// Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and Facebook, Inc.
// Copyright 2019-2020 Guillaume Becquin
// Copyright 2020 Maarten van Gompel
// 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.
//! # Sequence classification pipeline (e.g. Sentiment Analysis)
//! More generic sequence classification pipeline, works with multiple models (Bert, Roberta)
//!
//! ```no_run
//! use rust_bert::pipelines::sequence_classification::SequenceClassificationConfig;
//! use rust_bert::resources::{RemoteResource, Resource};
//! use rust_bert::distilbert::{DistilBertModelResources, DistilBertVocabResources, DistilBertConfigResources};
//! use rust_bert::pipelines::sequence_classification::SequenceClassificationModel;
//! use rust_bert::pipelines::common::ModelType;
//!# fn main() -> failure::Fallible<()> {
//!
//! //Load a configuration
//! let config = SequenceClassificationConfig::new(ModelType::DistilBert,
//!    Resource::Remote(RemoteResource::from_pretrained(DistilBertModelResources::DISTIL_BERT_SST2)),
//!    Resource::Remote(RemoteResource::from_pretrained(DistilBertVocabResources::DISTIL_BERT_SST2)),
//!    Resource::Remote(RemoteResource::from_pretrained(DistilBertConfigResources::DISTIL_BERT_SST2)),
//!    None, //merges resource only relevant with ModelType::Roberta
//!    true, //lowercase
//! );
//!
//! //Create the model
//! let sequence_classification_model = SequenceClassificationModel::new(config)?;
//!
//! let input = [
//!     "Probably my all-time favorite movie, a story of selflessness, sacrifice and dedication to a noble cause, but it's not preachy or boring.",
//!     "This film tried to be too many things all at once: stinging political satire, Hollywood blockbuster, sappy romantic comedy, family values promo...",
//!     "If you like original gut wrenching laughter you will like this movie. If you are young or old then you will love this movie, hell even my mom liked it.",
//! ];
//! let output = sequence_classification_model.predict(&input);
//!# Ok(())
//!# }
//! ```
//! (Example courtesy of [IMDb](http://www.imdb.com))
//!
//! Output: \
//! ```no_run
//!# use rust_bert::pipelines::sequence_classification::Label;
//! let output =
//! [
//!    Label { text: String::from("POSITIVE"), score: 0.9986, id: 1, sentence: 0},
//!    Label { text: String::from("NEGATIVE"), score: 0.9985, id: 0, sentence: 1},
//!    Label { text: String::from("POSITIVE"), score: 0.9988, id: 1, sentence: 12},
//! ]
//!# ;
//! ```
//!
use tch::nn::VarStore;
use rust_tokenizers::preprocessing::tokenizer::base_tokenizer::{TokenizedInput, TruncationStrategy};
use std::collections::HashMap;
use tch::{Tensor, no_grad, Device, Kind};
use crate::bert::BertForSequenceClassification;
use crate::roberta::RobertaForSequenceClassification;
use crate::distilbert::{DistilBertModelResources, DistilBertConfigResources, DistilBertVocabResources, DistilBertModelClassifier};
use crate::common::resources::{Resource, RemoteResource, download_resource};
use serde::{Serialize, Deserialize};
use crate::pipelines::common::{ModelType, ConfigOption, TokenizerOption};


#[derive(Debug, Serialize, Deserialize)]
/// # Label generated by a `SequenceClassificationModel`
pub struct Label {
    /// Label String representation
    pub text: String,
    /// Confidence score
    pub score: f64,
    /// Label ID
    pub id: i64,
    /// Sentence index
    #[serde(default)]
    pub sentence: usize,
}

/// # Configuration for SequenceClassificationModel
/// Contains information regarding the model to load and device to place the model on.
pub struct SequenceClassificationConfig {
    /// Model type
    pub model_type: ModelType,
    /// Model weights resource (default: pretrained BERT model on CoNLL)
    pub model_resource: Resource,
    /// Config resource (default: pretrained BERT model on CoNLL)
    pub config_resource: Resource,
    /// Vocab resource (default: pretrained BERT model on CoNLL)
    pub vocab_resource: Resource,
    /// Merges resource (default: pretrained BERT model on CoNLL)
    pub merges_resource: Option<Resource>,
    /// Automatically lower case all input upon tokenization (assumes a lower-cased model)
    pub lower_case: bool,
    /// Device to place the model on (default: CUDA/GPU when available)
    pub device: Device,
}

impl SequenceClassificationConfig {
    /// Instantiate a new sequence classification configuration of the supplied type.
    ///
    /// # Arguments
    ///
    /// * `model_type` - `ModelType` indicating the model type to load (must match with the actual data to be loaded!)
    /// * model - The `Resource` pointing to the model to load (e.g.  model.ot)
    /// * config - The `Resource' pointing to the model configuration to load (e.g. config.json)
    /// * vocab - The `Resource' pointing to the tokenizer's vocabulary to load (e.g.  vocab.txt/vocab.json)
    /// * vocab - An optional `Resource` tuple (`Option<Resource>`) pointing to the tokenizer's merge file to load (e.g.  merges.txt), needed only for Roberta.
    /// * lower_case - A `bool' indicating whether the tokeniser should lower case all input (in case of a lower-cased model)
    ///
    pub fn new(model_type: ModelType, model_resource: Resource, config_resource: Resource, vocab_resource: Resource, merges_resource: Option<Resource>, lower_case: bool) -> SequenceClassificationConfig {
        SequenceClassificationConfig {
            model_type,
            model_resource,
            config_resource,
            vocab_resource,
            merges_resource,
            lower_case,
            device: Device::cuda_if_available(),
        }
    }
}

impl Default for SequenceClassificationConfig {
    /// Provides a defaultSST-2 sentiment analysis model (English)
    fn default() -> SequenceClassificationConfig {
        SequenceClassificationConfig {
            model_type: ModelType::DistilBert,
            model_resource: Resource::Remote(RemoteResource::from_pretrained(DistilBertModelResources::DISTIL_BERT_SST2)),
            config_resource: Resource::Remote(RemoteResource::from_pretrained(DistilBertConfigResources::DISTIL_BERT_SST2)),
            vocab_resource: Resource::Remote(RemoteResource::from_pretrained(DistilBertVocabResources::DISTIL_BERT_SST2)),
            merges_resource: None,
            lower_case: true,
            device: Device::cuda_if_available(),
        }
    }
}

/// # Abstraction that holds one particular token sequence classifier model, for any of the supported models
pub enum SequenceClassificationOption {
    /// Bert for Sequence Classification
    Bert(BertForSequenceClassification),
    /// DistilBert for Sequence Classification
    DistilBert(DistilBertModelClassifier),
    /// Roberta for Sequence Classification
    Roberta(RobertaForSequenceClassification),
}

impl SequenceClassificationOption {
    /// Instantiate a new sequence classification model of the supplied type.
    ///
    /// # Arguments
    ///
    /// * `model_type` - `ModelType` indicating the model type to load (must match with the actual data to be loaded)
    /// * `p` - `tch::nn::Path` path to the model file to load (e.g. model.ot)
    /// * `config` - A configuration (the model type of the configuration must be compatible with the value for
    /// `model_type`)
    ///
    pub fn new(model_type: ModelType, p: &tch::nn::Path, config: &ConfigOption) -> Self {
        match model_type {
            ModelType::Bert => {
                if let ConfigOption::Bert(config) = config {
                    SequenceClassificationOption::Bert(BertForSequenceClassification::new(p, config))
                } else {
                    panic!("You can only supply a BertConfig for Bert!");
                }
            }
            ModelType::DistilBert => {
                if let ConfigOption::DistilBert(config) = config {
                    SequenceClassificationOption::DistilBert(DistilBertModelClassifier::new(p, config))
                } else {
                    panic!("You can only supply a DistilBertConfig for DistilBert!");
                }
            }
            ModelType::Roberta => {
                if let ConfigOption::Bert(config) = config {
                    SequenceClassificationOption::Roberta(RobertaForSequenceClassification::new(p, config))
                } else {
                    panic!("You can only supply a BertConfig for Roberta!");
                }
            }
            ModelType::Electra => { panic!("SequenceClassification not implemented for Electra!"); }
        }
    }

    /// Returns the `ModelType` for this SequenceClassificationOption
    pub fn model_type(&self) -> ModelType {
        match *self {
            Self::Bert(_) => ModelType::Bert,
            Self::Roberta(_) => ModelType::Roberta,
            Self::DistilBert(_) => ModelType::DistilBert
        }
    }

    /// Interface method to forward_t() of the particular models.
    pub fn forward_t(&self,
                     input_ids: Option<Tensor>,
                     mask: Option<Tensor>,
                     token_type_ids: Option<Tensor>,
                     position_ids: Option<Tensor>,
                     input_embeds: Option<Tensor>,
                     train: bool) -> (Tensor, Option<Vec<Tensor>>, Option<Vec<Tensor>>) {
        match *self {
            Self::Bert(ref model) => model.forward_t(input_ids, mask, token_type_ids, position_ids, input_embeds, train),
            Self::DistilBert(ref model) => model.forward_t(input_ids, mask, input_embeds, train).expect("Error in distilbert forward_t"),
            Self::Roberta(ref model) => model.forward_t(input_ids, mask, token_type_ids, position_ids, input_embeds, train),
        }
    }
}


/// # SequenceClassificationModel for Classification (e.g. Sentiment Analysis)
pub struct SequenceClassificationModel {
    tokenizer: TokenizerOption,
    sequence_classifier: SequenceClassificationOption,
    label_mapping: HashMap<i64, String>,
    var_store: VarStore,
}

impl SequenceClassificationModel {
    /// Build a new `SequenceClassificationModel`
    ///
    /// # Arguments
    ///
    /// * `config` - `SequenceClassificationConfig` object containing the resource references (model, vocabulary, configuration) and device placement (CPU/GPU)
    ///
    /// # Example
    ///
    /// ```no_run
    ///# fn main() -> failure::Fallible<()> {
    /// use rust_bert::pipelines::sequence_classification::SequenceClassificationModel;
    ///
    /// let model = SequenceClassificationModel::new(Default::default())?;
    ///# Ok(())
    ///# }
    /// ```
    ///
    pub fn new(config: SequenceClassificationConfig) -> failure::Fallible<SequenceClassificationModel> {
        let config_path = download_resource(&config.config_resource)?;
        let vocab_path = download_resource(&config.vocab_resource)?;
        let weights_path = download_resource(&config.model_resource)?;
        let merges_path = if let Some(merges_resource) = &config.merges_resource {
            Some(download_resource(merges_resource).expect("Failure downloading resource"))
        } else {
            None
        };
        let device = config.device;

        let tokenizer = TokenizerOption::from_file(config.model_type, vocab_path.to_str().unwrap(), merges_path.map(|path| path.to_str().unwrap()), config.lower_case);
        let mut var_store = VarStore::new(device);
        let model_config = ConfigOption::from_file(config.model_type, config_path);
        let sequence_classifier = SequenceClassificationOption::new(config.model_type, &var_store.root(), &model_config);
        let label_mapping = model_config.get_label_mapping();
        var_store.load(weights_path)?;
        Ok(SequenceClassificationModel { tokenizer, sequence_classifier, label_mapping, var_store })
    }

    fn prepare_for_model(&self, input: Vec<&str>) -> Tensor {
        let tokenized_input: Vec<TokenizedInput> = self.tokenizer.encode_list(input.to_vec(),
                                                                              128,
                                                                              &TruncationStrategy::LongestFirst,
                                                                              0);
        let max_len = tokenized_input.iter().map(|input| input.token_ids.len()).max().unwrap();
        let tokenized_input_tensors: Vec<tch::Tensor> = tokenized_input.
            iter().
            map(|input| input.token_ids.clone()).
            map(|mut input| {
                input.extend(vec![0; max_len - input.len()]);
                input
            }).
            map(|input|
                Tensor::of_slice(&(input))).
            collect::<Vec<_>>();
        Tensor::stack(tokenized_input_tensors.as_slice(), 0).to(self.var_store.device())
    }

    /// Classify texts
    ///
    /// # Arguments
    ///
    /// * `input` - `&[&str]` Array of texts to classify.
    ///
    /// # Returns
    ///
    /// * `Vec<Label>` containing labels for input texts
    ///
    /// # Example
    ///
    /// ```no_run
    ///# fn main() -> failure::Fallible<()> {
    ///# use rust_bert::pipelines::sequence_classification::SequenceClassificationModel;
    ///
    /// let sequence_classification_model =  SequenceClassificationModel::new(Default::default())?;
    /// let input = [
    ///     "Probably my all-time favorite movie, a story of selflessness, sacrifice and dedication to a noble cause, but it's not preachy or boring.",
    ///     "This film tried to be too many things all at once: stinging political satire, Hollywood blockbuster, sappy romantic comedy, family values promo...",
    ///     "If you like original gut wrenching laughter you will like this movie. If you are young or old then you will love this movie, hell even my mom liked it.",
    /// ];
    /// let output = sequence_classification_model.predict(&input);
    ///# Ok(())
    ///# }
    /// ```
    pub fn predict(&self, input: &[&str]) -> Vec<Label> {
        let input_tensor = self.prepare_for_model(input.to_vec());
        let output = no_grad(|| {
            let (output, _, _) = self.sequence_classifier
                .forward_t(Some(input_tensor.copy()),
                           None,
                           None,
                           None,
                           None,
                           false);
            output.softmax(-1, Kind::Float).detach().to(Device::Cpu)
        });
        let label_indices = output.as_ref().argmax(-1, true).squeeze();
        let scores = output.gather(1, &label_indices.unsqueeze(1), false).squeeze();
        let label_indices = label_indices.iter::<i64>().unwrap().collect::<Vec<i64>>();
        let scores = scores.iter::<f64>().unwrap().collect::<Vec<f64>>();

        let mut labels: Vec<Label> = vec!();
        for sentence_idx in 0..label_indices.len() {
            let label_string = self.label_mapping.get(&label_indices[sentence_idx]).unwrap().clone();
            let label = Label {
                text: label_string,
                score: scores[sentence_idx],
                id: label_indices[sentence_idx],
                sentence: sentence_idx,
            };
            labels.push(label)
        }
        labels
    }

    /// Multi-label classification of texts
    ///
    /// # Arguments
    ///
    /// * `input` - `&[&str]` Array of texts to classify.
    /// * `threshold` - `f64` threshold above which a label will be considered true by the classifier
    ///
    /// # Returns
    ///
    /// * `Vec<Vec<Label>>` containing a vector of true labels for each input text
    ///
    /// # Example
    ///
    /// ```no_run
    ///# fn main() -> failure::Fallible<()> {
    ///# use rust_bert::pipelines::sequence_classification::SequenceClassificationModel;
    ///
    /// let sequence_classification_model =  SequenceClassificationModel::new(Default::default())?;
    /// let input = [
    ///     "Probably my all-time favorite movie, a story of selflessness, sacrifice and dedication to a noble cause, but it's not preachy or boring.",
    ///     "This film tried to be too many things all at once: stinging political satire, Hollywood blockbuster, sappy romantic comedy, family values promo...",
    ///     "If you like original gut wrenching laughter you will like this movie. If you are young or old then you will love this movie, hell even my mom liked it.",
    /// ];
    /// let output = sequence_classification_model.predict_multilabel(&input, 0.5);
    ///# Ok(())
    ///# }
    /// ```
    pub fn predict_multilabel(&self, input: &[&str], threshold: f64) -> Vec<Vec<Label>> {
        let input_tensor = self.prepare_for_model(input.to_vec());
        let output = no_grad(|| {
            let (output, _, _) = self.sequence_classifier
                .forward_t(Some(input_tensor.copy()),
                           None,
                           None,
                           None,
                           None,
                           false);
            output.sigmoid().detach().to(Device::Cpu)
        });
        let label_indices = output.as_ref().ge(threshold).nonzero();

        let mut labels: Vec<Vec<Label>> = vec!();
        let mut sequence_labels: Vec<Label> = vec!();

        for sentence_idx in 0..label_indices.size()[0] {

            let label_index_tensor = label_indices.get(sentence_idx);
            let sentence_label = label_index_tensor.iter::<i64>().unwrap().collect::<Vec<i64>>();
            let (sentence, id) = (sentence_label[0], sentence_label[1]);
            if sentence as usize > labels.len() {
                labels.push(sequence_labels);
                sequence_labels = vec!();
            }
            let score = output.double_value(sentence_label.as_slice());
            let label_string = self.label_mapping.get(&id).unwrap().to_owned();
            let label = Label {
                text: label_string,
                score,
                id,
                sentence: sentence as usize,
            };
            sequence_labels.push(label);

        }
        if sequence_labels.len() > 0 {
            labels.push(sequence_labels);
        }
        labels
    }
}