agent-code-lib 0.16.1

//! Token estimation.
//!
//! Estimates token counts for messages and content blocks using a
//! character-based heuristic. Uses actual API usage data when
//! available, falling back to rough estimation for new messages.
//!
//! Default ratio: 4 bytes per token (conservative for most content).

use crate::llm::message::{ContentBlock, Message};

/// Default bytes per token for estimation.
const BYTES_PER_TOKEN: f64 = 4.0;

/// Fixed token estimate for image content blocks.
const IMAGE_TOKEN_ESTIMATE: u64 = 2000;

/// Estimate token count from a string.
pub fn estimate_tokens(content: &str) -> u64 {
    (content.len() as f64 / BYTES_PER_TOKEN).round() as u64
}

/// Estimate tokens for a single content block.
pub fn estimate_block_tokens(block: &ContentBlock) -> u64 {
    match block {
        ContentBlock::Text { text } => estimate_tokens(text),
        ContentBlock::ToolUse { name, input, .. } => {
            let input_str = serde_json::to_string(input).unwrap_or_default();
            estimate_tokens(name) + estimate_tokens(&input_str)
        }
        ContentBlock::ToolResult { content, .. } => estimate_tokens(content),
        ContentBlock::Thinking { thinking, .. } => estimate_tokens(thinking),
        ContentBlock::Image { .. } => IMAGE_TOKEN_ESTIMATE,
        ContentBlock::Document { data, .. } => {
            // Base64-encoded documents: estimate from decoded size.
            let decoded_size = (data.len() as f64 * 0.75) as u64;
            (decoded_size as f64 / BYTES_PER_TOKEN).round() as u64
        }
    }
}

/// Estimate tokens for a single message.
pub fn estimate_message_tokens(msg: &Message) -> u64 {
    match msg {
        Message::User(u) => {
            // Per-message overhead (role, formatting).
            let overhead = 4;
            let content: u64 = u.content.iter().map(estimate_block_tokens).sum();
            overhead + content
        }
        Message::Assistant(a) => {
            let overhead = 4;
            let content: u64 = a.content.iter().map(estimate_block_tokens).sum();
            overhead + content
        }
        Message::System(s) => {
            let overhead = 4;
            overhead + estimate_tokens(&s.content)
        }
    }
}

/// Estimate total context tokens for a message history.
///
/// Uses a hybrid approach: actual API usage counts for the most
/// recent assistant response, plus rough estimation for any
/// messages added after that point.
pub fn estimate_context_tokens(messages: &[Message]) -> u64 {
    if messages.is_empty() {
        return 0;
    }

    // Find the most recent assistant message with usage data.
    let mut last_usage_idx = None;
    for (i, msg) in messages.iter().enumerate().rev() {
        if let Message::Assistant(a) = msg
            && a.usage.is_some()
        {
            last_usage_idx = Some(i);
            break;
        }
    }

    match last_usage_idx {
        Some(idx) => {
            // Use actual API token count up to this point.
            let usage = messages[idx]
                .as_assistant()
                .and_then(|a| a.usage.as_ref())
                .unwrap();
            let api_tokens = usage.total();

            // Estimate tokens for messages added after the API call.
            let new_tokens: u64 = messages[idx + 1..]
                .iter()
                .map(estimate_message_tokens)
                .sum();

            api_tokens + new_tokens
        }
        None => {
            // No API usage data — estimate everything.
            messages.iter().map(estimate_message_tokens).sum()
        }
    }
}

/// Get the context window size for a model.
pub fn context_window_for_model(model: &str) -> u64 {
    let lower = model.to_lowercase();

    // Check for extended context variants first.
    if lower.contains("1m") || lower.contains("1000k") {
        return 1_000_000;
    }

    if lower.contains("opus") || lower.contains("sonnet") || lower.contains("haiku") {
        200_000
    } else if lower.contains("gpt-4") {
        128_000
    } else if lower.contains("gpt-3.5") {
        16_384
    } else {
        128_000
    }
}

/// Get the max output tokens for a model.
pub fn max_output_tokens_for_model(model: &str) -> u64 {
    let lower = model.to_lowercase();
    if lower.contains("opus") || lower.contains("sonnet") {
        16_384
    } else if lower.contains("haiku") {
        8_192
    } else {
        16_384
    }
}

/// Get the maximum thinking token budget for a model.
pub fn max_thinking_tokens_for_model(model: &str) -> u64 {
    let lower = model.to_lowercase();
    if lower.contains("opus") {
        32_000
    } else if lower.contains("sonnet") {
        16_000
    } else if lower.contains("haiku") {
        8_000
    } else {
        16_000
    }
}

// Helper to extract assistant message ref.
trait AsAssistant {
    fn as_assistant(&self) -> Option<&crate::llm::message::AssistantMessage>;
}

impl AsAssistant for Message {
    fn as_assistant(&self) -> Option<&crate::llm::message::AssistantMessage> {
        match self {
            Message::Assistant(a) => Some(a),
            _ => None,
        }
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_estimate_tokens() {
        // 100 chars / 4 = 25 tokens.
        let text = "a".repeat(100);
        assert_eq!(estimate_tokens(&text), 25);
    }

    #[test]
    fn test_empty_messages() {
        assert_eq!(estimate_context_tokens(&[]), 0);
    }

    #[test]
    fn test_estimate_block_tokens_text() {
        let block = ContentBlock::Text {
            text: "a".repeat(400),
        };
        assert_eq!(estimate_block_tokens(&block), 100);
    }

    #[test]
    fn test_estimate_block_tokens_image() {
        let block = ContentBlock::Image {
            media_type: "image/png".into(),
            data: "base64data".into(),
        };
        assert_eq!(estimate_block_tokens(&block), IMAGE_TOKEN_ESTIMATE);
    }

    #[test]
    fn test_estimate_block_tokens_tool_use() {
        let block = ContentBlock::ToolUse {
            id: "call_1".into(),
            name: "Bash".into(),
            input: serde_json::json!({"command": "ls"}),
        };
        let tokens = estimate_block_tokens(&block);
        assert!(tokens > 0);
    }

    #[test]
    fn test_estimate_message_tokens() {
        let msg = crate::llm::message::user_message("hello world");
        let tokens = estimate_message_tokens(&msg);
        // 11 chars / 4 = ~3, + 4 overhead = ~7
        assert!(tokens >= 5);
    }

    #[test]
    fn test_context_window_for_model() {
        assert_eq!(context_window_for_model("claude-opus-4"), 200_000);
        assert_eq!(context_window_for_model("claude-sonnet-4"), 200_000);
        assert_eq!(context_window_for_model("gpt-4"), 128_000);
        assert_eq!(context_window_for_model("claude-sonnet-1m"), 1_000_000);
    }

    #[test]
    fn test_max_output_tokens() {
        assert_eq!(max_output_tokens_for_model("claude-opus"), 16_384);
        assert_eq!(max_output_tokens_for_model("claude-haiku"), 8_192);
    }

    #[test]
    fn test_max_thinking_tokens() {
        assert_eq!(max_thinking_tokens_for_model("claude-opus"), 32_000);
        assert_eq!(max_thinking_tokens_for_model("claude-sonnet"), 16_000);
        assert_eq!(max_thinking_tokens_for_model("claude-haiku"), 8_000);
    }

    #[test]
    fn test_estimate_tokens_empty_string() {
        assert_eq!(estimate_tokens(""), 0);
    }

    #[test]
    fn test_estimate_tokens_unicode() {
        // Multi-byte chars: each char may be 2-4 bytes in UTF-8.
        let text = "\u{1F600}\u{1F600}\u{1F600}"; // 3 emoji, 4 bytes each = 12 bytes
        let tokens = estimate_tokens(text);
        // 12 / 4 = 3
        assert_eq!(tokens, 3);
    }

    #[test]
    fn test_estimate_block_tokens_document() {
        let block = ContentBlock::Document {
            media_type: "application/pdf".into(),
            data: "a".repeat(400), // 400 base64 chars -> ~300 decoded bytes -> 300/4 = 75 tokens
            title: Some("test.pdf".into()),
        };
        let tokens = estimate_block_tokens(&block);
        assert!(tokens > 0);
        assert_eq!(tokens, 75);
    }

    #[test]
    fn test_estimate_block_tokens_thinking() {
        let block = ContentBlock::Thinking {
            thinking: "a".repeat(200),
            signature: Some("sig".into()),
        };
        let tokens = estimate_block_tokens(&block);
        // 200 / 4 = 50
        assert_eq!(tokens, 50);
    }

    #[test]
    fn test_estimate_block_tokens_tool_result() {
        let block = ContentBlock::ToolResult {
            tool_use_id: "call_1".into(),
            content: "a".repeat(80),
            is_error: false,
            extra_content: vec![],
        };
        let tokens = estimate_block_tokens(&block);
        // 80 / 4 = 20
        assert_eq!(tokens, 20);
    }

    #[test]
    fn test_estimate_message_tokens_system() {
        let msg = Message::System(crate::llm::message::SystemMessage {
            uuid: uuid::Uuid::new_v4(),
            timestamp: String::new(),
            subtype: crate::llm::message::SystemMessageType::Informational,
            content: "a".repeat(40),
            level: crate::llm::message::MessageLevel::Info,
        });
        let tokens = estimate_message_tokens(&msg);
        // 40/4 = 10 + 4 overhead = 14
        assert_eq!(tokens, 14);
    }

    #[test]
    fn test_estimate_message_tokens_assistant_with_tool_use() {
        let msg = Message::Assistant(crate::llm::message::AssistantMessage {
            uuid: uuid::Uuid::new_v4(),
            timestamp: String::new(),
            content: vec![
                ContentBlock::Text {
                    text: "Let me run that.".into(),
                },
                ContentBlock::ToolUse {
                    id: "call_1".into(),
                    name: "Bash".into(),
                    input: serde_json::json!({"command": "ls"}),
                },
            ],
            model: None,
            usage: None,
            stop_reason: None,
            request_id: None,
        });
        let tokens = estimate_message_tokens(&msg);
        // Should include overhead + text tokens + tool_use tokens
        assert!(tokens > 4);
    }

    #[test]
    fn test_estimate_context_tokens_only_user_messages() {
        let messages = vec![
            crate::llm::message::user_message("hello world"),
            crate::llm::message::user_message("how are you"),
        ];
        let tokens = estimate_context_tokens(&messages);
        // No usage data, so everything is estimated.
        assert!(tokens > 0);
    }

    #[test]
    fn test_context_window_for_gpt35() {
        assert_eq!(context_window_for_model("gpt-3.5-turbo"), 16_384);
    }

    #[test]
    fn test_context_window_for_unknown_model() {
        // Unknown models default to 128K.
        assert_eq!(context_window_for_model("some-unknown-model"), 128_000);
    }

    #[test]
    fn test_context_window_for_1000k_variant() {
        assert_eq!(context_window_for_model("claude-sonnet-1000k"), 1_000_000);
    }

    #[test]
    fn test_max_output_tokens_for_unknown_model() {
        // Unknown models default to 16384.
        assert_eq!(max_output_tokens_for_model("unknown-llm"), 16_384);
    }

    #[test]
    fn test_max_thinking_tokens_for_unknown_model() {
        // Unknown models default to 16000.
        assert_eq!(max_thinking_tokens_for_model("unknown-llm"), 16_000);
    }
}