use std::path::{Path, PathBuf};
use std::fs;

use hf_hub::api::sync::Api;
use std::borrow::Cow;

use ndarray::{ArrayD, Array2, Array3, Array4, IxDyn};
use ort::session::Session;
use ort::value::{Value, DynValue};
use tokenizers::Tokenizer;

use crate::audio;

pub const SAMPLE_RATE: u32 = 24_000;
const START_SPEECH_TOKEN: i64 = 6561;
const STOP_SPEECH_TOKEN: i64 = 6562;
const SILENCE_TOKEN: i64 = 4299;
const NUM_LAYERS: usize = 24;
const NUM_KV_HEADS: usize = 16;
const HEAD_DIM: usize = 64;

const MODEL_ID: &str = "ResembleAI/chatterbox-turbo-ONNX";
const DEFAULT_MODEL_DIR: &str = "models/chatterbox-turbo";

#[derive(Debug)]
pub enum TtsError {
    ModelLoad(String),
    Inference(String),
    Tokenizer(String),
    Audio(audio::AudioError),
    Io(std::io::Error),
    VoiceRequired,
}

impl std::fmt::Display for TtsError {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        match self {
            TtsError::ModelLoad(msg) => write!(f, "model load error: {msg}"),
            TtsError::Inference(msg) => write!(f, "inference error: {msg}"),
            TtsError::Tokenizer(msg) => write!(f, "tokenizer error: {msg}"),
            TtsError::Audio(err) => write!(f, "audio error: {err}"),
            TtsError::Io(err) => write!(f, "io error: {err}"),
            TtsError::VoiceRequired => write!(f, "voice reference audio is required for chatterbox-turbo"),
        }
    }
}

impl std::error::Error for TtsError {}

impl From<audio::AudioError> for TtsError {
    fn from(value: audio::AudioError) -> Self {
        TtsError::Audio(value)
    }
}

impl From<std::io::Error> for TtsError {
    fn from(value: std::io::Error) -> Self {
        TtsError::Io(value)
    }
}

impl From<ort::Error> for TtsError {
    fn from(value: ort::Error) -> Self {
        TtsError::ModelLoad(value.to_string())
    }
}

pub struct ChatterboxTTS {
    speech_encoder: Session,
    embed_tokens: Session,
    language_model: Session,
    conditional_decoder: Session,
    tokenizer: Tokenizer,
}

struct VoiceCondition {
    audio_features: ArrayD<f32>,
    prompt_tokens: ArrayD<i64>,
    speaker_embeddings: ArrayD<f32>,
    speaker_features: ArrayD<f32>,
}

fn extract_f32_tensor(value: &Value) -> Result<ArrayD<f32>, TtsError> {
    let (shape, data) = value
        .try_extract_tensor::<f32>()
        .map_err(|e| TtsError::Inference(e.to_string()))?;

    let dims: Vec<usize> = shape.iter().map(|&d| d as usize).collect();
    ArrayD::from_shape_vec(IxDyn(&dims), data.to_vec())
        .map_err(|e| TtsError::Inference(e.to_string()))
}

fn extract_i64_tensor(value: &Value) -> Result<ArrayD<i64>, TtsError> {
    let (shape, data) = value
        .try_extract_tensor::<i64>()
        .map_err(|e| TtsError::Inference(e.to_string()))?;

    let dims: Vec<usize> = shape.iter().map(|&d| d as usize).collect();
    ArrayD::from_shape_vec(IxDyn(&dims), data.to_vec())
        .map_err(|e| TtsError::Inference(e.to_string()))
}

impl ChatterboxTTS {
    pub fn from_pretrained(model_dir: Option<&str>) -> Result<Self, TtsError> {
        let model_path = PathBuf::from(model_dir.unwrap_or(DEFAULT_MODEL_DIR));

        if !model_path.exists() {
            download_models(&model_path)?;
        }

        Self::load_from_path(&model_path)
    }

    pub fn load_from_path(model_dir: &Path) -> Result<Self, TtsError> {
        let speech_encoder = Session::builder()?
            .with_intra_threads(4)?
            .commit_from_file(model_dir.join("speech_encoder.onnx"))?;

        let embed_tokens = Session::builder()?
            .with_intra_threads(4)?
            .commit_from_file(model_dir.join("embed_tokens.onnx"))?;

        let language_model = Session::builder()?
            .with_intra_threads(4)?
            .commit_from_file(model_dir.join("language_model.onnx"))?;

        let conditional_decoder = Session::builder()?
            .with_intra_threads(4)?
            .commit_from_file(model_dir.join("conditional_decoder.onnx"))?;

        let tokenizer_path = model_dir.join("tokenizer.json");
        let tokenizer = Tokenizer::from_file(&tokenizer_path)
            .map_err(|e| TtsError::Tokenizer(e.to_string()))?;

        Ok(Self {
            speech_encoder,
            embed_tokens,
            language_model,
            conditional_decoder,
            tokenizer,
        })
    }

    pub fn generate_tts(&mut self, _text: &str) -> Result<Vec<f32>, TtsError> {
        // Chatterbox TTS requires voice reference audio
        Err(TtsError::VoiceRequired)
    }

    pub fn generate_tts_with_voice(
        &mut self,
        text: &str,
        sample_audio_path: &Path,
    ) -> Result<Vec<f32>, TtsError> {
        let audio = audio::to_16k_mono_from_path(sample_audio_path)?;
        let resampled = resample_to_24k(&audio.samples, audio.sample_rate);
        self.generate_tts_with_samples(text, &resampled, SAMPLE_RATE)
    }

    pub fn generate_tts_with_samples(
        &mut self,
        text: &str,
        samples: &[f32],
        sample_rate: u32,
    ) -> Result<Vec<f32>, TtsError> {
        let resampled = if sample_rate != SAMPLE_RATE {
            resample_to_24k(samples, sample_rate)
        } else {
            samples.to_vec()
        };

        // 1. Encode reference audio
        let voice_condition = self.encode_voice(&resampled)?;

        // 2. Tokenize text
        let encoding = self
            .tokenizer
            .encode(text, true)
            .map_err(|e| TtsError::Tokenizer(e.to_string()))?;

        let text_input_ids: Vec<i64> = encoding.get_ids().iter().map(|&id| id as i64).collect();

        // 3. Generate speech tokens
        let generated_tokens = self.generate_speech_tokens(
            &text_input_ids,
            &voice_condition.audio_features,
        )?;

        // 4. Prepare final speech tokens: prompt_tokens + generated + silence
        let prompt_tokens: Vec<i64> = voice_condition.prompt_tokens.iter().copied().collect();
        let silence_tokens = vec![SILENCE_TOKEN; 3];

        let mut final_tokens = Vec::with_capacity(
            prompt_tokens.len() + generated_tokens.len() + silence_tokens.len()
        );
        final_tokens.extend_from_slice(&prompt_tokens);
        final_tokens.extend_from_slice(&generated_tokens);
        final_tokens.extend_from_slice(&silence_tokens);

        // 5. Decode to audio
        let audio_samples = self.decode_speech_tokens(
            &final_tokens,
            &voice_condition.speaker_embeddings,
            &voice_condition.speaker_features,
        )?;

        Ok(audio_samples)
    }

    fn encode_voice(&mut self, samples: &[f32]) -> Result<VoiceCondition, TtsError> {
        let audio_arr = Array2::from_shape_vec((1, samples.len()), samples.to_vec())
            .map_err(|e| TtsError::Inference(e.to_string()))?;

        let audio_tensor = Value::from_array(audio_arr)?;

        let outputs = self.speech_encoder.run(ort::inputs!["audio_values" => audio_tensor])?;

        // Order: audio_features, audio_tokens (prompt_token), speaker_embeddings, speaker_features
        let audio_features = extract_f32_tensor(&outputs[0])?;
        let prompt_tokens = extract_i64_tensor(&outputs[1])?;
        let speaker_embeddings = extract_f32_tensor(&outputs[2])?;
        let speaker_features = extract_f32_tensor(&outputs[3])?;

        Ok(VoiceCondition {
            audio_features,
            prompt_tokens,
            speaker_embeddings,
            speaker_features,
        })
    }

    fn generate_speech_tokens(
        &mut self,
        text_input_ids: &[i64],
        audio_features: &ArrayD<f32>,
    ) -> Result<Vec<i64>, TtsError> {
        let max_new_tokens: usize = 1024;
        let repetition_penalty: f32 = 1.2;

        // Start with START_SPEECH_TOKEN
        let mut generate_tokens: Vec<i64> = vec![START_SPEECH_TOKEN];

        // Initialize empty KV cache (seq_len = 0)
        let mut past_key_values = self.init_kv_cache(0)?;

        let mut first_iteration = true;
        let mut total_seq_len: usize = 0;

        for _ in 0..max_new_tokens {
            // Get embeddings for current input_ids
            let current_input_ids = if first_iteration {
                // First iteration: use text input_ids
                text_input_ids.to_vec()
            } else {
                // Subsequent iterations: use last generated token
                vec![*generate_tokens.last().unwrap()]
            };

            let input_ids_arr = Array2::from_shape_vec(
                (1, current_input_ids.len()),
                current_input_ids
            ).map_err(|e| TtsError::Inference(e.to_string()))?;

            let input_ids_tensor = Value::from_array(input_ids_arr)?;

            let inputs_embeds = {
                let embed_outputs = self.embed_tokens.run(ort::inputs![input_ids_tensor])?;
                extract_f32_tensor(&embed_outputs[0])?
            };

            // On first iteration, concatenate audio features with text embeddings
            let inputs_embeds = if first_iteration {
                let audio_feat_3d = audio_features.view()
                    .into_dimensionality::<ndarray::Ix3>()
                    .map_err(|e| TtsError::Inference(e.to_string()))?;
                let text_emb_3d = inputs_embeds.view()
                    .into_dimensionality::<ndarray::Ix3>()
                    .map_err(|e| TtsError::Inference(e.to_string()))?;

                ndarray::concatenate(ndarray::Axis(1), &[audio_feat_3d, text_emb_3d])
                    .map_err(|e| TtsError::Inference(e.to_string()))?
            } else {
                inputs_embeds.view()
                    .into_dimensionality::<ndarray::Ix3>()
                    .map_err(|e| TtsError::Inference(e.to_string()))?
                    .to_owned()
            };

            let seq_len = inputs_embeds.shape()[1];

            // Set up attention mask and position ids
            let (attention_mask, position_ids) = if first_iteration {
                total_seq_len = seq_len;
                let attention_mask: Array2<i64> = Array2::ones((1, seq_len));
                let position_ids = Array2::from_shape_fn((1, seq_len), |(_, j)| j as i64);
                (attention_mask, position_ids)
            } else {
                total_seq_len += 1;
                let attention_mask: Array2<i64> = Array2::ones((1, total_seq_len));
                let position_ids = Array2::from_shape_vec(
                    (1, 1),
                    vec![(total_seq_len - 1) as i64]
                ).map_err(|e| TtsError::Inference(e.to_string()))?;
                (attention_mask, position_ids)
            };

            // Run language model
            let (logits, new_kv) = self.run_language_model(
                inputs_embeds,
                position_ids,
                attention_mask,
                past_key_values,
            )?;

            past_key_values = new_kv;

            // Get last logits
            let logits_3d = logits.view().into_dimensionality::<ndarray::Ix3>()
                .map_err(|e| TtsError::Inference(e.to_string()))?;
            let last_idx = logits_3d.shape()[1] - 1;

            let mut current_logits: Vec<f32> = logits_3d
                .slice(ndarray::s![0, last_idx, ..])
                .iter()
                .copied()
                .collect();

            // Apply repetition penalty
            apply_repetition_penalty(&mut current_logits, &generate_tokens, repetition_penalty);

            // Get next token
            let next_token = argmax(&current_logits);

            generate_tokens.push(next_token);

            if next_token == STOP_SPEECH_TOKEN {
                break;
            }

            first_iteration = false;
        }

        // Return tokens without START and STOP tokens: [1:-1]
        if generate_tokens.len() > 2 {
            Ok(generate_tokens[1..generate_tokens.len()-1].to_vec())
        } else {
            Ok(Vec::new())
        }
    }

    fn init_kv_cache(&self, seq_len: usize) -> Result<Vec<Array4<f32>>, TtsError> {
        let mut cache = Vec::with_capacity(NUM_LAYERS * 2);
        for _ in 0..NUM_LAYERS {
            let key = Array4::<f32>::zeros((1, NUM_KV_HEADS, seq_len, HEAD_DIM));
            let value = Array4::<f32>::zeros((1, NUM_KV_HEADS, seq_len, HEAD_DIM));
            cache.push(key);
            cache.push(value);
        }
        Ok(cache)
    }

    fn run_language_model(
        &mut self,
        inputs_embeds: Array3<f32>,
        position_ids: Array2<i64>,
        attention_mask: Array2<i64>,
        past_key_values: Vec<Array4<f32>>,
    ) -> Result<(ArrayD<f32>, Vec<Array4<f32>>), TtsError> {
        let mut inputs: Vec<(Cow<str>, DynValue)> = Vec::new();

        inputs.push((Cow::from("inputs_embeds"), Value::from_array(inputs_embeds)?.into_dyn()));
        inputs.push((Cow::from("position_ids"), Value::from_array(position_ids)?.into_dyn()));
        inputs.push((Cow::from("attention_mask"), Value::from_array(attention_mask)?.into_dyn()));

        // Add KV cache inputs
        for layer_idx in 0..NUM_LAYERS {
            let key_name = format!("past_key_values.{}.key", layer_idx);
            let value_name = format!("past_key_values.{}.value", layer_idx);

            let key_tensor = Value::from_array(past_key_values[layer_idx * 2].clone())?.into_dyn();
            let value_tensor = Value::from_array(past_key_values[layer_idx * 2 + 1].clone())?.into_dyn();

            inputs.push((Cow::from(key_name), key_tensor));
            inputs.push((Cow::from(value_name), value_tensor));
        }

        let outputs = self.language_model.run(inputs)?;

        let logits = extract_f32_tensor(&outputs[0])?;

        let mut new_kv = Vec::with_capacity(NUM_LAYERS * 2);
        for layer_idx in 0..NUM_LAYERS {
            let key_idx = 1 + layer_idx * 2;
            let value_idx = 2 + layer_idx * 2;

            let key_arr = extract_f32_tensor(&outputs[key_idx])?;
            let value_arr = extract_f32_tensor(&outputs[value_idx])?;

            let key_4d = key_arr.into_dimensionality::<ndarray::Ix4>()
                .map_err(|e| TtsError::Inference(e.to_string()))?;
            let value_4d = value_arr.into_dimensionality::<ndarray::Ix4>()
                .map_err(|e| TtsError::Inference(e.to_string()))?;

            new_kv.push(key_4d.to_owned());
            new_kv.push(value_4d.to_owned());
        }

        Ok((logits, new_kv))
    }

    fn decode_speech_tokens(
        &mut self,
        speech_tokens: &[i64],
        speaker_embeddings: &ArrayD<f32>,
        speaker_features: &ArrayD<f32>,
    ) -> Result<Vec<f32>, TtsError> {
        if speech_tokens.is_empty() {
            return Ok(Vec::new());
        }

        let tokens_arr = Array2::from_shape_vec((1, speech_tokens.len()), speech_tokens.to_vec())
            .map_err(|e| TtsError::Inference(e.to_string()))?;

        let mut inputs: Vec<(Cow<str>, DynValue)> = Vec::new();
        inputs.push((Cow::from("speech_tokens"), Value::from_array(tokens_arr)?.into_dyn()));
        inputs.push((Cow::from("speaker_embeddings"), Value::from_array(speaker_embeddings.clone())?.into_dyn()));
        inputs.push((Cow::from("speaker_features"), Value::from_array(speaker_features.clone())?.into_dyn()));

        let outputs = self.conditional_decoder.run(inputs)?;

        let waveform = extract_f32_tensor(&outputs[0])?;

        Ok(waveform.iter().copied().collect())
    }
}

fn download_models(target_dir: &Path) -> Result<(), TtsError> {
    fs::create_dir_all(target_dir)?;

    let api = Api::new().map_err(|e| TtsError::ModelLoad(e.to_string()))?;
    let repo = api.model(MODEL_ID.to_string());

    let model_files = [
        "onnx/speech_encoder.onnx",
        "onnx/speech_encoder.onnx_data",
        "onnx/embed_tokens.onnx",
        "onnx/embed_tokens.onnx_data",
        "onnx/language_model.onnx",
        "onnx/language_model.onnx_data",
        "onnx/conditional_decoder.onnx",
        "onnx/conditional_decoder.onnx_data",
        "tokenizer.json",
    ];

    for file in &model_files {
        println!("Downloading {}...", file);
        let downloaded_path = repo.get(file).map_err(|e| TtsError::ModelLoad(e.to_string()))?;

        let filename = Path::new(file).file_name().unwrap();
        let target_path = target_dir.join(filename);

        if !target_path.exists() {
            fs::copy(&downloaded_path, &target_path)?;
        }
    }

    println!("Models downloaded to {:?}", target_dir);
    Ok(())
}

fn resample_to_24k(samples: &[f32], input_rate: u32) -> Vec<f32> {
    if input_rate == SAMPLE_RATE {
        return samples.to_vec();
    }
    if samples.is_empty() {
        return Vec::new();
    }

    let ratio = input_rate as f64 / SAMPLE_RATE as f64;
    let output_len = ((samples.len() as f64) / ratio).ceil() as usize;

    let mut output = Vec::with_capacity(output_len);
    for i in 0..output_len {
        let src_idx = (i as f64 * ratio) as usize;
        let sample = samples.get(src_idx).copied().unwrap_or(0.0);
        output.push(sample);
    }

    output
}

fn apply_repetition_penalty(logits: &mut [f32], generated: &[i64], penalty: f32) {
    for &token in generated {
        if (token as usize) < logits.len() {
            let score = logits[token as usize];
            // Note: opposite of standard - if score < 0, multiply; if > 0, divide
            logits[token as usize] = if score < 0.0 {
                score * penalty
            } else {
                score / penalty
            };
        }
    }
}

fn argmax(logits: &[f32]) -> i64 {
    logits
        .iter()
        .enumerate()
        .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
        .map(|(idx, _)| idx as i64)
        .unwrap_or(0)
}

pub fn save_wav(samples: &[f32], path: &Path) -> Result<(), TtsError> {
    let mut file = fs::File::create(path)?;
    write_wav(&mut file, samples, SAMPLE_RATE)?;
    Ok(())
}

fn write_wav<W: std::io::Write>(writer: &mut W, samples: &[f32], sample_rate: u32) -> Result<(), std::io::Error> {
    let num_samples = samples.len() as u32;
    let num_channels: u16 = 1;
    let bits_per_sample: u16 = 16;
    let byte_rate = sample_rate * num_channels as u32 * bits_per_sample as u32 / 8;
    let block_align = num_channels * bits_per_sample / 8;
    let data_size = num_samples * num_channels as u32 * bits_per_sample as u32 / 8;
    let file_size = 36 + data_size;

    writer.write_all(b"RIFF")?;
    writer.write_all(&file_size.to_le_bytes())?;
    writer.write_all(b"WAVE")?;

    writer.write_all(b"fmt ")?;
    writer.write_all(&16u32.to_le_bytes())?;
    writer.write_all(&1u16.to_le_bytes())?;
    writer.write_all(&num_channels.to_le_bytes())?;
    writer.write_all(&sample_rate.to_le_bytes())?;
    writer.write_all(&byte_rate.to_le_bytes())?;
    writer.write_all(&block_align.to_le_bytes())?;
    writer.write_all(&bits_per_sample.to_le_bytes())?;

    writer.write_all(b"data")?;
    writer.write_all(&data_size.to_le_bytes())?;

    for &sample in samples {
        let clamped = sample.clamp(-1.0, 1.0);
        let int_sample = (clamped * 32767.0) as i16;
        writer.write_all(&int_sample.to_le_bytes())?;
    }

    Ok(())
}

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

    #[test]
    fn test_argmax() {
        let logits = vec![0.1, 0.5, 0.3, 0.8, 0.2];
        assert_eq!(argmax(&logits), 3);
    }

    #[test]
    fn test_resample_same_rate() {
        let samples = vec![0.1, 0.2, 0.3];
        let resampled = resample_to_24k(&samples, SAMPLE_RATE);
        assert_eq!(resampled, samples);
    }

    #[test]
    fn test_repetition_penalty() {
        let mut logits = vec![1.0, 2.0, 3.0, 4.0];
        let generated = vec![1, 3];
        apply_repetition_penalty(&mut logits, &generated, 1.2);
        // score > 0 -> divide
        assert!((logits[1] - 2.0 / 1.2).abs() < 1e-6);
        assert!((logits[3] - 4.0 / 1.2).abs() < 1e-6);
    }
}