//! Speech Tokenizer — ConvNet encoder/decoder with RVQ codebooks.
//!
//! Two sub-components:
//!
//! **Encoder** (voice cloning): converts reference audio waveform to discrete
//! multi-codebook tokens via a causal 1D ConvNet + RVQ.
//!
//! **Decoder** (audio synthesis): reconstructs waveform from discrete codebook
//! indices via embedding lookup + causal 1D ConvNet.
//!
//! The speech tokenizer is a separate model (~682MB) loaded from
//! `Qwen/Qwen3-TTS-Tokenizer-12Hz`.

use candle_core::{Device, Module, Result, Tensor, D};
use candle_nn::{
    conv1d, embedding, linear_no_bias, Conv1d, Conv1dConfig, Embedding, Linear, VarBuilder,
};

use super::config::SpeechTokenizerConfig;

// ---------------------------------------------------------------------------
// Weight-Normalized Conv1d
// ---------------------------------------------------------------------------

/// A 1D convolution with optional weight normalization and activation.
pub struct ConvBlock {
    conv: Conv1d,
    activation: ConvActivation,
}

#[derive(Debug, Clone, Copy)]
pub enum ConvActivation {
    None,
    Elu,
    Tanh,
}

impl ConvBlock {
    pub fn new(
        in_channels: usize,
        out_channels: usize,
        kernel_size: usize,
        stride: usize,
        padding: usize,
        dilation: usize,
        activation: ConvActivation,
        vb: VarBuilder,
    ) -> Result<Self> {
        let config = Conv1dConfig {
            stride,
            padding,
            dilation,
            groups: 1,
        };
        let conv = conv1d(in_channels, out_channels, kernel_size, config, vb.pp("conv"))?;

        Ok(Self { conv, activation })
    }

    pub fn forward(&self, x: &Tensor) -> Result<Tensor> {
        let out = self.conv.forward(x)?;
        match self.activation {
            ConvActivation::None => Ok(out),
            ConvActivation::Elu => elu(&out, 1.0),
            ConvActivation::Tanh => out.tanh(),
        }
    }
}

/// ELU activation: x if x >= 0, alpha * (exp(x) - 1) if x < 0
fn elu(x: &Tensor, alpha: f64) -> Result<Tensor> {
    let zeros = x.zeros_like()?;
    let positive = x.maximum(&zeros)?;
    let negative_mask = x.lt(&zeros)?.to_dtype(x.dtype())?;
    let exp_x = x.exp()?;
    let one = Tensor::ones_like(&exp_x)?;
    let negative = ((exp_x - one)? * alpha)?.broadcast_mul(&negative_mask)?;
    positive + negative
}

// ---------------------------------------------------------------------------
// Residual Unit
// ---------------------------------------------------------------------------

/// Residual convolutional unit with dilated convolutions.
pub struct ResidualUnit {
    conv1: ConvBlock,
    conv2: ConvBlock,
}

impl ResidualUnit {
    pub fn new(
        channels: usize,
        dilation: usize,
        vb: VarBuilder,
    ) -> Result<Self> {
        // Dilated causal conv (kernel=7, dilation varies)
        let padding = (7 - 1) * dilation / 2; // causal-ish padding
        let conv1 = ConvBlock::new(
            channels,
            channels,
            7,
            1,
            padding,
            dilation,
            ConvActivation::Elu,
            vb.pp("block.0"),
        )?;

        // Pointwise conv (kernel=1)
        let conv2 = ConvBlock::new(
            channels,
            channels,
            1,
            1,
            0,
            1,
            ConvActivation::Elu,
            vb.pp("block.1"),
        )?;

        Ok(Self { conv1, conv2 })
    }

    pub fn forward(&self, x: &Tensor) -> Result<Tensor> {
        let residual = x;
        let out = self.conv1.forward(x)?;
        let out = self.conv2.forward(&out)?;
        // Match sequence lengths if needed (causal conv may change length)
        let out_len = out.dim(D::Minus1)?;
        let res_len = residual.dim(D::Minus1)?;
        if out_len != res_len {
            let start = res_len.saturating_sub(out_len);
            let residual = residual.narrow(D::Minus1, start, out_len)?;
            residual + out
        } else {
            residual + out
        }
    }
}

// ---------------------------------------------------------------------------
// Encoder Block
// ---------------------------------------------------------------------------

/// Encoder downsampling block: residual units + strided conv.
pub struct EncoderBlock {
    residual_units: Vec<ResidualUnit>,
    downsample: ConvBlock,
}

impl EncoderBlock {
    pub fn new(
        in_channels: usize,
        out_channels: usize,
        stride: usize,
        num_residuals: usize,
        vb: VarBuilder,
    ) -> Result<Self> {
        let mut residual_units = Vec::with_capacity(num_residuals);
        for i in 0..num_residuals {
            let dilation = 3usize.pow(i as u32); // 1, 3, 9
            let unit = ResidualUnit::new(in_channels, dilation, vb.pp(format!("residuals.{i}")))?;
            residual_units.push(unit);
        }

        // Strided downsampling convolution
        let kernel_size = stride * 2;
        let padding = stride / 2;
        let downsample = ConvBlock::new(
            in_channels,
            out_channels,
            kernel_size,
            stride,
            padding,
            1,
            ConvActivation::Elu,
            vb.pp("downsample"),
        )?;

        Ok(Self {
            residual_units,
            downsample,
        })
    }

    pub fn forward(&self, x: &Tensor) -> Result<Tensor> {
        let mut out = x.clone();
        for unit in &self.residual_units {
            out = unit.forward(&out)?;
        }
        self.downsample.forward(&out)
    }
}

// ---------------------------------------------------------------------------
// Decoder Block
// ---------------------------------------------------------------------------

/// Decoder upsampling block: transposed conv + residual units.
pub struct DecoderBlock {
    upsample: ConvBlock,
    residual_units: Vec<ResidualUnit>,
}

impl DecoderBlock {
    pub fn new(
        in_channels: usize,
        out_channels: usize,
        stride: usize,
        num_residuals: usize,
        vb: VarBuilder,
    ) -> Result<Self> {
        // Strided upsampling (transpose conv simulated by regular conv + padding)
        let kernel_size = stride * 2;
        let padding = stride / 2;
        let upsample = ConvBlock::new(
            in_channels,
            out_channels,
            kernel_size,
            1, // stride=1 for output; upsample via repeat/interpolation
            padding,
            1,
            ConvActivation::Elu,
            vb.pp("upsample"),
        )?;

        let mut residual_units = Vec::with_capacity(num_residuals);
        for i in 0..num_residuals {
            let dilation = 3usize.pow(i as u32);
            let unit =
                ResidualUnit::new(out_channels, dilation, vb.pp(format!("residuals.{i}")))?;
            residual_units.push(unit);
        }

        Ok(Self {
            upsample,
            residual_units,
        })
    }

    pub fn forward(&self, x: &Tensor) -> Result<Tensor> {
        let mut out = self.upsample.forward(x)?;
        for unit in &self.residual_units {
            out = unit.forward(&out)?;
        }
        Ok(out)
    }
}

// ---------------------------------------------------------------------------
// RVQ Codebook
// ---------------------------------------------------------------------------

/// Residual Vector Quantization codebook.
///
/// Contains `num_codebooks` embedding tables, each mapping
/// `codebook_size` indices to `codebook_dim`-dimensional vectors.
pub struct RvqCodebook {
    codebooks: Vec<Embedding>,
    num_codebooks: usize,
    #[allow(dead_code)]
    codebook_dim: usize,
}

impl RvqCodebook {
    pub fn new(config: &SpeechTokenizerConfig, vb: VarBuilder) -> Result<Self> {
        let mut codebooks = Vec::with_capacity(config.num_codebooks);
        for i in 0..config.num_codebooks {
            let cb = embedding(
                config.codebook_size,
                config.codebook_dim,
                vb.pp(format!("codebooks.{i}")),
            )?;
            codebooks.push(cb);
        }

        Ok(Self {
            codebooks,
            num_codebooks: config.num_codebooks,
            codebook_dim: config.codebook_dim,
        })
    }

    /// Look up codebook embeddings for all codebook layers.
    ///
    /// `codes`: [num_codebooks, seq_len] — codebook indices per layer
    /// Returns: [1, codebook_dim, seq_len] — sum of all codebook embeddings
    pub fn decode(&self, codes: &[Vec<u32>], device: &Device) -> Result<Tensor> {
        assert_eq!(codes.len(), self.num_codebooks, "Expected {} codebook layers", self.num_codebooks);

        let seq_len = codes[0].len();
        let mut sum: Option<Tensor> = None;

        for (i, code_layer) in codes.iter().enumerate() {
            assert_eq!(code_layer.len(), seq_len, "Codebook layer {i} length mismatch");

            let indices = Tensor::from_vec(
                code_layer.clone(),
                (1, seq_len),
                device,
            )?;

            // [1, seq_len, codebook_dim]
            let emb = self.codebooks[i].forward(&indices)?;

            sum = Some(match sum {
                Some(prev) => (prev + emb)?,
                None => emb,
            });
        }

        // [1, seq_len, codebook_dim] -> [1, codebook_dim, seq_len]
        let result = sum.unwrap().transpose(1, 2)?;
        Ok(result)
    }

    /// Number of codebooks.
    pub fn num_codebooks(&self) -> usize {
        self.num_codebooks
    }
}

// ---------------------------------------------------------------------------
// Speech Tokenizer (Encoder + Decoder)
// ---------------------------------------------------------------------------

/// The complete speech tokenizer with encoder and decoder.
pub struct SpeechTokenizer {
    /// Encoder: waveform -> latent (for voice cloning).
    encoder_input_conv: ConvBlock,
    encoder_blocks: Vec<EncoderBlock>,
    encoder_output_conv: ConvBlock,

    /// RVQ codebooks for quantization.
    codebook: RvqCodebook,

    /// Decoder: codes -> waveform.
    decoder_input_conv: ConvBlock,
    decoder_blocks: Vec<DecoderBlock>,
    decoder_output_conv: ConvBlock,

    /// Projection from codebook dim to decoder hidden channels.
    decoder_proj: Linear,

    config: SpeechTokenizerConfig,
    device: Device,
}

impl SpeechTokenizer {
    /// Load the speech tokenizer from safetensors.
    pub fn new(config: &SpeechTokenizerConfig, vb: VarBuilder, device: &Device) -> Result<Self> {
        let hidden = config.hidden_channels; // 512

        // ===== Encoder =====
        // Input: [batch, 1, samples] -> [batch, hidden/8, ...]
        let encoder_input_conv = ConvBlock::new(
            1,
            hidden / 8, // 64
            7,
            1,
            3,
            1,
            ConvActivation::Elu,
            vb.pp("encoder.input_conv"),
        )?;

        // Downsampling blocks with increasing channels
        let strides = [8, 5, 4, 3]; // Total downsampling: 8*5*4*3 = 480
        let channels = [hidden / 8, hidden / 4, hidden / 2, hidden]; // 64, 128, 256, 512
        let mut encoder_blocks = Vec::with_capacity(strides.len());
        for (i, (&stride, &out_ch)) in strides.iter().zip(channels.iter().skip(0)).enumerate() {
            let in_ch = if i == 0 { hidden / 8 } else { channels[i - 1] };
            let block = EncoderBlock::new(
                in_ch,
                out_ch,
                stride,
                3, // 3 residual units per block
                vb.pp(format!("encoder.blocks.{i}")),
            )?;
            encoder_blocks.push(block);
        }

        // Encoder output projection to codebook dim
        let encoder_output_conv = ConvBlock::new(
            hidden,
            config.codebook_dim,
            3,
            1,
            1,
            1,
            ConvActivation::None,
            vb.pp("encoder.output_conv"),
        )?;

        // ===== RVQ Codebook =====
        let codebook = RvqCodebook::new(config, vb.pp("quantizer"))?;

        // ===== Decoder =====
        // Projection from codebook dim to decoder hidden
        let decoder_proj = linear_no_bias(
            config.codebook_dim,
            hidden,
            vb.pp("decoder.proj"),
        )?;

        // Input conv
        let decoder_input_conv = ConvBlock::new(
            hidden,
            hidden,
            7,
            1,
            3,
            1,
            ConvActivation::Elu,
            vb.pp("decoder.input_conv"),
        )?;

        // Upsampling blocks (reverse order of encoder)
        let dec_strides = [3, 4, 5, 8];
        let dec_channels = [hidden, hidden / 2, hidden / 4, hidden / 8]; // 512, 256, 128, 64
        let mut decoder_blocks = Vec::with_capacity(dec_strides.len());
        for (i, (&stride, &out_ch)) in dec_strides.iter().zip(dec_channels.iter().skip(0)).enumerate()
        {
            let in_ch = if i == 0 { hidden } else { dec_channels[i - 1] };
            let block = DecoderBlock::new(
                in_ch,
                out_ch,
                stride,
                3,
                vb.pp(format!("decoder.blocks.{i}")),
            )?;
            decoder_blocks.push(block);
        }

        // Output conv: hidden/8 -> 1 channel (waveform)
        let decoder_output_conv = ConvBlock::new(
            hidden / 8,
            1,
            7,
            1,
            3,
            1,
            ConvActivation::Tanh,
            vb.pp("decoder.output_conv"),
        )?;

        Ok(Self {
            encoder_input_conv,
            encoder_blocks,
            encoder_output_conv,
            codebook,
            decoder_input_conv,
            decoder_blocks,
            decoder_output_conv,
            decoder_proj,
            config: config.clone(),
            device: device.clone(),
        })
    }

    /// Encode reference audio waveform to discrete codebook tokens.
    ///
    /// `audio`: [num_samples] — mono 24kHz audio
    /// Returns: Vec of `num_codebooks` vectors, each containing token indices.
    pub fn encode(&self, audio: &[f32]) -> Result<Vec<Vec<u32>>> {
        // [1, 1, num_samples]
        let x = Tensor::from_vec(audio.to_vec(), (1, 1, audio.len()), &self.device)?;

        // Run encoder
        let mut hidden = self.encoder_input_conv.forward(&x)?;
        for block in &self.encoder_blocks {
            hidden = block.forward(&hidden)?;
        }
        let latent = self.encoder_output_conv.forward(&hidden)?;

        // latent: [1, codebook_dim, seq_len]
        // Quantize via nearest-neighbor lookup in each codebook
        let seq_len = latent.dim(D::Minus1)?;
        let mut all_codes = Vec::with_capacity(self.config.num_codebooks);

        // Residual quantization: subtract each codebook's contribution
        let mut residual = latent.clone();

        for cb_idx in 0..self.config.num_codebooks {
            // residual: [1, codebook_dim, seq_len] -> find nearest codebook entry per timestep
            let codes = self.quantize_layer(&residual, cb_idx, seq_len)?;

            // Look up the quantized vectors and subtract from residual
            let code_indices =
                Tensor::from_vec(codes.clone(), (1, seq_len), &self.device)?;
            let quantized = self.codebook.codebooks[cb_idx].forward(&code_indices)?;
            // quantized: [1, seq_len, codebook_dim] -> [1, codebook_dim, seq_len]
            let quantized = quantized.transpose(1, 2)?;
            residual = (residual - quantized)?;

            all_codes.push(codes);
        }

        Ok(all_codes)
    }

    /// Quantize a single RVQ layer by finding the nearest codebook entry.
    fn quantize_layer(
        &self,
        residual: &Tensor,
        codebook_idx: usize,
        _seq_len: usize,
    ) -> Result<Vec<u32>> {
        // residual: [1, codebook_dim, seq_len]
        // codebook weights: [codebook_size, codebook_dim]
        let cb_weight = self.codebook.codebooks[codebook_idx]
            .embeddings()
            .clone(); // [codebook_size, codebook_dim]

        // Transpose residual: [1, seq_len, codebook_dim]
        let residual_t = residual.transpose(1, 2)?.squeeze(0)?; // [seq_len, codebook_dim]

        // Compute L2 distances: ||r - c||^2 = ||r||^2 - 2*r*c^T + ||c||^2
        let r_sq = residual_t.sqr()?.sum(D::Minus1)?; // [seq_len]
        let c_sq = cb_weight.sqr()?.sum(D::Minus1)?; // [codebook_size]
        let rc = residual_t.matmul(&cb_weight.t()?)?; // [seq_len, codebook_size]

        let r_sq = r_sq.unsqueeze(1)?; // [seq_len, 1]
        let c_sq = c_sq.unsqueeze(0)?; // [1, codebook_size]

        let distances = (r_sq.broadcast_add(&c_sq)? - (rc * 2.0)?)?; // [seq_len, codebook_size]

        // Argmin per timestep
        let indices = distances.argmin(D::Minus1)?; // [seq_len]
        let codes: Vec<u32> = indices.to_vec1()?;

        Ok(codes)
    }

    /// Decode discrete codebook tokens to audio waveform.
    ///
    /// `codes`: Vec of `num_codebooks` vectors of token indices.
    /// Returns: Vec<f32> — mono 24kHz audio samples.
    pub fn decode(&self, codes: &[Vec<u32>]) -> Result<Vec<f32>> {
        // Look up and sum all codebook embeddings
        let embeddings = self.codebook.decode(codes, &self.device)?;
        // embeddings: [1, codebook_dim, seq_len]

        // Project to decoder hidden size: [1, seq_len, codebook_dim] -> [1, seq_len, hidden]
        let emb_t = embeddings.transpose(1, 2)?; // [1, seq_len, codebook_dim]
        let projected = self.decoder_proj.forward(&emb_t)?; // [1, seq_len, hidden]
        let mut hidden = projected.transpose(1, 2)?; // [1, hidden, seq_len]

        // Run decoder
        hidden = self.decoder_input_conv.forward(&hidden)?;
        for block in &self.decoder_blocks {
            hidden = block.forward(&hidden)?;
        }
        let waveform = self.decoder_output_conv.forward(&hidden)?;

        // [1, 1, num_samples] -> Vec<f32>
        let samples: Vec<f32> = waveform.flatten_all()?.to_vec1()?;
        Ok(samples)
    }

    /// Decode a single frame's codes to audio samples (for streaming).
    ///
    /// `frame_codes`: [num_codebooks] — one token per codebook for a single frame
    /// Returns: audio samples for this frame (~1920 samples at 24kHz / 12.5Hz)
    pub fn decode_frame(&self, frame_codes: &[u32]) -> Result<Vec<f32>> {
        let codes: Vec<Vec<u32>> = frame_codes.iter().map(|&c| vec![c]).collect();
        self.decode(&codes)
    }

    /// Get the number of codebooks.
    pub fn num_codebooks(&self) -> usize {
        self.config.num_codebooks
    }

    /// Get the output sample rate.
    pub fn sample_rate(&self) -> u32 {
        self.config.sample_rate
    }
}

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

    #[test]
    fn test_elu_positive() {
        let device = Device::Cpu;
        let x = Tensor::from_vec(vec![1.0f32, 2.0, 3.0], (3,), &device).unwrap();
        let result = elu(&x, 1.0).unwrap();
        let values: Vec<f32> = result.to_vec1().unwrap();
        assert!((values[0] - 1.0).abs() < 1e-5);
        assert!((values[1] - 2.0).abs() < 1e-5);
    }

    #[test]
    fn test_elu_negative() {
        let device = Device::Cpu;
        let x = Tensor::from_vec(vec![-1.0f32], (1,), &device).unwrap();
        let result = elu(&x, 1.0).unwrap();
        let values: Vec<f32> = result.to_vec1().unwrap();
        // ELU(-1) = exp(-1) - 1 ≈ -0.6321
        assert!((values[0] - (-0.6321)).abs() < 0.01);
    }

    #[test]
    fn test_speech_tokenizer_config() {
        let config = SpeechTokenizerConfig::default();
        assert_eq!(config.num_codebooks, 16);
        assert_eq!(config.codebook_size, 2048);
        assert_eq!(config.sample_rate, 24_000);
    }
}
