finalfusion 0.17.2

//! Word embeddings.

use std::collections::HashMap;
use std::io::{Read, Seek, Write};
use std::iter::Enumerate;
use std::mem;
use std::slice;

use ndarray::{s, Array1, Array2, ArrayViewMut1, ArrayViewMut2, Axis, CowArray, Ix1};
use rand::{CryptoRng, RngCore, SeedableRng};
use rand_chacha::ChaChaRng;
use reductive::pq::TrainPq;

use crate::chunks::io::{ChunkIdentifier, Header, ReadChunk, WriteChunk};
use crate::chunks::metadata::Metadata;
use crate::chunks::norms::NdNorms;
use crate::chunks::storage::{
    sealed::CloneFromMapping, NdArray, Quantize as QuantizeStorage, QuantizedArray, Storage,
    StorageView, StorageViewWrap, StorageWrap,
};
use crate::chunks::vocab::{
    BucketSubwordVocab, ExplicitSubwordVocab, FastTextSubwordVocab, SimpleVocab, SubwordVocab,
    Vocab, VocabWrap, WordIndex,
};
use crate::error::{Error, Result};
use crate::io::{ReadEmbeddings, WriteEmbeddings};
use crate::subword::BucketIndexer;
use crate::util::l2_normalize;
use crate::vocab::FloretSubwordVocab;

/// Word embeddings.
///
/// This data structure stores word embeddings (also known as *word vectors*)
/// and provides some useful methods on the embeddings, such as similarity
/// and analogy queries.
#[derive(Clone, Debug)]
pub struct Embeddings<V, S> {
    metadata: Option<Metadata>,
    storage: S,
    vocab: V,
    norms: Option<NdNorms>,
}

impl<V, S> Embeddings<V, S>
where
    V: Vocab,
    S: Storage,
{
    /// Construct an embeddings from a vocabulary, storage, and norms.
    ///
    /// The embeddings for known words **must** be
    /// normalized. However, this is not verified due to the high
    /// computational cost.
    pub fn new(metadata: Option<Metadata>, vocab: V, storage: S, norms: NdNorms) -> Self {
        assert_eq!(
            vocab.words_len(),
            norms.len(),
            "Vocab and norms do not have the same length"
        );
        Embeddings::new_with_maybe_norms(metadata, vocab, storage, Some(norms))
    }

    pub(crate) fn new_with_maybe_norms(
        metadata: Option<Metadata>,
        vocab: V,
        storage: S,
        norms: Option<NdNorms>,
    ) -> Self {
        assert_eq!(
            vocab.vocab_len(),
            storage.shape().0,
            "Max vocab index must match number of rows in the embedding matrix."
        );
        Embeddings {
            metadata,
            storage,
            vocab,
            norms,
        }
    }
}

impl<V, S> Embeddings<V, S> {
    /// Decompose embeddings in its vocabulary, storage, and
    /// optionally norms.
    pub fn into_parts(self) -> (Option<Metadata>, V, S, Option<NdNorms>) {
        (self.metadata, self.vocab, self.storage, self.norms)
    }

    /// Get metadata.
    pub fn metadata(&self) -> Option<&Metadata> {
        self.metadata.as_ref()
    }

    /// Get metadata mutably.
    pub fn metadata_mut(&mut self) -> Option<&mut Metadata> {
        self.metadata.as_mut()
    }

    /// Get embedding norms.
    pub fn norms(&self) -> Option<&NdNorms> {
        self.norms.as_ref()
    }

    /// Set metadata.
    ///
    /// Returns the previously-stored metadata.
    pub fn set_metadata(&mut self, mut metadata: Option<Metadata>) -> Option<Metadata> {
        mem::swap(&mut self.metadata, &mut metadata);
        metadata
    }

    /// Get the embedding storage.
    pub fn storage(&self) -> &S {
        &self.storage
    }

    /// Get the vocabulary.
    pub fn vocab(&self) -> &V {
        &self.vocab
    }
}

#[allow(clippy::len_without_is_empty)]
impl<V, S> Embeddings<V, S>
where
    V: Vocab,
    S: Storage,
{
    /// Return the length (in vector components) of the word embeddings.
    pub fn dims(&self) -> usize {
        self.storage.shape().1
    }

    /// Get the embedding of a word.
    pub fn embedding(&self, word: &str) -> Option<CowArray<f32, Ix1>> {
        match self.vocab.idx(word)? {
            WordIndex::Word(idx) => Some(self.storage.embedding(idx)),
            WordIndex::Subword(indices) => {
                let embeds = self.storage.embeddings(&indices);
                let mut embed = embeds.sum_axis(Axis(0));
                l2_normalize(embed.view_mut());

                Some(CowArray::from(embed))
            }
        }
    }

    /// Realize the embedding of a word into the given vector.
    ///
    /// This variant of `embedding` realizes the embedding into the
    /// given vector. This makes it possible to look up embeddings
    /// without any additional allocations. This method returns
    /// `false` and does not modify the vector if no embedding could
    /// be found.
    ///
    /// Panics when then the vector does not have the same
    /// dimensionality as the word embeddings.
    pub fn embedding_into(&self, word: &str, mut target: ArrayViewMut1<f32>) -> bool {
        assert_eq!(
            target.len(),
            self.dims(),
            "Embeddings have {} dimensions, whereas target array has {}",
            self.dims(),
            target.len()
        );

        let index = if let Some(idx) = self.vocab.idx(word) {
            idx
        } else {
            return false;
        };

        match index {
            WordIndex::Word(idx) => target.assign(&self.storage.embedding(idx)),
            WordIndex::Subword(indices) => {
                target.fill(0.);

                let embeds = self.storage.embeddings(&indices);

                for embed in embeds.outer_iter() {
                    target += &embed;
                }

                l2_normalize(target.view_mut());
            }
        }

        true
    }

    /// Get a batch of embeddings.
    ///
    /// The embeddings of all `words` are computed and returned. This method also
    /// return a `Vec` indicating for each word if an embedding could be found.
    pub fn embedding_batch(&self, words: &[impl AsRef<str>]) -> (Array2<f32>, Vec<bool>) {
        let mut embeddings = Array2::zeros((words.len(), self.dims()));
        let found = self.embedding_batch_into(words, embeddings.view_mut());
        (embeddings, found)
    }

    /// Get a batch of embeddings.
    ///
    /// The embeddings of all `words` are computed and written to `output`. A `Vec` is
    /// returned that indicates for each word if an embedding could be found.
    ///
    /// This method panics when `output` does not have the correct shape.
    pub fn embedding_batch_into(
        &self,
        words: &[impl AsRef<str>],
        mut output: ArrayViewMut2<f32>,
    ) -> Vec<bool> {
        assert_eq!(
            output.len_of(Axis(0)),
            words.len(),
            "Expected embedding matrix for batch size {}, got {}",
            words.len(),
            output.len_of(Axis(0))
        );
        assert_eq!(
            output.len_of(Axis(1)),
            self.dims(),
            "Expected embedding matrix for embeddings with dimensionality {}, got {}",
            self.dims(),
            output.len_of(Axis(1))
        );

        let mut found = vec![false; words.len()];

        let mut word_indices: HashMap<_, Vec<_>> = HashMap::new();
        for (idx, word) in words.iter().enumerate() {
            let indices = word_indices.entry(word.as_ref()).or_default();
            indices.push(idx);
        }

        for (word, indices) in word_indices {
            // Look up the embedding for the first occurence of the word and
            // then copy to other occurences.
            let idx_first = indices[0];
            if self.embedding_into(word, output.index_axis_mut(Axis(0), idx_first)) {
                found[idx_first] = true;

                for &idx in indices.iter().skip(1) {
                    let (first, mut cur) = output.multi_slice_mut((s![idx_first, ..], s![idx, ..]));
                    cur.assign(&first);
                    found[idx] = true;
                }
            }
        }

        found
    }

    /// Get the embedding and original norm of a word.
    ///
    /// Returns for a word:
    ///
    /// * The word embedding.
    /// * The norm of the embedding before normalization to a unit vector.
    ///
    /// The original embedding can be reconstructed by multiplying all
    /// embedding components by the original norm.
    ///
    /// If the model does not have associated norms, *1* will be
    /// returned as the norm for vocabulary words.
    pub fn embedding_with_norm(&self, word: &str) -> Option<EmbeddingWithNorm> {
        match self.vocab.idx(word)? {
            WordIndex::Word(idx) => Some(EmbeddingWithNorm {
                embedding: self.storage.embedding(idx),
                norm: self.norms().map(|n| n[idx]).unwrap_or(1.),
            }),
            WordIndex::Subword(indices) => {
                let embeds = self.storage.embeddings(&indices);
                let mut embed = embeds.sum_axis(Axis(0));

                let norm = l2_normalize(embed.view_mut());

                Some(EmbeddingWithNorm {
                    embedding: CowArray::from(embed),
                    norm,
                })
            }
        }
    }

    /// Get an iterator over pairs of words and the corresponding embeddings.
    pub fn iter(&self) -> Iter {
        Iter {
            storage: &self.storage,
            inner: self.vocab.words().iter().enumerate(),
        }
    }

    /// Get an iterator over triples of words, embeddings, and norms.
    ///
    /// Returns an iterator that returns triples of:
    ///
    /// * A word.
    /// * Its word embedding.
    /// * The original norm of the embedding before normalization to a unit vector.
    ///
    /// The original embedding can be reconstructed by multiplying all
    /// embedding components by the original norm.
    ///
    /// If the model does not have associated norms, the norm is
    /// always *1*.
    pub fn iter_with_norms(&self) -> IterWithNorms {
        IterWithNorms {
            storage: &self.storage,
            norms: self.norms(),
            inner: self.vocab.words().iter().enumerate(),
        }
    }

    /// Get the vocabulary size.
    ///
    /// The vocabulary size excludes subword units.
    pub fn len(&self) -> usize {
        self.vocab.words_len()
    }
}

impl<I, S> Embeddings<SubwordVocab<I>, S>
where
    I: BucketIndexer,
    S: Storage + CloneFromMapping,
{
    /// Convert to explicitly indexed subword Embeddings.
    pub fn to_explicit(&self) -> Result<Embeddings<ExplicitSubwordVocab, S::Result>> {
        to_explicit_impl(self.vocab(), self.storage(), self.norms())
    }
}

impl<S> Embeddings<VocabWrap, S>
where
    S: Storage + CloneFromMapping,
{
    /// Try to convert to explicitly indexed subword embeddings.
    ///
    /// Conversion fails if the wrapped vocabulary is `SimpleVocab`, `FloretSubwordVocab` or
    /// already an `ExplicitSubwordVocab`.
    pub fn try_to_explicit(&self) -> Result<Embeddings<ExplicitSubwordVocab, S::Result>> {
        match &self.vocab {
            VocabWrap::BucketSubwordVocab(sw) => {
                Ok(to_explicit_impl(sw, self.storage(), self.norms())?)
            }
            VocabWrap::FastTextSubwordVocab(sw) => {
                Ok(to_explicit_impl(sw, self.storage(), self.norms())?)
            }
            VocabWrap::SimpleVocab(_) => {
                Err(Error::conversion_error("SimpleVocab", "ExplicitVocab"))
            }
            VocabWrap::ExplicitSubwordVocab(_) => {
                Err(Error::conversion_error("ExplicitVocab", "ExplicitVocab"))
            }
            VocabWrap::FloretSubwordVocab(_) => Err(Error::conversion_error(
                "FloretSubwordVocab",
                "ExplicitVocab",
            )),
        }
    }
}

fn to_explicit_impl<I, S>(
    vocab: &SubwordVocab<I>,
    storage: &S,
    norms: Option<&NdNorms>,
) -> Result<Embeddings<ExplicitSubwordVocab, S::Result>>
where
    S: Storage + CloneFromMapping,
    I: BucketIndexer,
{
    let (expl_voc, old_to_new) = vocab.to_explicit()?;
    let mut mapping = (0..expl_voc.vocab_len()).collect::<Vec<_>>();
    for (old, new) in old_to_new {
        mapping[expl_voc.words_len() + new] = old as usize + expl_voc.words_len();
    }

    let new_storage = storage.clone_from_mapping(&mapping);
    Ok(Embeddings::new_with_maybe_norms(
        None,
        expl_voc,
        new_storage,
        norms.cloned(),
    ))
}

macro_rules! impl_embeddings_from(
    ($vocab:ty, $storage:ty, $storage_wrap:ty) => {
        impl From<Embeddings<$vocab, $storage>> for Embeddings<VocabWrap, $storage_wrap> {
            fn from(from: Embeddings<$vocab, $storage>) -> Self {
                let (metadata, vocab, storage, norms) = from.into_parts();
                Embeddings {
                    metadata,
                    vocab: vocab.into(),
                    storage: storage.into(),
                    norms,
                }
            }
        }
    }
);

// Hmpf. With the blanket From<T> for T implementation, we need
// specialization to generalize this.
impl_embeddings_from!(SimpleVocab, NdArray, StorageWrap);
impl_embeddings_from!(SimpleVocab, NdArray, StorageViewWrap);
impl_embeddings_from!(SimpleVocab, QuantizedArray, StorageWrap);
impl_embeddings_from!(BucketSubwordVocab, NdArray, StorageWrap);
impl_embeddings_from!(BucketSubwordVocab, NdArray, StorageViewWrap);
impl_embeddings_from!(BucketSubwordVocab, QuantizedArray, StorageWrap);
impl_embeddings_from!(FastTextSubwordVocab, NdArray, StorageWrap);
impl_embeddings_from!(FastTextSubwordVocab, NdArray, StorageViewWrap);
impl_embeddings_from!(FastTextSubwordVocab, QuantizedArray, StorageWrap);
impl_embeddings_from!(ExplicitSubwordVocab, NdArray, StorageWrap);
impl_embeddings_from!(ExplicitSubwordVocab, NdArray, StorageViewWrap);
impl_embeddings_from!(ExplicitSubwordVocab, QuantizedArray, StorageWrap);
impl_embeddings_from!(FloretSubwordVocab, NdArray, StorageWrap);
impl_embeddings_from!(FloretSubwordVocab, NdArray, StorageViewWrap);
impl_embeddings_from!(FloretSubwordVocab, QuantizedArray, StorageWrap);
impl_embeddings_from!(VocabWrap, QuantizedArray, StorageWrap);

impl<'a, V, S> IntoIterator for &'a Embeddings<V, S>
where
    V: Vocab,
    S: Storage,
{
    type Item = (&'a str, CowArray<'a, f32, Ix1>);
    type IntoIter = Iter<'a>;

    fn into_iter(self) -> Self::IntoIter {
        self.iter()
    }
}

#[cfg(feature = "memmap")]
mod mmap {
    use std::fs::File;
    use std::io::BufReader;

    use super::Embeddings;
    use crate::chunks::io::MmapChunk;
    use crate::chunks::io::{ChunkIdentifier, Header, ReadChunk};
    use crate::chunks::metadata::Metadata;
    use crate::chunks::norms::NdNorms;
    #[cfg(target_endian = "little")]
    use crate::chunks::storage::StorageViewWrap;
    use crate::chunks::storage::{MmapArray, MmapQuantizedArray, StorageWrap};
    use crate::chunks::vocab::{
        BucketSubwordVocab, ExplicitSubwordVocab, FastTextSubwordVocab, SimpleVocab, VocabWrap,
    };
    use crate::error::{Error, Result};
    use crate::io::MmapEmbeddings;
    use crate::vocab::FloretSubwordVocab;

    impl_embeddings_from!(SimpleVocab, MmapArray, StorageWrap);
    #[cfg(target_endian = "little")]
    impl_embeddings_from!(SimpleVocab, MmapArray, StorageViewWrap);
    impl_embeddings_from!(SimpleVocab, MmapQuantizedArray, StorageWrap);
    impl_embeddings_from!(BucketSubwordVocab, MmapArray, StorageWrap);
    #[cfg(target_endian = "little")]
    impl_embeddings_from!(BucketSubwordVocab, MmapArray, StorageViewWrap);
    impl_embeddings_from!(BucketSubwordVocab, MmapQuantizedArray, StorageWrap);
    impl_embeddings_from!(FloretSubwordVocab, MmapArray, StorageWrap);
    #[cfg(target_endian = "little")]
    impl_embeddings_from!(FloretSubwordVocab, MmapArray, StorageViewWrap);
    impl_embeddings_from!(FloretSubwordVocab, MmapQuantizedArray, StorageWrap);
    impl_embeddings_from!(FastTextSubwordVocab, MmapArray, StorageWrap);
    #[cfg(target_endian = "little")]
    impl_embeddings_from!(FastTextSubwordVocab, MmapArray, StorageViewWrap);
    impl_embeddings_from!(FastTextSubwordVocab, MmapQuantizedArray, StorageWrap);
    impl_embeddings_from!(ExplicitSubwordVocab, MmapArray, StorageWrap);
    impl_embeddings_from!(ExplicitSubwordVocab, MmapQuantizedArray, StorageWrap);
    #[cfg(target_endian = "little")]
    impl_embeddings_from!(ExplicitSubwordVocab, MmapArray, StorageViewWrap);
    #[cfg(feature = "memmap")]
    impl_embeddings_from!(VocabWrap, MmapQuantizedArray, StorageWrap);

    impl<V, S> MmapEmbeddings for Embeddings<V, S>
    where
        Self: Sized,
        V: ReadChunk,
        S: MmapChunk,
    {
        fn mmap_embeddings(read: &mut BufReader<File>) -> Result<Self> {
            let header = Header::read_chunk(read)?;
            let chunks = header.chunk_identifiers();
            if chunks.is_empty() {
                return Err(Error::Format(String::from(
                    "Embedding file does not contain chunks",
                )));
            }

            let metadata = if header.chunk_identifiers()[0] == ChunkIdentifier::Metadata {
                Some(Metadata::read_chunk(read)?)
            } else {
                None
            };

            let vocab = V::read_chunk(read)?;
            let storage = S::mmap_chunk(read)?;
            let norms = NdNorms::read_chunk(read).ok();

            Ok(Embeddings {
                metadata,
                storage,
                vocab,
                norms,
            })
        }
    }
}

impl<V, S> ReadEmbeddings for Embeddings<V, S>
where
    V: ReadChunk,
    S: ReadChunk,
{
    fn read_embeddings<R>(read: &mut R) -> Result<Self>
    where
        R: Read + Seek,
    {
        let header = Header::read_chunk(read)?;
        let chunks = header.chunk_identifiers();
        if chunks.is_empty() {
            return Err(Error::Format(String::from(
                "Embedding file does not contain chunks",
            )));
        }

        let metadata = if header.chunk_identifiers()[0] == ChunkIdentifier::Metadata {
            Some(Metadata::read_chunk(read)?)
        } else {
            None
        };

        let vocab = V::read_chunk(read)?;
        let storage = S::read_chunk(read)?;
        let norms = NdNorms::read_chunk(read).ok();

        Ok(Embeddings {
            metadata,
            storage,
            vocab,
            norms,
        })
    }
}

impl<V, S> WriteEmbeddings for Embeddings<V, S>
where
    V: WriteChunk,
    S: WriteChunk,
{
    fn write_embeddings<W>(&self, write: &mut W) -> Result<()>
    where
        W: Write + Seek,
    {
        let mut chunks = match self.metadata {
            Some(ref metadata) => vec![metadata.chunk_identifier()],
            None => vec![],
        };

        chunks.extend_from_slice(&[
            self.vocab.chunk_identifier(),
            self.storage.chunk_identifier(),
        ]);

        if let Some(ref norms) = self.norms {
            chunks.push(norms.chunk_identifier());
        }

        Header::new(chunks).write_chunk(write)?;
        if let Some(ref metadata) = self.metadata {
            metadata.write_chunk(write)?;
        }

        self.vocab.write_chunk(write)?;
        self.storage.write_chunk(write)?;

        if let Some(norms) = self.norms() {
            norms.write_chunk(write)?;
        }

        Ok(())
    }

    fn write_embeddings_len(&self, offset: u64) -> u64 {
        let mut len = 0;

        let mut chunks = match self.metadata {
            Some(ref metadata) => vec![metadata.chunk_identifier()],
            None => vec![],
        };

        chunks.extend_from_slice(&[
            self.vocab.chunk_identifier(),
            self.storage.chunk_identifier(),
        ]);

        if let Some(ref norms) = self.norms {
            chunks.push(norms.chunk_identifier());
        }

        let header = Header::new(chunks);
        len += header.chunk_len(offset + len);

        if let Some(ref metadata) = self.metadata {
            len += metadata.chunk_len(offset + len);
        }

        len += self.vocab.chunk_len(offset + len);
        len += self.storage.chunk_len(offset + len);

        if let Some(ref norms) = self.norms {
            len += norms.chunk_len(offset + len);
        }

        len
    }
}

/// Quantizable embedding matrix.
pub trait Quantize<V> {
    /// Quantize the embedding matrix.
    ///
    /// This method trains a quantizer for the embedding matrix and
    /// then quantizes the matrix using this quantizer.
    ///
    /// The xorshift PRNG is used for picking the initial quantizer
    /// centroids.
    fn quantize<T>(
        &self,
        n_subquantizers: usize,
        n_subquantizer_bits: u32,
        n_iterations: usize,
        n_attempts: usize,
        normalize: bool,
    ) -> Result<Embeddings<V, QuantizedArray>>
    where
        T: TrainPq<f32>,
    {
        self.quantize_using::<T, _>(
            n_subquantizers,
            n_subquantizer_bits,
            n_iterations,
            n_attempts,
            normalize,
            ChaChaRng::from_entropy(),
        )
    }

    /// Quantize the embedding matrix using the provided RNG.
    ///
    /// This method trains a quantizer for the embedding matrix and
    /// then quantizes the matrix using this quantizer.
    fn quantize_using<T, R>(
        &self,
        n_subquantizers: usize,
        n_subquantizer_bits: u32,
        n_iterations: usize,
        n_attempts: usize,
        normalize: bool,
        rng: R,
    ) -> Result<Embeddings<V, QuantizedArray>>
    where
        T: TrainPq<f32>,
        R: CryptoRng + RngCore + SeedableRng + Send;
}

impl<V, S> Quantize<V> for Embeddings<V, S>
where
    V: Vocab + Clone,
    S: StorageView,
{
    fn quantize_using<T, R>(
        &self,
        n_subquantizers: usize,
        n_subquantizer_bits: u32,
        n_iterations: usize,
        n_attempts: usize,
        normalize: bool,
        rng: R,
    ) -> Result<Embeddings<V, QuantizedArray>>
    where
        T: TrainPq<f32>,
        R: CryptoRng + RngCore + SeedableRng + Send,
    {
        let quantized_storage = self.storage().quantize_using::<T, R>(
            n_subquantizers,
            n_subquantizer_bits,
            n_iterations,
            n_attempts,
            normalize,
            rng,
        )?;

        Ok(Embeddings {
            metadata: self.metadata().cloned(),
            vocab: self.vocab.clone(),
            storage: quantized_storage,
            norms: self.norms().cloned(),
        })
    }
}

/// An embedding with its (pre-normalization) l2 norm.
pub struct EmbeddingWithNorm<'a> {
    pub embedding: CowArray<'a, f32, Ix1>,
    pub norm: f32,
}

impl<'a> EmbeddingWithNorm<'a> {
    // Compute the unnormalized embedding.
    pub fn into_unnormalized(self) -> Array1<f32> {
        let mut unnormalized = self.embedding.into_owned();
        unnormalized *= self.norm;
        unnormalized
    }
}

/// Iterator over embeddings.
pub struct Iter<'a> {
    storage: &'a dyn Storage,
    inner: Enumerate<slice::Iter<'a, String>>,
}

impl<'a> Iterator for Iter<'a> {
    type Item = (&'a str, CowArray<'a, f32, Ix1>);

    fn next(&mut self) -> Option<Self::Item> {
        self.inner
            .next()
            .map(|(idx, word)| (word.as_str(), self.storage.embedding(idx)))
    }
}

/// Iterator over embeddings.
pub struct IterWithNorms<'a> {
    storage: &'a dyn Storage,
    norms: Option<&'a NdNorms>,
    inner: Enumerate<slice::Iter<'a, String>>,
}

impl<'a> Iterator for IterWithNorms<'a> {
    type Item = (&'a str, EmbeddingWithNorm<'a>);

    fn next(&mut self) -> Option<Self::Item> {
        self.inner.next().map(|(idx, word)| {
            (
                word.as_str(),
                EmbeddingWithNorm {
                    embedding: self.storage.embedding(idx),
                    norm: self.norms.map(|n| n[idx]).unwrap_or(1.),
                },
            )
        })
    }
}

#[cfg(test)]
mod tests {
    use std::fs::File;
    use std::io::{BufReader, Cursor, Seek, SeekFrom};

    use ndarray::{array, Array1};
    use toml::toml;

    use super::Embeddings;
    use crate::chunks::metadata::Metadata;
    use crate::chunks::norms::NdNorms;
    use crate::chunks::storage::{NdArray, Storage, StorageView};
    use crate::chunks::vocab::{FastTextSubwordVocab, SimpleVocab, Vocab};
    use crate::compat::fasttext::ReadFastText;
    use crate::compat::word2vec::ReadWord2VecRaw;
    use crate::io::{ReadEmbeddings, WriteEmbeddings};
    use crate::prelude::StorageWrap;
    use crate::storage::QuantizedArray;
    use crate::subword::Indexer;
    use crate::vocab::VocabWrap;

    fn test_embeddings() -> Embeddings<SimpleVocab, NdArray> {
        let mut reader = BufReader::new(File::open("testdata/similarity.bin").unwrap());
        Embeddings::read_word2vec_binary_raw(&mut reader, false).unwrap()
    }

    fn test_embeddings_with_metadata() -> Embeddings<SimpleVocab, NdArray> {
        let mut embeds = test_embeddings();
        embeds.set_metadata(Some(test_metadata()));
        embeds
    }

    fn test_embeddings_fasttext() -> Embeddings<FastTextSubwordVocab, NdArray> {
        let mut reader = BufReader::new(File::open("testdata/fasttext.bin").unwrap());
        Embeddings::read_fasttext(&mut reader).unwrap()
    }

    fn test_embeddings_quantized() -> Embeddings<SimpleVocab, QuantizedArray> {
        let mut reader = BufReader::new(File::open("testdata/quantized.fifu").unwrap());
        Embeddings::read_embeddings(&mut reader).unwrap()
    }

    fn test_metadata() -> Metadata {
        Metadata::new(toml! {
            [hyperparameters]
            dims = 300
            ns = 5

            [description]
            description = "Test model"
            language = "de"
        })
    }

    #[test]
    fn embedding_into_equal_to_embedding() {
        let mut reader = BufReader::new(File::open("testdata/fasttext.bin").unwrap());
        let embeds = Embeddings::read_fasttext(&mut reader).unwrap();

        // Known word
        let mut target = Array1::zeros(embeds.dims());
        assert!(embeds.embedding_into("ganz", target.view_mut()));
        assert_eq!(target, embeds.embedding("ganz").unwrap());

        // Unknown word
        let mut target = Array1::zeros(embeds.dims());
        assert!(embeds.embedding_into("iddqd", target.view_mut()));
        assert_eq!(target, embeds.embedding("iddqd").unwrap());

        // Unknown word, non-zero vector
        assert!(embeds.embedding_into("idspispopd", target.view_mut()));
        assert_eq!(target, embeds.embedding("idspispopd").unwrap());
    }

    #[test]
    fn embedding_batch_returns_correct_embeddings() {
        let embeds = test_embeddings();
        let (lookups, found) = embeds.embedding_batch(&[
            "Berlin",
            "Bremen",
            "Groningen",
            "Bremen",
            "Berlin",
            "Amsterdam",
        ]);

        assert_eq!(lookups.row(0), embeds.embedding("Berlin").unwrap());
        assert_eq!(lookups.row(1), embeds.embedding("Bremen").unwrap());
        assert_eq!(lookups.row(3), embeds.embedding("Bremen").unwrap());
        assert_eq!(lookups.row(4), embeds.embedding("Berlin").unwrap());
        assert_eq!(found, &[true, true, false, true, true, false]);
    }

    #[test]
    #[cfg(feature = "memmap")]
    fn mmap() {
        use crate::chunks::storage::MmapArray;
        use crate::io::MmapEmbeddings;
        let check_embeds = test_embeddings();
        let mut reader = BufReader::new(File::open("testdata/similarity.fifu").unwrap());
        let embeds: Embeddings<SimpleVocab, MmapArray> =
            Embeddings::mmap_embeddings(&mut reader).unwrap();
        assert_eq!(embeds.vocab(), check_embeds.vocab());

        #[cfg(target_endian = "little")]
        assert_eq!(embeds.storage().view(), check_embeds.storage().view());
    }

    #[test]
    fn to_explicit() {
        let mut reader = BufReader::new(File::open("testdata/fasttext.bin").unwrap());
        let embeds = Embeddings::read_fasttext(&mut reader).unwrap();
        let expl_embeds = embeds.to_explicit().unwrap();
        let ganz = embeds.embedding("ganz").unwrap();
        let ganz_expl = expl_embeds.embedding("ganz").unwrap();
        assert_eq!(ganz, ganz_expl);

        assert!(embeds
            .vocab
            .idx("anz")
            .map(|i| i.subword().is_some())
            .unwrap_or(false));
        let anz_idx = embeds.vocab.indexer().index_ngram(&"anz".into())[0] as usize
            + embeds.vocab.words_len();
        let anz = embeds.storage.embedding(anz_idx);
        let anz_idx = expl_embeds.vocab.indexer().index_ngram(&"anz".into())[0] as usize
            + embeds.vocab.words_len();
        let anz_expl = expl_embeds.storage.embedding(anz_idx);
        assert_eq!(anz, anz_expl);
    }

    #[test]
    fn norms() {
        let vocab = SimpleVocab::new(vec!["norms".to_string(), "test".to_string()]);
        let storage = NdArray::new(array![[1f32], [-1f32]]);
        let norms = NdNorms::new(array![2f32, 3f32]);
        let check = Embeddings::new(None, vocab, storage, norms);

        let mut serialized = Cursor::new(Vec::new());
        check.write_embeddings(&mut serialized).unwrap();
        serialized.seek(SeekFrom::Start(0)).unwrap();

        let embeddings: Embeddings<SimpleVocab, NdArray> =
            Embeddings::read_embeddings(&mut serialized).unwrap();

        assert!(check
            .norms()
            .unwrap()
            .view()
            .abs_diff_eq(&embeddings.norms().unwrap().view(), 1e-8),);
    }

    #[test]
    fn write_read_simple_roundtrip() {
        let check_embeds = test_embeddings();
        let mut cursor = Cursor::new(Vec::new());
        check_embeds.write_embeddings(&mut cursor).unwrap();
        cursor.seek(SeekFrom::Start(0)).unwrap();
        let embeds: Embeddings<SimpleVocab, NdArray> =
            Embeddings::read_embeddings(&mut cursor).unwrap();
        assert_eq!(embeds.storage().view(), check_embeds.storage().view());
        assert_eq!(embeds.vocab(), check_embeds.vocab());
        assert_eq!(
            cursor.into_inner().len() as u64,
            check_embeds.write_embeddings_len(0)
        );
    }

    #[test]
    fn write_read_simple_metadata_roundtrip() {
        let check_embeds = test_embeddings_with_metadata();

        let mut cursor = Cursor::new(Vec::new());
        check_embeds.write_embeddings(&mut cursor).unwrap();
        cursor.seek(SeekFrom::Start(0)).unwrap();
        let embeds: Embeddings<SimpleVocab, NdArray> =
            Embeddings::read_embeddings(&mut cursor).unwrap();
        assert_eq!(embeds.storage().view(), check_embeds.storage().view());
        assert_eq!(embeds.vocab(), check_embeds.vocab());
        assert_eq!(
            cursor.into_inner().len() as u64,
            check_embeds.write_embeddings_len(0)
        );
    }

    #[test]
    fn embeddings_write_length_different_offsets() {
        let embeddings: Vec<Embeddings<VocabWrap, StorageWrap>> = vec![
            test_embeddings().into(),
            test_embeddings_with_metadata().into(),
            test_embeddings_fasttext().into(),
            test_embeddings_quantized().into(),
        ];

        for check_embeddings in &embeddings {
            for offset in 0..16u64 {
                let mut cursor = Cursor::new(Vec::new());
                cursor.seek(SeekFrom::Start(offset)).unwrap();
                check_embeddings.write_embeddings(&mut cursor).unwrap();
                assert_eq!(
                    cursor.into_inner().len() as u64 - offset,
                    check_embeddings.write_embeddings_len(offset)
                );
            }
        }
    }
}