// Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and Facebook, Inc.
// Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
// Copyright 2019 Guillaume Becquin
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//     http://www.apache.org/licenses/LICENSE-2.0
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

use tch::{nn, Tensor, Kind};
use crate::common::dropout::Dropout;
use tch::nn::{EmbeddingConfig, embedding};
use crate::bert::{BertConfig, BertEmbedding};

#[derive(Debug)]
/// # BertEmbeddings implementation for RoBERTa model
/// Implementation of the `BertEmbedding` trait for RoBERTa models
pub struct RobertaEmbeddings {
    word_embeddings: nn::Embedding,
    position_embeddings: nn::Embedding,
    token_type_embeddings: nn::Embedding,
    layer_norm: nn::LayerNorm,
    dropout: Dropout,
    padding_index: i64,
}

impl RobertaEmbeddings {
    fn create_position_ids_from_input_ids(&self, x: &Tensor) -> Tensor {
        let mask: Tensor = x.ne(self.padding_index).to_kind(Kind::Int64);
        mask.cumsum(1, Kind::Int64) * mask + self.padding_index
    }

    fn create_position_ids_from_embeddings(&self, x: &Tensor) -> Tensor {
        let input_shape = x.size();
        let input_shape = vec!(input_shape[0], input_shape[1]);
        let position_ids: Tensor = Tensor::arange1(self.padding_index + 1, input_shape[0], (Kind::Int64, x.device()));
        position_ids.unsqueeze(0).expand(&input_shape, true)
    }
}

impl BertEmbedding for RobertaEmbeddings {
    /// Build a new `RobertaEmbeddings`
    ///
    /// # Arguments
    ///
    /// * `p` - Variable store path for the root of the BertEmbeddings model
    /// * `config` - `BertConfig` object defining the model architecture and vocab/hidden size
    ///
    /// # Example
    ///
    /// ```no_run
    /// use rust_bert::bert::{BertConfig, BertEmbedding};
    /// use tch::{nn, Device};
    /// use rust_bert::Config;
    /// use std::path::Path;
    /// use rust_bert::roberta::RobertaEmbeddings;
    ///
    /// let config_path = Path::new("path/to/config.json");
    /// let device = Device::Cpu;
    /// let p = nn::VarStore::new(device);
    /// let config = BertConfig::from_file(config_path);
    /// let robert_embeddings = RobertaEmbeddings::new(&(&p.root() / "bert_embeddings"), &config);
    /// ```
    ///
    fn new(p: &nn::Path, config: &BertConfig) -> RobertaEmbeddings {
        let embedding_config = EmbeddingConfig { padding_idx: 1, ..Default::default() };

        let word_embeddings: nn::Embedding = embedding(p / "word_embeddings",
                                                       config.vocab_size,
                                                       config.hidden_size,
                                                       embedding_config);

        let position_embeddings: nn::Embedding = embedding(p / "position_embeddings",
                                                           config.max_position_embeddings,
                                                           config.hidden_size,
                                                           Default::default());

        let token_type_embeddings: nn::Embedding = embedding(p / "token_type_embeddings",
                                                             config.type_vocab_size,
                                                             config.hidden_size,
                                                             Default::default());

        let layer_norm_config = nn::LayerNormConfig { eps: 1e-12, ..Default::default() };
        let layer_norm: nn::LayerNorm = nn::layer_norm(p / "LayerNorm", vec![config.hidden_size], layer_norm_config);
        let dropout: Dropout = Dropout::new(config.hidden_dropout_prob);
        RobertaEmbeddings { word_embeddings, position_embeddings, token_type_embeddings, layer_norm, dropout, padding_index: 1 }
    }

    /// Forward pass through the embedding layer.
    /// This differs from the original BERT embeddings in how the position ids are calculated when not provided.
    ///
    /// # Arguments
    ///
    /// * `input_ids` - Optional input tensor of shape (*batch size*, *sequence_length*). If None, pre-computed embeddings must be provided (see *input_embeds*)
    /// * `token_type_ids` -Optional segment id of shape (*batch size*, *sequence_length*). Convention is value of 0 for the first sentence (incl. *[SEP]*) and 1 for the second sentence. If None set to 0.
    /// * `position_ids` - Optional position ids of shape (*batch size*, *sequence_length*). If None, will be incremented from 0.
    /// * `input_embeds` - Optional pre-computed input embeddings of shape (*batch size*, *sequence_length*, *hidden_size*). If None, input ids must be provided (see *input_ids*)
    /// * `train` - boolean flag to turn on/off the dropout layers in the model. Should be set to false for inference.
    ///
    /// # Returns
    ///
    /// * `embedded_output` - `Tensor` of shape (*batch size*, *sequence_length*, *hidden_size*)
    ///
    /// # Example
    ///
    /// ```no_run
    ///# use rust_bert::bert::{BertConfig, BertEmbedding};
    ///# use tch::{nn, Device, Tensor, no_grad};
    ///# use rust_bert::Config;
    ///# use std::path::Path;
    ///# use tch::kind::Kind::Int64;
    /// use rust_bert::roberta::RobertaEmbeddings;
    ///# let config_path = Path::new("path/to/config.json");
    ///# let vocab_path = Path::new("path/to/vocab.txt");
    ///# let device = Device::Cpu;
    ///# let vs = nn::VarStore::new(device);
    ///# let config = BertConfig::from_file(config_path);
    ///# let roberta_embeddings = RobertaEmbeddings::new(&vs.root(), &config);
    ///  let (batch_size, sequence_length) = (64, 128);
    ///  let input_tensor = Tensor::rand(&[batch_size, sequence_length], (Int64, device));
    ///  let token_type_ids = Tensor::zeros(&[batch_size, sequence_length], (Int64, device));
    ///  let position_ids = Tensor::arange(sequence_length, (Int64, device)).expand(&[batch_size, sequence_length], true);
    ///
    ///  let embedded_output = no_grad(|| {
    ///    roberta_embeddings
    ///         .forward_t(Some(input_tensor),
    ///                    Some(token_type_ids),
    ///                    Some(position_ids),
    ///                    None,
    ///                    false).unwrap()
    ///    });
    /// ```
    ///
    fn forward_t(&self,
                 input_ids: Option<Tensor>,
                 token_type_ids: Option<Tensor>,
                 position_ids: Option<Tensor>,
                 input_embeds: Option<Tensor>,
                 train: bool) -> Result<Tensor, &'static str> {
        let (input_embeddings, input_shape) = match &input_ids {
            Some(input_value) => match &input_embeds {
                Some(_) => { return Err("Only one of input ids or input embeddings may be set"); }
                None => (input_value.apply_t(&self.word_embeddings, train), input_value.size())
            }
            None => match &input_embeds {
                Some(embeds) => (embeds.copy(), vec!(embeds.size()[0], embeds.size()[1])),
                None => { return Err("Only one of input ids or input embeddings may be set"); }
            }
        };


        let position_ids = match position_ids {
            Some(value) => value,
            None => match input_ids {
                Some(value) => self.create_position_ids_from_input_ids(&value),
                None => self.create_position_ids_from_embeddings(&input_embeds.unwrap())
            }
        };

        let token_type_ids = match token_type_ids {
            Some(value) => value,
            None => Tensor::zeros(&input_shape, (Kind::Int64, input_embeddings.device()))
        };

        let position_embeddings = position_ids.apply(&self.position_embeddings);
        let token_type_embeddings = token_type_ids.apply(&self.token_type_embeddings);

        let input_embeddings: Tensor = input_embeddings + position_embeddings + token_type_embeddings;
        Ok(input_embeddings.apply(&self.layer_norm).apply_t(&self.dropout, train))
    }
}