//! # T5 (Text-To-Text Transfer Transformer)

//!

//! Implementation of the T5 language model ([Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) Raffel, Shazeer, Roberts, Lee, Narang, Matena, Zhou, Li, Liu, 2019).

//! The base model is implemented in the `t5::T5Model` struct. This model includes a language model head: `t5::T5ForConditionalGeneration`

//! implementing the common `generation::LMHeadModel` trait shared between the models used for generation (see `pipelines` for more information).

//!

//! # Model set-up and pre-trained weights loading

//!

//! A full working example (translation) is provided in `examples/t5`, run with `cargo run --example t5`.

//! All models expect the following resources:

//! - Configuration file expected to have a structure following the [Transformers library](https://github.com/huggingface/transformers)

//! - Model weights are expected to have a structure and parameter names following the [Transformers library](https://github.com/huggingface/transformers). A conversion using the Python utility scripts is required to convert the `.bin` weights to the `.ot` format.

//! - `T5Tokenizer` using a `spiece.model` sentence piece model

//!

//! Pretrained models for a number of language pairs are available and can be downloaded using RemoteResources.

//!

//! ```no_run

//! # fn main() -> anyhow::Result<()> {

//! #

//! use tch::{nn, Device};

//! # use std::path::PathBuf;

//! use rust_bert::resources::{LocalResource, Resource};

//! use rust_bert::t5::{T5Config, T5ForConditionalGeneration};

//! use rust_bert::Config;

//! use rust_tokenizers::preprocessing::tokenizer::t5_tokenizer::T5Tokenizer;

//!

//! let config_resource = Resource::Local(LocalResource {

//!     local_path: PathBuf::from("path/to/config.json"),

//! });

//! let sentence_piece_resource = Resource::Local(LocalResource {

//!     local_path: PathBuf::from("path/to/spiece.model"),

//! });

//! let weights_resource = Resource::Local(LocalResource {

//!     local_path: PathBuf::from("path/to/model.ot"),

//! });

//! let config_path = config_resource.get_local_path()?;

//! let spiece_path = sentence_piece_resource.get_local_path()?;

//! let weights_path = weights_resource.get_local_path()?;

//!

//! let device = Device::cuda_if_available();

//! let mut vs = nn::VarStore::new(device);

//! let tokenizer = T5Tokenizer::from_file(spiece_path.to_str().unwrap(), true);

//! let config = T5Config::from_file(config_path);

//! let t5_model = T5ForConditionalGeneration::new(&vs.root(), &config, false, false);

//! vs.load(weights_path)?;

//!

//! # Ok(())

//! # }

//! ```


mod attention;
mod encoder;
mod layer_norm;
mod t5_model;

pub use attention::LayerState;
pub use t5_model::{
    T5Config, T5ConfigResources, T5ForConditionalGeneration, T5Model, T5ModelOutput,
    T5ModelResources, T5Prefix, T5VocabResources,
};