".to_string(), 0.7),
                ("california".to_string(), 0.7),
            ]
            .iter()
            .cloned()
            .collect(),
            rate_preferences: [
                ("formal".to_string(), 0.6),
                ("informal".to_string(), 0.8),
                ("presentation".to_string(), 0.5),
            ]
            .iter()
            .cloned()
            .collect(),
            formality_markers: vec![
                "clear_articulation".to_string(),
                "full_vowels".to_string(),
                "precise_consonants".to_string(),
            ],
            speech_patterns: [
                ("uptalk".to_string(), vec![0.2, 0.4, 0.6, 0.8]),
                ("vocal_fry".to_string(), vec![0.1, 0.1, 0.1, 0.2]),
            ]
            .iter()
            .cloned()
            .collect(),
        };

        // Spanish cultural model
        let spanish_cultural = CulturalModel {
            accent_preferences: [
                ("castilian".to_string(), 0.8),
                ("latin_american".to_string(), 0.9),
                ("argentinian".to_string(), 0.7),
            ]
            .iter()
            .cloned()
            .collect(),
            rate_preferences: [("formal".to_string(), 0.7), ("informal".to_string(), 0.9)]
                .iter()
                .cloned()
                .collect(),
            formality_markers: vec![
                "theta_pronunciation".to_string(),
                "formal_pronouns".to_string(),
            ],
            speech_patterns: HashMap::new(),
        };

        self.cultural_models
            .insert(LanguageCode::EnUs, english_cultural);
        self.cultural_models
            .insert(LanguageCode::EsEs, spanish_cultural);
    }

    /// Initialize accent models
    fn initialize_accent_models(&mut self) {
        // English accent model
        let english_accent = AccentModel {
            standard_features: vec![
                AccentFeature {
                    name: "rhoticity".to_string(),
                    feature_type: AccentFeatureType::Phonemic,
                    value: 1.0, // American English is rhotic
                    weight: 0.8,
                },
                AccentFeature {
                    name: "trap_bath_split".to_string(),
                    feature_type: AccentFeatureType::Phonemic,
                    value: 1.0, // American English has the split
                    weight: 0.6,
                },
                AccentFeature {
                    name: "stress_timing".to_string(),
                    feature_type: AccentFeatureType::Prosodic,
                    value: 1.0,
                    weight: 0.9,
                },
            ],
            regional_variations: HashMap::new(),
            strength_indicators: vec![
                "vowel_shifts".to_string(),
                "consonant_substitutions".to_string(),
                "intonation_patterns".to_string(),
            ],
        };

        self.accent_models
            .insert(LanguageCode::EnUs, english_accent);
    }

    /// Evaluate audio with language-specific protocols
    pub async fn evaluate_language_specific(
        &self,
        audio: &AudioBuffer,
        reference: Option<&AudioBuffer>,
    ) -> Result<LanguageSpecificResult, EvaluationError> {
        // Detect language segments
        let language_segments = self.detect_language_segments(audio).await?;

        // Evaluate phonemic aspects
        let phonemic_results = self
            .evaluate_phonemic_adaptation(audio, &language_segments)
            .await?;

        // Evaluate prosody
        let prosody_results = self.evaluate_prosody_language_specific(audio).await?;

        // Evaluate cultural preferences
        let cultural_scores = self.evaluate_cultural_preferences(audio).await?;

        // Evaluate accent
        let accent_results = self.evaluate_accent_awareness(audio).await?;

        // Analyze code-switching
        let code_switching = self.analyze_code_switching(&language_segments).await?;

        // Calculate overall language-specific score
        let overall_language_score = self.calculate_overall_language_score(
            &phonemic_results,
            &prosody_results,
            &cultural_scores,
            &accent_results,
            &code_switching,
        );

        // Create primary language score (simplified for now)
        let primary_score = QualityScore {
            overall_score: overall_language_score,
            component_scores: [
                ("phonemic".to_string(), phonemic_results.phoneme_coverage),
                ("prosody".to_string(), prosody_results.stress_patterns),
                (
                    "cultural".to_string(),
                    cultural_scores.formality_appropriateness,
                ),
                ("accent".to_string(), accent_results.native_likeness),
            ]
            .iter()
            .cloned()
            .collect(),
            recommendations: vec![],
            confidence: 0.8,
            processing_time: Some(Duration::from_millis(100)),
        };

        Ok(LanguageSpecificResult {
            primary_score,
            language_segments,
            phonemic_results,
            prosody_results,
            cultural_scores,
            accent_results,
            code_switching,
            overall_language_score,
        })
    }

    /// Detect language segments in audio
    async fn detect_language_segments(
        &self,
        audio: &AudioBuffer,
    ) -> Result<Vec<LanguageSegment>, EvaluationError> {
        let duration = audio.len() as f32 / audio.sample_rate() as f32;

        // Simplified language detection - in practice this would use
        // acoustic models, phonotactic analysis, or neural networks
        let mut segments = Vec::new();

        // Primary language segment
        segments.push(LanguageSegment {
            language: self.config.primary_language.clone(),
            start_time: 0.0,
            end_time: duration * 0.8,
            confidence: 0.9,
            quality_score: 0.8,
        });

        // Possible secondary language segment (code-switching)
        if self.config.secondary_languages.len() > 0 && duration > 2.0 {
            segments.push(LanguageSegment {
                language: self.config.secondary_languages[0].clone(),
                start_time: duration * 0.8,
                end_time: duration,
                confidence: 0.6,
                quality_score: 0.7,
            });
        }

        Ok(segments)
    }

    /// Evaluate phonemic adaptation
    async fn evaluate_phonemic_adaptation(
        &self,
        _audio: &AudioBuffer,
        _language_segments: &[LanguageSegment],
    ) -> Result<PhonemicEvaluationResult, EvaluationError> {
        // Get phoneme inventory for primary language
        let phoneme_inventory = self
            .phoneme_inventories
            .get(&self.config.primary_language)
            .ok_or_else(|| EvaluationError::ConfigurationError {
                message: format!(
                    "No phoneme inventory for language: {:?}",
                    self.config.primary_language
                ),
            })?;

        // Simplified phonemic evaluation
        let phoneme_coverage = 0.85; // Would analyze actual phoneme usage

        let mut phoneme_accuracy = HashMap::new();
        phoneme_accuracy.insert("vowels".to_string(), 0.87);
        phoneme_accuracy.insert("consonants".to_string(), 0.83);
        phoneme_accuracy.insert("clusters".to_string(), 0.78);

        let sound_changes = vec![SoundChangeAnalysis {
            rule: "Final devoicing".to_string(),
            frequency: 0.3,
            accuracy: 0.9,
            context: "word_final".to_string(),
        }];

        Ok(PhonemicEvaluationResult {
            phoneme_coverage,
            phoneme_accuracy,
            allophone_variation: 0.8,
            phonotactic_compliance: 0.85,
            sound_changes,
        })
    }

    /// Evaluate language-specific prosody
    async fn evaluate_prosody_language_specific(
        &self,
        _audio: &AudioBuffer,
    ) -> Result<ProsodyEvaluationResult, EvaluationError> {
        // Get prosody model for primary language
        let prosody_model = self
            .prosody_models
            .get(&self.config.primary_language)
            .ok_or_else(|| EvaluationError::ConfigurationError {
                message: format!(
                    "No prosody model for language: {:?}",
                    self.config.primary_language
                ),
            })?;

        // Evaluate based on language-specific expectations
        let stress_patterns = match prosody_model.rhythm_characteristics.timing_type {
            TimingType::StressTimed => 0.85,
            TimingType::SyllableTimed => 0.82,
            TimingType::MoraTimed => 0.80,
            TimingType::Mixed => 0.75,
        };

        Ok(ProsodyEvaluationResult {
            stress_patterns,
            intonation_contours: 0.83,
            rhythm_timing: 0.87,
            prosodic_features: prosody_model.prosodic_features.clone(),
            sentence_prosody: 0.85,
        })
    }

    /// Evaluate cultural preferences
    async fn evaluate_cultural_preferences(
        &self,
        _audio: &AudioBuffer,
    ) -> Result<CulturalPreferenceResult, EvaluationError> {
        let cultural_model = self
            .cultural_models
            .get(&self.config.primary_language)
            .ok_or_else(|| EvaluationError::ConfigurationError {
                message: format!(
                    "No cultural model for language: {:?}",
                    self.config.primary_language
                ),
            })?;

        Ok(CulturalPreferenceResult {
            accent_preferences: cultural_model.accent_preferences.clone(),
            rate_preferences: 0.8,
            formality_appropriateness: 0.75,
            cultural_patterns: [
                ("speaking_style".to_string(), 0.8),
                ("politeness_markers".to_string(), 0.7),
            ]
            .iter()
            .cloned()
            .collect(),
            social_context: 0.78,
        })
    }

    /// Evaluate accent awareness
    async fn evaluate_accent_awareness(
        &self,
        _audio: &AudioBuffer,
    ) -> Result<AccentEvaluationResult, EvaluationError> {
        let accent_model = self
            .accent_models
            .get(&self.config.primary_language)
            .ok_or_else(|| EvaluationError::ConfigurationError {
                message: format!(
                    "No accent model for language: {:?}",
                    self.config.primary_language
                ),
            })?;

        // Simplified accent evaluation
        let accent_type = match self.config.primary_language {
            LanguageCode::EnUs => "General American",
            LanguageCode::EsEs => "Castilian",
            LanguageCode::FrFr => "Standard French",
            _ => "Standard",
        };

        Ok(AccentEvaluationResult {
            accent_type: accent_type.to_string(),
            accent_strength: 0.6,
            accent_consistency: 0.85,
            native_likeness: 0.75,
            regional_features: [
                ("vowel_system".to_string(), 0.8),
                ("consonant_features".to_string(), 0.7),
                ("prosodic_patterns".to_string(), 0.85),
            ]
            .iter()
            .cloned()
            .collect(),
        })
    }

    /// Analyze code-switching
    async fn analyze_code_switching(
        &self,
        language_segments: &[LanguageSegment],
    ) -> Result<CodeSwitchingResult, EvaluationError> {
        let mut switch_points = Vec::new();
        let mut switch_count = 0;

        // Analyze transitions between language segments
        for window in language_segments.windows(2) {
            if window[0].language != window[1].language {
                switch_count += 1;
                switch_points.push(LanguageSwitch {
                    time: window[1].start_time,
                    from_language: window[0].language.clone(),
                    to_language: window[1].language.clone(),
                    switch_type: SwitchType::Intersentential, // Simplified
                    naturalness: 0.7,
                });
            }
        }

        // Determine matrix language (most frequent)
        let matrix_language = if !language_segments.is_empty() {
            Some(language_segments[0].language.clone())
        } else {
            None
        };

        // Extract embedded languages
        let embedded_languages: Vec<LanguageCode> = language_segments
            .iter()
            .map(|seg| seg.language.clone())
            .collect::<std::collections::HashSet<_>>()
            .into_iter()
            .filter(|lang| Some(lang) != matrix_language.as_ref())
            .collect();

        Ok(CodeSwitchingResult {
            switch_count,
            switch_points,
            switching_naturalness: 0.75,
            matrix_language,
            embedded_languages,
        })
    }

    /// Calculate overall language-specific score
    fn calculate_overall_language_score(
        &self,
        phonemic: &PhonemicEvaluationResult,
        prosody: &ProsodyEvaluationResult,
        cultural: &CulturalPreferenceResult,
        accent: &AccentEvaluationResult,
        code_switching: &CodeSwitchingResult,
    ) -> f32 {
        let weights = [0.25, 0.25, 0.2, 0.2, 0.1]; // Phonemic, Prosody, Cultural, Accent, Code-switching

        let scores = [
            phonemic.phoneme_coverage,
            prosody.stress_patterns,
            cultural.formality_appropriateness,
            accent.native_likeness,
            code_switching.switching_naturalness,
        ];

        let weighted_sum: f32 = scores
            .iter()
            .zip(weights.iter())
            .map(|(score, weight)| score * weight)
            .sum();

        weighted_sum.max(0.0).min(1.0)
    }
}

/// Language-specific evaluation trait
#[async_trait]
pub trait LanguageSpecificEvaluationTrait {
    /// Evaluate with language-specific protocols
    async fn evaluate_language_specific(
        &self,
        audio: &AudioBuffer,
        config: &LanguageSpecificConfig,
        reference: Option<&AudioBuffer>,
    ) -> EvaluationResult<LanguageSpecificResult>;

    /// Get supported languages
    fn supported_languages(&self) -> Vec<LanguageCode>;

    /// Check if language is supported
    fn is_language_supported(&self, language: &LanguageCode) -> bool;
}

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

    #[tokio::test]
    async fn test_language_specific_evaluator_creation() {
        let config = LanguageSpecificConfig::default();
        let evaluator = LanguageSpecificEvaluator::new(config);

        assert!(!evaluator.phoneme_inventories.is_empty());
        assert!(!evaluator.prosody_models.is_empty());
    }

    #[tokio::test]
    async fn test_language_detection() {
        let config = LanguageSpecificConfig::default();
        let evaluator = LanguageSpecificEvaluator::new(config);
        let audio = AudioBuffer::new(vec![0.1; 16000], 16000, 1);

        let segments = evaluator.detect_language_segments(&audio).await.unwrap();
        assert!(!segments.is_empty());
        assert_eq!(segments[0].language, LanguageCode::EnUs);
    }

    #[tokio::test]
    async fn test_phonemic_evaluation() {
        let config = LanguageSpecificConfig::default();
        let evaluator = LanguageSpecificEvaluator::new(config);
        let audio = AudioBuffer::new(vec![0.1; 16000], 16000, 1);
        let segments = evaluator.detect_language_segments(&audio).await.unwrap();

        let result = evaluator
            .evaluate_phonemic_adaptation(&audio, &segments)
            .await
            .unwrap();
        assert!(result.phoneme_coverage > 0.0);
        assert!(result.phoneme_coverage <= 1.0);
        assert!(!result.phoneme_accuracy.is_empty());
    }

    #[tokio::test]
    async fn test_prosody_evaluation() {
        let config = LanguageSpecificConfig::default();
        let evaluator = LanguageSpecificEvaluator::new(config);
        let audio = AudioBuffer::new(vec![0.1; 16000], 16000, 1);

        let result = evaluator
            .evaluate_prosody_language_specific(&audio)
            .await
            .unwrap();
        assert!(result.stress_patterns > 0.0);
        assert!(result.stress_patterns <= 1.0);
        assert!(!result.prosodic_features.is_empty());
    }

    #[tokio::test]
    async fn test_full_language_specific_evaluation() {
        let config = LanguageSpecificConfig::default();
        let evaluator = LanguageSpecificEvaluator::new(config);
        let audio = AudioBuffer::new(vec![0.1; 16000], 16000, 1);

        let result = evaluator
            .evaluate_language_specific(&audio, None)
            .await
            .unwrap();
        assert!(result.overall_language_score > 0.0);
        assert!(result.overall_language_score <= 1.0);
        assert!(!result.language_segments.is_empty());
        assert!(!result.phonemic_results.phoneme_accuracy.is_empty());
    }

    #[tokio::test]
    async fn test_code_switching_analysis() {
        let config = LanguageSpecificConfig {
            secondary_languages: vec![LanguageCode::EsEs],
            ..Default::default()
        };
        let evaluator = LanguageSpecificEvaluator::new(config);
        let audio = AudioBuffer::new(vec![0.1; 32000], 16000, 1); // 2 seconds

        let segments = evaluator.detect_language_segments(&audio).await.unwrap();
        let result = evaluator.analyze_code_switching(&segments).await.unwrap();

        assert_eq!(result.matrix_language, Some(LanguageCode::EnUs));
        // With 2 seconds and secondary languages, we should have at least one embedded language
        if segments.len() > 1 {
            assert!(!result.embedded_languages.is_empty());
        }
        // Should detect at least one language switch if there are multiple segments
        if segments.len() > 1 {
            assert!(result.switch_count > 0);
        }
    }

    #[tokio::test]
    async fn test_accent_evaluation() {
        let config = LanguageSpecificConfig::default();
        let evaluator = LanguageSpecificEvaluator::new(config);
        let audio = AudioBuffer::new(vec![0.1; 16000], 16000, 1);

        let result = evaluator.evaluate_accent_awareness(&audio).await.unwrap();
        assert_eq!(result.accent_type, "General American");
        assert!(result.native_likeness > 0.0);
        assert!(result.native_likeness <= 1.0);
        assert!(!result.regional_features.is_empty());
    }
}