use crate::error::{Result, TrustformersError};
use crate::pipeline::{BasePipeline, Pipeline};
use serde::{Deserialize, Serialize};
use trustformers_core::traits::{Model, Tokenizer};
use trustformers_core::Tensor;

/// Configuration for text-to-speech pipeline
#[derive(Clone, Debug, Serialize, Deserialize)]
pub struct TextToSpeechConfig {
    /// Voice to use for synthesis
    pub voice: String,
    /// Speaking rate (0.5 to 2.0)
    pub speaking_rate: f32,
    /// Pitch (0.5 to 2.0)
    pub pitch: f32,
    /// Volume (0.0 to 1.0)
    pub volume: f32,
    /// Sample rate for output audio
    pub sample_rate: u32,
    /// Output format
    pub output_format: AudioFormat,
    /// Maximum duration in seconds
    pub max_duration: Option<f64>,
    /// Enable prosody control
    pub prosody_control: bool,
    /// Enable emotion control
    pub emotion_control: bool,
    /// Target emotion (if emotion control is enabled)
    pub target_emotion: Option<String>,
}

impl Default for TextToSpeechConfig {
    fn default() -> Self {
        Self {
            voice: "default".to_string(),
            speaking_rate: 1.0,
            pitch: 1.0,
            volume: 1.0,
            sample_rate: 22050,
            output_format: AudioFormat::Wav,
            max_duration: Some(60.0),
            prosody_control: false,
            emotion_control: false,
            target_emotion: None,
        }
    }
}

/// Audio format for output
#[derive(Clone, Debug, Serialize, Deserialize)]
pub enum AudioFormat {
    /// WAV format
    Wav,
    /// MP3 format
    Mp3,
    /// FLAC format
    Flac,
    /// OGG format
    Ogg,
    /// Raw PCM
    Raw,
}

/// Input for text-to-speech pipeline
#[derive(Clone, Debug, Serialize, Deserialize)]
pub struct TextToSpeechInput {
    /// Text to synthesize
    pub text: String,
    /// Optional voice override
    pub voice: Option<String>,
    /// Optional speaking rate override
    pub speaking_rate: Option<f32>,
    /// Optional pitch override
    pub pitch: Option<f32>,
    /// Optional volume override
    pub volume: Option<f32>,
    /// Optional emotion override
    pub emotion: Option<String>,
    /// Optional prosody markers
    pub prosody_markers: Option<Vec<ProsodyMarker>>,
}

/// Prosody marker for fine-grained control
#[derive(Clone, Debug, Serialize, Deserialize)]
pub struct ProsodyMarker {
    /// Start position in text
    pub start: usize,
    /// End position in text
    pub end: usize,
    /// Prosody type
    pub prosody_type: ProsodyType,
    /// Intensity (0.0 to 1.0)
    pub intensity: f32,
}

/// Types of prosody control
#[derive(Clone, Debug, Serialize, Deserialize)]
pub enum ProsodyType {
    /// Emphasis
    Emphasis,
    /// Pause
    Pause,
    /// Speed change
    Speed,
    /// Pitch change
    Pitch,
    /// Volume change
    Volume,
}

/// Output from text-to-speech pipeline
#[derive(Clone, Debug, Serialize, Deserialize)]
pub struct TextToSpeechOutput {
    /// Generated audio data
    pub audio_data: Vec<f32>,
    /// Sample rate of the audio
    pub sample_rate: u32,
    /// Duration in seconds
    pub duration: f64,
    /// Audio format
    pub format: AudioFormat,
    /// Voice used for synthesis
    pub voice: String,
    /// Text that was synthesized
    pub text: String,
    /// Phoneme sequence (if available)
    pub phonemes: Option<Vec<String>>,
    /// Timing information for phonemes
    pub phoneme_timings: Option<Vec<PhonemeTimings>>,
    /// Prosody information
    pub prosody_info: Option<ProsodyInfo>,
}

/// Timing information for phonemes
#[derive(Clone, Debug, Serialize, Deserialize)]
pub struct PhonemeTimings {
    /// Phoneme symbol
    pub phoneme: String,
    /// Start time in seconds
    pub start_time: f64,
    /// End time in seconds
    pub end_time: f64,
    /// Confidence score
    pub confidence: f32,
}

/// Prosody information
#[derive(Clone, Debug, Serialize, Deserialize)]
pub struct ProsodyInfo {
    /// Average pitch
    pub avg_pitch: f32,
    /// Pitch range
    pub pitch_range: f32,
    /// Speaking rate (words per minute)
    pub speaking_rate: f32,
    /// Pause locations
    pub pauses: Vec<PauseInfo>,
    /// Emphasis locations
    pub emphasis: Vec<EmphasisInfo>,
}

/// Pause information
#[derive(Clone, Debug, Serialize, Deserialize)]
pub struct PauseInfo {
    /// Start time in seconds
    pub start_time: f64,
    /// Duration in seconds
    pub duration: f64,
    /// Pause type
    pub pause_type: PauseType,
}

/// Types of pauses
#[derive(Clone, Debug, Serialize, Deserialize)]
pub enum PauseType {
    /// Sentence boundary
    Sentence,
    /// Phrase boundary
    Phrase,
    /// Comma pause
    Comma,
    /// Breath pause
    Breath,
    /// Emphasis pause
    Emphasis,
}

/// Emphasis information
#[derive(Clone, Debug, Serialize, Deserialize)]
pub struct EmphasisInfo {
    /// Start time in seconds
    pub start_time: f64,
    /// End time in seconds
    pub end_time: f64,
    /// Emphasis intensity
    pub intensity: f32,
    /// Emphasis type
    pub emphasis_type: EmphasisType,
}

/// Types of emphasis
#[derive(Clone, Debug, Serialize, Deserialize)]
pub enum EmphasisType {
    /// Stress emphasis
    Stress,
    /// Pitch emphasis
    Pitch,
    /// Volume emphasis
    Volume,
    /// Duration emphasis
    Duration,
}

/// Text-to-speech pipeline implementation
pub struct TextToSpeechPipeline<M, T>
where
    M: Model + Clone + Send + Sync + 'static,
    T: Tokenizer + Clone + Send + Sync + 'static,
{
    base: BasePipeline<M, T>,
    config: TextToSpeechConfig,
    available_voices: Vec<String>,
    phoneme_converter: Option<PhonemeConverter>,
    prosody_analyzer: Option<ProsodyAnalyzer>,
}

impl<M, T> TextToSpeechPipeline<M, T>
where
    M: Model<Input = Tensor, Output = Tensor> + Clone + Send + Sync + 'static,
    T: Tokenizer + Clone + Send + Sync + 'static,
{
    /// Create a new text-to-speech pipeline
    pub fn new(model: M, tokenizer: T) -> Result<Self> {
        let base = BasePipeline::new(model, tokenizer);
        let config = TextToSpeechConfig::default();
        let available_voices = Self::get_available_voices();

        Ok(Self {
            base,
            config,
            available_voices,
            phoneme_converter: None,
            prosody_analyzer: None,
        })
    }

    /// Set configuration
    pub fn with_config(mut self, config: TextToSpeechConfig) -> Self {
        self.config = config;
        self
    }

    /// Set voice
    pub fn with_voice(mut self, voice: String) -> Self {
        self.config.voice = voice;
        self
    }

    /// Set speaking rate
    pub fn with_speaking_rate(mut self, rate: f32) -> Self {
        self.config.speaking_rate = rate.clamp(0.5, 2.0);
        self
    }

    /// Set pitch
    pub fn with_pitch(mut self, pitch: f32) -> Self {
        self.config.pitch = pitch.clamp(0.5, 2.0);
        self
    }

    /// Set volume
    pub fn with_volume(mut self, volume: f32) -> Self {
        self.config.volume = volume.clamp(0.0, 1.0);
        self
    }

    /// Set output format
    pub fn with_output_format(mut self, format: AudioFormat) -> Self {
        self.config.output_format = format;
        self
    }

    /// Enable prosody control
    pub fn with_prosody_control(mut self, enable: bool) -> Self {
        self.config.prosody_control = enable;
        if enable && self.prosody_analyzer.is_none() {
            self.prosody_analyzer = Some(ProsodyAnalyzer::new());
        }
        self
    }

    /// Enable emotion control
    pub fn with_emotion_control(mut self, enable: bool) -> Self {
        self.config.emotion_control = enable;
        self
    }

    /// Set target emotion
    pub fn with_target_emotion(mut self, emotion: String) -> Self {
        self.config.target_emotion = Some(emotion);
        self.config.emotion_control = true;
        self
    }

    /// Get available voices
    pub fn get_available_voices() -> Vec<String> {
        vec![
            "default".to_string(),
            "male-neutral".to_string(),
            "female-neutral".to_string(),
            "male-young".to_string(),
            "female-young".to_string(),
            "male-elderly".to_string(),
            "female-elderly".to_string(),
            "child".to_string(),
            "narrator".to_string(),
            "robot".to_string(),
        ]
    }

    /// Get supported emotions
    pub fn get_supported_emotions() -> Vec<String> {
        vec![
            "neutral".to_string(),
            "happy".to_string(),
            "sad".to_string(),
            "angry".to_string(),
            "excited".to_string(),
            "calm".to_string(),
            "surprised".to_string(),
            "fearful".to_string(),
            "disgusted".to_string(),
            "confident".to_string(),
            "whispering".to_string(),
            "shouting".to_string(),
        ]
    }

    /// Synthesize text to speech
    pub fn synthesize(&self, input: TextToSpeechInput) -> Result<TextToSpeechOutput> {
        // Validate input
        if input.text.is_empty() {
            return Err(TrustformersError::invalid_input_simple(
                "Text input cannot be empty. Expected: non-empty text string, received: empty string".to_string()
            ));
        }

        // Get effective configuration
        let voice = input.voice.unwrap_or_else(|| self.config.voice.clone());
        let speaking_rate = input.speaking_rate.unwrap_or(self.config.speaking_rate);
        let pitch = input.pitch.unwrap_or(self.config.pitch);
        let volume = input.volume.unwrap_or(self.config.volume);

        // Validate voice
        if !self.available_voices.contains(&voice) {
            return Err(TrustformersError::invalid_input_simple(
                format!("Voice '{}' is not available. Parameter: voice, Expected: one of {:?}, Received: {}", voice, self.available_voices, voice)
            ));
        }

        // Preprocess text
        let processed_text = self.preprocess_text(&input.text)?;

        // Tokenize text
        let tokenized = self.base.tokenizer.encode(&processed_text)?;

        // Convert to tensor (converting u32 to f32)
        let input_ids_f32: Vec<f32> = tokenized.input_ids.iter().map(|&x| x as f32).collect();
        let input_tensor = Tensor::from_vec(input_ids_f32, &[1, tokenized.input_ids.len()])?;

        // Run model inference
        let output = self.base.model.forward(input_tensor)?;

        // Convert model output to audio
        let audio_data = self.tensor_to_audio(&output, &voice, speaking_rate, pitch, volume)?;

        // Calculate duration
        let duration = audio_data.len() as f64 / self.config.sample_rate as f64;

        // Generate phonemes if converter is available
        let phonemes = if let Some(converter) = &self.phoneme_converter {
            Some(converter.text_to_phonemes(&processed_text)?)
        } else {
            None
        };

        // Generate phoneme timings
        let phoneme_timings = if let Some(phonemes) = &phonemes {
            Some(self.generate_phoneme_timings(phonemes, duration)?)
        } else {
            None
        };

        // Analyze prosody if enabled
        let prosody_info = if self.config.prosody_control {
            if let Some(analyzer) = &self.prosody_analyzer {
                Some(analyzer.analyze(&processed_text, &audio_data, self.config.sample_rate)?)
            } else {
                None
            }
        } else {
            None
        };

        Ok(TextToSpeechOutput {
            audio_data,
            sample_rate: self.config.sample_rate,
            duration,
            format: self.config.output_format.clone(),
            voice,
            text: processed_text,
            phonemes,
            phoneme_timings,
            prosody_info,
        })
    }

    /// Preprocess text for TTS
    fn preprocess_text(&self, text: &str) -> Result<String> {
        let mut processed = text.to_string();

        // Normalize whitespace
        processed = processed.split_whitespace().collect::<Vec<_>>().join(" ");

        // Expand abbreviations
        processed = self.expand_abbreviations(&processed);

        // Normalize numbers
        processed = self.normalize_numbers(&processed);

        // Handle punctuation
        processed = self.normalize_punctuation(&processed);

        Ok(processed)
    }

    /// Expand common abbreviations
    fn expand_abbreviations(&self, text: &str) -> String {
        let abbreviations = [
            ("Dr.", "Doctor"),
            ("Mr.", "Mister"),
            ("Mrs.", "Missus"),
            ("Ms.", "Miss"),
            ("Prof.", "Professor"),
            ("St.", "Street"),
            ("Ave.", "Avenue"),
            ("Blvd.", "Boulevard"),
            ("etc.", "et cetera"),
            ("vs.", "versus"),
            ("Inc.", "Incorporated"),
            ("Corp.", "Corporation"),
            ("Ltd.", "Limited"),
            ("Co.", "Company"),
            ("USA", "United States of America"),
            ("UK", "United Kingdom"),
            ("CEO", "Chief Executive Officer"),
            ("CTO", "Chief Technology Officer"),
            ("CFO", "Chief Financial Officer"),
            ("AI", "Artificial Intelligence"),
            ("ML", "Machine Learning"),
            ("TTS", "Text To Speech"),
            ("API", "Application Programming Interface"),
        ];

        let mut result = text.to_string();
        for (abbr, expansion) in &abbreviations {
            result = result.replace(abbr, expansion);
        }
        result
    }

    /// Normalize numbers to words
    fn normalize_numbers(&self, text: &str) -> String {
        // This is a simplified number normalization
        // In a real implementation, you would use a proper number-to-words library
        let number_words = [
            ("0", "zero"),
            ("1", "one"),
            ("2", "two"),
            ("3", "three"),
            ("4", "four"),
            ("5", "five"),
            ("6", "six"),
            ("7", "seven"),
            ("8", "eight"),
            ("9", "nine"),
            ("10", "ten"),
            ("11", "eleven"),
            ("12", "twelve"),
            ("13", "thirteen"),
            ("14", "fourteen"),
            ("15", "fifteen"),
            ("16", "sixteen"),
            ("17", "seventeen"),
            ("18", "eighteen"),
            ("19", "nineteen"),
            ("20", "twenty"),
        ];

        let mut result = text.to_string();
        for (num, word) in &number_words {
            result = result.replace(&format!(" {} ", num), &format!(" {} ", word));
        }
        result
    }

    /// Normalize punctuation for speech
    fn normalize_punctuation(&self, text: &str) -> String {
        text.replace("...", " pause ")
            .replace(".", " period ")
            .replace("!", " exclamation ")
            .replace("?", " question ")
            .replace(",", " comma ")
            .replace(";", " semicolon ")
            .replace(":", " colon ")
            .replace("-", " dash ")
            .replace("(", " open parenthesis ")
            .replace(")", " close parenthesis ")
            .replace("\"", " quote ")
            .replace("'", " apostrophe ")
    }

    /// Convert tensor output to audio samples
    fn tensor_to_audio(
        &self,
        tensor: &Tensor,
        voice: &str,
        speaking_rate: f32,
        pitch: f32,
        volume: f32,
    ) -> Result<Vec<f32>> {
        // This is a placeholder implementation
        // In a real TTS system, this would involve:
        // 1. Mel-spectrogram generation from model output
        // 2. Vocoder to convert mel-spectrogram to audio
        // 3. Post-processing for voice characteristics

        let tensor_data = tensor.data()?;
        let audio_length = (tensor_data.len() as f32 * speaking_rate) as usize;
        let mut audio_data = Vec::with_capacity(audio_length);

        // Generate synthetic audio based on tensor data
        for i in 0..audio_length {
            let t = i as f32 / self.config.sample_rate as f32;
            let tensor_index = (i * tensor_data.len() / audio_length).min(tensor_data.len() - 1);
            let base_freq = 220.0 * pitch; // Base frequency modified by pitch
            let amplitude = tensor_data[tensor_index] * volume;

            // Generate a simple sine wave with some harmonics
            let fundamental = (2.0 * std::f32::consts::PI * base_freq * t).sin();
            let harmonic2 = 0.5 * (2.0 * std::f32::consts::PI * base_freq * 2.0 * t).sin();
            let harmonic3 = 0.25 * (2.0 * std::f32::consts::PI * base_freq * 3.0 * t).sin();

            let sample = amplitude * (fundamental + harmonic2 + harmonic3);
            audio_data.push(sample);
        }

        // Apply voice characteristics
        self.apply_voice_characteristics(&mut audio_data, voice);

        Ok(audio_data)
    }

    /// Apply voice-specific characteristics
    fn apply_voice_characteristics(&self, audio_data: &mut [f32], voice: &str) {
        match voice {
            "male-neutral" => {
                // Lower pitch and add some resonance
                for sample in audio_data.iter_mut() {
                    *sample *= 0.9;
                }
            },
            "female-neutral" => {
                // Higher pitch and brighter tone
                for sample in audio_data.iter_mut() {
                    *sample *= 1.1;
                }
            },
            "child" => {
                // Much higher pitch and lighter tone
                for sample in audio_data.iter_mut() {
                    *sample *= 1.3;
                }
            },
            "elderly" => {
                // Slightly lower pitch with some tremolo
                for (i, sample) in audio_data.iter_mut().enumerate() {
                    let tremolo = 1.0 + 0.1 * (i as f32 * 0.01).sin();
                    *sample *= 0.8 * tremolo;
                }
            },
            "robot" => {
                // Robotic voice with digital artifacts
                for sample in audio_data.iter_mut() {
                    *sample = (*sample * 10.0).round() / 10.0; // Quantize
                }
            },
            _ => {
                // Default voice - no modifications
            },
        }
    }

    /// Generate phoneme timings
    fn generate_phoneme_timings(
        &self,
        phonemes: &[String],
        total_duration: f64,
    ) -> Result<Vec<PhonemeTimings>> {
        let mut timings = Vec::new();
        let avg_duration = total_duration / phonemes.len() as f64;

        for (i, phoneme) in phonemes.iter().enumerate() {
            let start_time = i as f64 * avg_duration;
            let end_time = start_time + avg_duration;

            timings.push(PhonemeTimings {
                phoneme: phoneme.clone(),
                start_time,
                end_time,
                confidence: 0.8, // Placeholder confidence
            });
        }

        Ok(timings)
    }
}

impl<M, T> Pipeline for TextToSpeechPipeline<M, T>
where
    M: Model<Input = Tensor, Output = Tensor> + Clone + Send + Sync + 'static,
    T: Tokenizer + Clone + Send + Sync + 'static,
{
    type Input = TextToSpeechInput;
    type Output = TextToSpeechOutput;

    fn __call__(&self, input: Self::Input) -> Result<Self::Output> {
        self.synthesize(input)
    }
}

/// Phoneme converter for text-to-phoneme conversion
pub struct PhonemeConverter {
    phoneme_dict: std::collections::HashMap<String, Vec<String>>,
}

impl Default for PhonemeConverter {
    fn default() -> Self {
        Self::new()
    }
}

impl PhonemeConverter {
    pub fn new() -> Self {
        let mut phoneme_dict = std::collections::HashMap::new();

        // Add some basic phoneme mappings
        phoneme_dict.insert(
            "hello".to_string(),
            vec![
                "h".to_string(),
                "ə".to_string(),
                "ˈl".to_string(),
                "oʊ".to_string(),
            ],
        );
        phoneme_dict.insert(
            "world".to_string(),
            vec![
                "w".to_string(),
                "ɜr".to_string(),
                "l".to_string(),
                "d".to_string(),
            ],
        );
        phoneme_dict.insert("the".to_string(), vec!["ð".to_string(), "ə".to_string()]);
        phoneme_dict.insert("a".to_string(), vec!["ə".to_string()]);
        phoneme_dict.insert("an".to_string(), vec!["æ".to_string(), "n".to_string()]);

        Self { phoneme_dict }
    }

    pub fn text_to_phonemes(&self, text: &str) -> Result<Vec<String>> {
        let mut phonemes = Vec::new();

        for word in text.split_whitespace() {
            let clean_word =
                word.to_lowercase().trim_matches(|c: char| !c.is_alphabetic()).to_string();

            if let Some(word_phonemes) = self.phoneme_dict.get(&clean_word) {
                phonemes.extend(word_phonemes.clone());
            } else {
                // Fallback: convert each character to a phoneme
                for ch in clean_word.chars() {
                    phonemes.push(ch.to_string());
                }
            }
        }

        Ok(phonemes)
    }
}

/// Prosody analyzer for speech characteristics
pub struct ProsodyAnalyzer;

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

    pub fn analyze(
        &self,
        _text: &str,
        audio_data: &[f32],
        sample_rate: u32,
    ) -> Result<ProsodyInfo> {
        // Analyze pitch
        let avg_pitch = self.calculate_average_pitch(audio_data, sample_rate);
        let pitch_range = self.calculate_pitch_range(audio_data, sample_rate);

        // Analyze speaking rate (simplified)
        let speaking_rate = 150.0; // Words per minute (placeholder)

        // Detect pauses
        let pauses = self.detect_pauses(audio_data, sample_rate)?;

        // Detect emphasis
        let emphasis = self.detect_emphasis(audio_data, sample_rate)?;

        Ok(ProsodyInfo {
            avg_pitch,
            pitch_range,
            speaking_rate,
            pauses,
            emphasis,
        })
    }

    fn calculate_average_pitch(&self, audio_data: &[f32], _sample_rate: u32) -> f32 {
        // Simplified pitch calculation
        let mut sum = 0.0;
        for sample in audio_data {
            sum += sample.abs();
        }
        sum / audio_data.len() as f32 * 440.0 // Convert to Hz (placeholder)
    }

    fn calculate_pitch_range(&self, audio_data: &[f32], _sample_rate: u32) -> f32 {
        let min_val = audio_data.iter().fold(f32::INFINITY, |a, &b| a.min(b));
        let max_val = audio_data.iter().fold(f32::NEG_INFINITY, |a, &b| a.max(b));
        (max_val - min_val) * 100.0 // Convert to Hz range (placeholder)
    }

    fn detect_pauses(&self, audio_data: &[f32], sample_rate: u32) -> Result<Vec<PauseInfo>> {
        let mut pauses = Vec::new();
        let silence_threshold = 0.01;
        let min_pause_duration = 0.1; // 100ms

        let mut in_pause = false;
        let mut pause_start = 0.0;

        for (i, &sample) in audio_data.iter().enumerate() {
            let time = i as f64 / sample_rate as f64;

            if sample.abs() < silence_threshold {
                if !in_pause {
                    pause_start = time;
                    in_pause = true;
                }
            } else if in_pause {
                let duration = time - pause_start;
                if duration >= min_pause_duration {
                    pauses.push(PauseInfo {
                        start_time: pause_start,
                        duration,
                        pause_type: PauseType::Phrase, // Simplified classification
                    });
                }
                in_pause = false;
            }
        }

        Ok(pauses)
    }

    fn detect_emphasis(&self, audio_data: &[f32], sample_rate: u32) -> Result<Vec<EmphasisInfo>> {
        let mut emphasis = Vec::new();
        let emphasis_threshold = 0.5;
        let window_size = sample_rate as usize / 10; // 100ms window

        for (i, window) in audio_data.windows(window_size).enumerate() {
            let avg_amplitude = window.iter().map(|&x| x.abs()).sum::<f32>() / window.len() as f32;

            if avg_amplitude > emphasis_threshold {
                let start_time = i as f64 / sample_rate as f64 * window_size as f64;
                let end_time = start_time + window_size as f64 / sample_rate as f64;

                emphasis.push(EmphasisInfo {
                    start_time,
                    end_time,
                    intensity: avg_amplitude,
                    emphasis_type: EmphasisType::Volume,
                });
            }
        }

        Ok(emphasis)
    }
}

impl Default for ProsodyAnalyzer {
    fn default() -> Self {
        Self::new()
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::core::traits::{Model, TokenizedInput, Tokenizer};
    use crate::AutoConfig;
    use std::collections::HashMap;
    use trustformers_core::Tensor;

    #[derive(Clone)]
    struct MockModel {
        config: AutoConfig,
    }

    impl MockModel {
        fn new() -> Self {
            MockModel {
                config: {
                    // Try each available config in order
                    #[cfg(feature = "bert")]
                    {
                        AutoConfig::Bert(Default::default())
                    }
                    #[cfg(all(not(feature = "bert"), feature = "roberta"))]
                    {
                        AutoConfig::Roberta(Default::default())
                    }
                    #[cfg(all(not(feature = "bert"), not(feature = "roberta"), feature = "gpt2"))]
                    {
                        AutoConfig::Gpt2(Default::default())
                    }
                    #[cfg(all(
                        not(feature = "bert"),
                        not(feature = "roberta"),
                        not(feature = "gpt2"),
                        feature = "gpt_neo"
                    ))]
                    {
                        AutoConfig::GptNeo(Default::default())
                    }
                    #[cfg(all(
                        not(feature = "bert"),
                        not(feature = "roberta"),
                        not(feature = "gpt2"),
                        not(feature = "gpt_neo"),
                        feature = "gpt_j"
                    ))]
                    {
                        AutoConfig::GptJ(Default::default())
                    }
                    #[cfg(all(
                        not(feature = "bert"),
                        not(feature = "roberta"),
                        not(feature = "gpt2"),
                        not(feature = "gpt_neo"),
                        not(feature = "gpt_j"),
                        feature = "t5"
                    ))]
                    {
                        AutoConfig::T5(Default::default())
                    }
                    #[cfg(all(
                        not(feature = "bert"),
                        not(feature = "roberta"),
                        not(feature = "gpt2"),
                        not(feature = "gpt_neo"),
                        not(feature = "gpt_j"),
                        not(feature = "t5"),
                        feature = "albert"
                    ))]
                    {
                        AutoConfig::Albert(Default::default())
                    }
                    #[cfg(not(any(
                        feature = "bert",
                        feature = "roberta",
                        feature = "gpt2",
                        feature = "gpt_neo",
                        feature = "gpt_j",
                        feature = "t5",
                        feature = "albert"
                    )))]
                    {
                        // If no model features are enabled, we need to enable at least one for testing
                        // Since this is a test context, we'll compile-fail rather than panic at runtime
                        compile_error!("At least one model feature must be enabled for tests (bert, roberta, gpt2, gpt_neo, gpt_j, t5, or albert)")
                    }
                },
            }
        }
    }

    impl Model for MockModel {
        type Input = Tensor;
        type Output = Tensor;
        type Config = AutoConfig;

        fn forward(&self, _input: Self::Input) -> trustformers_core::errors::Result<Self::Output> {
            // Return a dummy tensor for testing
            Tensor::zeros(&[1, 10])
        }

        fn num_parameters(&self) -> usize {
            1000 // Mock parameter count
        }

        fn load_pretrained(
            &mut self,
            _reader: &mut dyn std::io::Read,
        ) -> trustformers_core::errors::Result<()> {
            Ok(()) // Mock implementation
        }

        fn get_config(&self) -> &Self::Config {
            &self.config
        }
    }

    #[derive(Clone)]
    struct MockTokenizer;

    impl MockTokenizer {
        fn new() -> Self {
            MockTokenizer
        }
    }

    impl Tokenizer for MockTokenizer {
        fn encode(&self, _text: &str) -> trustformers_core::errors::Result<TokenizedInput> {
            Ok(TokenizedInput {
                input_ids: vec![1, 2, 3], // Mock token IDs
                attention_mask: vec![1, 1, 1],
                token_type_ids: Some(vec![0, 0, 0]),
                offset_mapping: None,
                special_tokens_mask: None,
                overflowing_tokens: None,
            })
        }

        fn encode_pair(
            &self,
            _text_a: &str,
            _text_b: &str,
        ) -> trustformers_core::errors::Result<TokenizedInput> {
            Ok(TokenizedInput {
                input_ids: vec![1, 2, 3, 4, 5], // Mock token IDs for pair
                attention_mask: vec![1, 1, 1, 1, 1],
                token_type_ids: Some(vec![0, 0, 0, 1, 1]),
                offset_mapping: None,
                special_tokens_mask: None,
                overflowing_tokens: None,
            })
        }

        fn decode(&self, _token_ids: &[u32]) -> trustformers_core::errors::Result<String> {
            Ok("mock decoded text".to_string())
        }

        fn vocab_size(&self) -> usize {
            1000
        }

        fn get_vocab(&self) -> HashMap<String, u32> {
            let mut vocab = HashMap::new();
            vocab.insert("test".to_string(), 1);
            vocab.insert("mock".to_string(), 2);
            vocab.insert("token".to_string(), 3);
            vocab
        }

        fn token_to_id(&self, token: &str) -> Option<u32> {
            match token {
                "test" => Some(1),
                "mock" => Some(2),
                "token" => Some(3),
                _ => None,
            }
        }

        fn id_to_token(&self, id: u32) -> Option<String> {
            match id {
                1 => Some("test".to_string()),
                2 => Some("mock".to_string()),
                3 => Some("token".to_string()),
                _ => None,
            }
        }
    }

    #[test]
    fn test_text_to_speech_pipeline_creation() {
        let model = MockModel::new();
        let tokenizer = MockTokenizer::new();
        let pipeline = TextToSpeechPipeline::new(model, tokenizer);
        assert!(pipeline.is_ok());
    }

    #[test]
    fn test_text_to_speech_config() {
        let config = TextToSpeechConfig::default();
        assert_eq!(config.voice, "default");
        assert_eq!(config.speaking_rate, 1.0);
        assert_eq!(config.pitch, 1.0);
        assert_eq!(config.volume, 1.0);
    }

    #[test]
    fn test_available_voices() {
        let voices = TextToSpeechPipeline::<MockModel, MockTokenizer>::get_available_voices();
        assert!(voices.contains(&"default".to_string()));
        assert!(voices.contains(&"male-neutral".to_string()));
        assert!(voices.contains(&"female-neutral".to_string()));
    }

    #[test]
    fn test_supported_emotions() {
        let emotions = TextToSpeechPipeline::<MockModel, MockTokenizer>::get_supported_emotions();
        assert!(emotions.contains(&"neutral".to_string()));
        assert!(emotions.contains(&"happy".to_string()));
        assert!(emotions.contains(&"sad".to_string()));
    }

    #[test]
    fn test_phoneme_converter() {
        let converter = PhonemeConverter::new();
        let phonemes = converter.text_to_phonemes("hello world").unwrap();
        assert!(!phonemes.is_empty());
    }

    #[test]
    fn test_prosody_analyzer() {
        let analyzer = ProsodyAnalyzer::new();
        let audio_data = vec![0.1, 0.2, 0.0, 0.0, 0.3, 0.4];
        let prosody = analyzer.analyze("test", &audio_data, 22050).unwrap();
        assert!(prosody.avg_pitch > 0.0);
    }

    #[test]
    fn test_text_preprocessing() {
        let model = MockModel::new();
        let tokenizer = MockTokenizer::new();
        let pipeline = TextToSpeechPipeline::new(model, tokenizer).unwrap();

        let processed = pipeline.preprocess_text("Dr. Smith said 5 words.").unwrap();
        assert!(processed.contains("Doctor"));
        assert!(processed.contains("five"));
    }

    #[test]
    fn test_pipeline_configuration() {
        let model = MockModel::new();
        let tokenizer = MockTokenizer::new();
        let pipeline = TextToSpeechPipeline::new(model, tokenizer)
            .unwrap()
            .with_voice("female-neutral".to_string())
            .with_speaking_rate(1.5)
            .with_pitch(1.2)
            .with_volume(0.8);

        assert_eq!(pipeline.config.voice, "female-neutral");
        assert_eq!(pipeline.config.speaking_rate, 1.5);
        assert_eq!(pipeline.config.pitch, 1.2);
        assert_eq!(pipeline.config.volume, 0.8);
    }
}
