llm-tokenizer 1.3.1

use std::collections::HashMap;

use anyhow::{Error, Result};
use tokenizers::{
    processors::template::TemplateProcessing,
    tokenizer::{step_decode_stream, Tokenizer as HfTokenizer},
};
use tracing::debug;

use crate::{
    chat_template::{
        load_chat_template_from_file, ChatTemplateContentFormat, ChatTemplateParams,
        ChatTemplateState,
    },
    traits::{Decoder, Encoder, Encoding, SpecialTokens, TokenIdType, Tokenizer as TokenizerTrait},
};

/// HuggingFace tokenizer wrapper
pub struct HuggingFaceTokenizer {
    tokenizer: HfTokenizer,
    special_tokens: SpecialTokens,
    vocab: HashMap<String, TokenIdType>,
    reverse_vocab: HashMap<TokenIdType, String>,
    chat_template: ChatTemplateState,
}

impl HuggingFaceTokenizer {
    /// Create a tokenizer from a HuggingFace tokenizer JSON file
    pub fn from_file(file_path: &str) -> Result<Self> {
        // Try to auto-discover chat template if not explicitly provided
        let path = std::path::Path::new(file_path);
        let chat_template_path = path
            .parent()
            .and_then(crate::factory::discover_chat_template_in_dir);
        Self::from_file_with_chat_template(file_path, chat_template_path.as_deref())
    }

    /// Create a tokenizer from a HuggingFace tokenizer JSON file with an optional chat template
    pub fn from_file_with_chat_template(
        file_path: &str,
        chat_template_path: Option<&str>,
    ) -> Result<Self> {
        let mut tokenizer = HfTokenizer::from_file(file_path)
            .map_err(|e| Error::msg(format!("Failed to load tokenizer: {e}")))?;

        // Build vocab mappings (include special tokens to get added_tokens like <|im_start|>)
        let vocab = tokenizer.get_vocab(true); // true = include special tokens and added_tokens
        let reverse_vocab: HashMap<TokenIdType, String> = vocab
            .iter()
            .map(|(token, &id)| (id, token.clone()))
            .collect();

        // Load tokenizer_config.json once for chat template, add_bos/eos, and special tokens
        let config_result = Self::load_chat_template_and_config(file_path);
        let mut chat_template_str = config_result.chat_template;
        let add_bos_token = config_result.add_bos_token;
        let add_eos_token = config_result.add_eos_token;

        // Extract special tokens — config values override vocab pattern matching
        let special_tokens = Self::extract_special_tokens(&tokenizer, &config_result.config_tokens);

        if let Some(template_path) = chat_template_path {
            chat_template_str = load_chat_template_from_file(template_path)?;
        }

        // Configure post_processor based on tokenizer_config.json (matches Python transformers)
        // Only modify when at least one setting is explicitly true
        let needs_eos = add_eos_token == Some(true);
        let needs_bos = match add_bos_token {
            Some(true) => true,
            Some(false) => false,
            // Not set: preserve existing behavior from tokenizer.json
            None => needs_eos && Self::tokenizer_adds_special_tokens(&tokenizer),
        };

        if needs_bos || needs_eos {
            if let Some(post_processor) =
                Self::build_post_processor(needs_bos, needs_eos, &special_tokens, &vocab)
            {
                debug!(needs_bos, needs_eos, "Configured post_processor");
                tokenizer.with_post_processor(Some(post_processor));
            }
        }

        Ok(HuggingFaceTokenizer {
            tokenizer,
            special_tokens,
            vocab,
            reverse_vocab,
            chat_template: ChatTemplateState::new(chat_template_str)?,
        })
    }

    /// Check if the tokenizer's post_processor adds special tokens (e.g., BOS)
    fn tokenizer_adds_special_tokens(tokenizer: &HfTokenizer) -> bool {
        tokenizer
            .encode("", true)
            .map(|enc| !enc.get_ids().is_empty())
            .unwrap_or(false)
    }

    /// Build a TemplateProcessing post_processor (matches Python transformers' update_post_processor)
    /// Template format: "{bos}:0 $A:0 {eos}:0" with optional BOS/EOS based on config
    fn build_post_processor(
        add_bos_token: bool,
        add_eos_token: bool,
        special_tokens: &SpecialTokens,
        vocab: &HashMap<String, TokenIdType>,
    ) -> Option<TemplateProcessing> {
        // Build template string exactly like Python:
        // single = f"{(bos + ':0 ') if add_bos_token else ''}$A:0{(' ' + eos + ':0') if add_eos_token else ''}"
        let mut template = String::with_capacity(32);
        let mut tokens = Vec::with_capacity(2);

        if add_bos_token {
            let bos = special_tokens.bos_token.as_ref()?;
            let bos_id = vocab.get(bos).copied()?;
            template.push_str(bos);
            template.push_str(":0 ");
            tokens.push((bos.clone(), bos_id));
        }

        template.push_str("$A:0");

        if add_eos_token {
            let eos = special_tokens.eos_token.as_ref()?;
            let eos_id = vocab.get(eos).copied()?;
            template.push(' ');
            template.push_str(eos);
            template.push_str(":0");
            tokens.push((eos.clone(), eos_id));
        }

        TemplateProcessing::builder()
            .try_single(template.as_str())
            .ok()?
            .special_tokens(tokens)
            .build()
            .ok()
    }

    /// Create from an existing HuggingFace tokenizer
    pub fn from_tokenizer(tokenizer: HfTokenizer) -> Self {
        let special_tokens = Self::extract_special_tokens(&tokenizer, &ConfigTokens::default());
        let vocab = tokenizer.get_vocab(true); // true = include special tokens and added_tokens
        let reverse_vocab: HashMap<TokenIdType, String> = vocab
            .iter()
            .map(|(token, &id)| (id, token.clone()))
            .collect();

        HuggingFaceTokenizer {
            tokenizer,
            special_tokens,
            vocab,
            reverse_vocab,
            chat_template: ChatTemplateState::empty(),
        }
    }

    /// Extract special tokens from the tokenizer, using config values when available.
    ///
    /// Prefers explicit values from `tokenizer_config.json` (e.g., `"bos_token": "<|begin_of_text|>"`)
    /// over pattern matching against the vocabulary, since models like Llama 4 use non-standard
    /// token names that aren't in the hardcoded pattern list.
    fn extract_special_tokens(
        tokenizer: &HfTokenizer,
        config_tokens: &ConfigTokens,
    ) -> SpecialTokens {
        // Get vocab with special tokens included (added_tokens like <|im_start|>)
        let vocab = tokenizer.get_vocab(true);

        let find_token = |patterns: &[&str]| -> Option<String> {
            for pattern in patterns {
                if vocab.contains_key(*pattern) {
                    return Some((*pattern).to_string());
                }
            }
            None
        };

        // Extract additional special tokens using the tokenizers library API
        let additional_special_tokens: Vec<String> = tokenizer
            .get_added_tokens_decoder()
            .iter()
            .filter(|(_id, token)| token.special)
            .map(|(_id, token)| token.content.clone())
            .collect();

        // Config values take priority over pattern matching
        SpecialTokens {
            bos_token: config_tokens
                .bos_token
                .clone()
                .or_else(|| find_token(&["<s>", "<|startoftext|>", "<BOS>", "[CLS]"])),
            eos_token: config_tokens
                .eos_token
                .clone()
                .or_else(|| find_token(&["</s>", "<|endoftext|>", "<EOS>", "[SEP]"])),
            unk_token: config_tokens
                .unk_token
                .clone()
                .or_else(|| find_token(&["<unk>", "<UNK>", "[UNK]"])),
            sep_token: find_token(&["[SEP]", "<sep>", "<SEP>"]),
            pad_token: config_tokens
                .pad_token
                .clone()
                .or_else(|| find_token(&["<pad>", "<PAD>", "[PAD]"])),
            cls_token: find_token(&["[CLS]", "<cls>", "<CLS>"]),
            mask_token: find_token(&["[MASK]", "<mask>", "<MASK>"]),
            additional_special_tokens,
        }
    }

    /// Load chat template, special token settings, and token strings from tokenizer_config.json.
    /// Reads the file once and extracts everything needed by the tokenizer constructor.
    fn load_chat_template_and_config(tokenizer_path: &str) -> TokenizerConfigResult {
        (|| {
            let path = std::path::Path::new(tokenizer_path);
            let config_path = path.parent()?.join("tokenizer_config.json");

            if !config_path.exists() {
                return None;
            }

            let content = std::fs::read_to_string(&config_path).ok()?;
            let config: serde_json::Value = serde_json::from_str(&content).ok()?;

            // Extract chat template directly from parsed config (avoid re-reading the file)
            let chat_template = config
                .get("chat_template")
                .and_then(|v| v.as_str())
                .map(String::from);

            let add_bos_token = config.get("add_bos_token").and_then(|v| v.as_bool());
            let add_eos_token = config.get("add_eos_token").and_then(|v| v.as_bool());

            // Extract special token strings (handles both "string" and {"content": "string"})
            let get_token = |key: &str| -> Option<String> {
                config.get(key).and_then(|v| {
                    v.as_str()
                        .map(String::from)
                        .or_else(|| v.get("content").and_then(|c| c.as_str()).map(String::from))
                })
            };

            let config_tokens = ConfigTokens {
                bos_token: get_token("bos_token"),
                eos_token: get_token("eos_token"),
                unk_token: get_token("unk_token"),
                pad_token: get_token("pad_token"),
            };

            Some(TokenizerConfigResult {
                chat_template,
                add_bos_token,
                add_eos_token,
                config_tokens,
            })
        })()
        .unwrap_or_default()
    }
}

/// Special token strings read from tokenizer_config.json.
#[derive(Default)]
struct ConfigTokens {
    bos_token: Option<String>,
    eos_token: Option<String>,
    unk_token: Option<String>,
    pad_token: Option<String>,
}

/// Result of parsing tokenizer_config.json.
#[derive(Default)]
struct TokenizerConfigResult {
    chat_template: Option<String>,
    add_bos_token: Option<bool>,
    add_eos_token: Option<bool>,
    config_tokens: ConfigTokens,
}

impl Encoder for HuggingFaceTokenizer {
    fn encode(&self, input: &str, add_special_tokens: bool) -> Result<Encoding> {
        self.tokenizer
            .encode(input, add_special_tokens)
            .map_err(|e| Error::msg(format!("Encoding failed: {e}")))
            .map(|encoding| Encoding::Hf(Box::new(encoding)))
    }

    fn encode_batch(&self, inputs: &[&str], add_special_tokens: bool) -> Result<Vec<Encoding>> {
        self.tokenizer
            .encode_batch(inputs.to_vec(), add_special_tokens)
            .map_err(|e| Error::msg(format!("Batch encoding failed: {e}")))
            .map(|encodings| {
                encodings
                    .into_iter()
                    .map(|e| Encoding::Hf(Box::new(e)))
                    .collect()
            })
    }
}

impl Decoder for HuggingFaceTokenizer {
    fn decode(&self, token_ids: &[TokenIdType], skip_special_tokens: bool) -> Result<String> {
        self.tokenizer
            .decode(token_ids, skip_special_tokens)
            .map_err(|e| Error::msg(format!("Decoding failed: {e}")))
    }

    /// Native incremental decode using the HF `step_decode_stream`.
    ///
    /// This delegates to the same algorithm the default trait method uses, but
    /// the two internal `decode()` calls go directly through the concrete
    /// `TokenizerImpl` rather than through `dyn Decoder` vtable dispatch.
    fn decode_step(
        &self,
        token_id: TokenIdType,
        ids: &mut Vec<TokenIdType>,
        prefix: &mut String,
        prefix_index: &mut usize,
        skip_special_tokens: bool,
    ) -> Result<Option<String>> {
        step_decode_stream(
            &self.tokenizer,
            vec![token_id],
            skip_special_tokens,
            ids,
            prefix,
            prefix_index,
        )
        .map_err(|e| Error::msg(format!("Decode stream error: {e}")))
    }
}

impl TokenizerTrait for HuggingFaceTokenizer {
    fn vocab_size(&self) -> usize {
        self.tokenizer.get_vocab_size(false)
    }

    fn get_special_tokens(&self) -> &SpecialTokens {
        &self.special_tokens
    }

    fn token_to_id(&self, token: &str) -> Option<TokenIdType> {
        self.vocab.get(token).copied()
    }

    fn id_to_token(&self, id: TokenIdType) -> Option<String> {
        self.reverse_vocab.get(&id).cloned()
    }

    fn as_any(&self) -> &dyn std::any::Any {
        self
    }

    fn apply_chat_template(
        &self,
        messages: &[serde_json::Value],
        params: ChatTemplateParams,
    ) -> Result<String> {
        // Inject special tokens if the caller didn't provide them
        if params.special_tokens.is_some() {
            return self.chat_template.apply(messages, params);
        }
        let params = ChatTemplateParams {
            special_tokens: Some(&self.special_tokens),
            ..params
        };
        self.chat_template.apply(messages, params)
    }

    fn chat_template_content_format(&self) -> ChatTemplateContentFormat {
        self.chat_template.content_format()
    }

    fn set_chat_template(&mut self, template: String) -> Result<()> {
        self.chat_template.set(template)
    }
}