//! Composition engine for the voice pipeline (D-138, Spike 2).
//!
//! Assembles LLM prompts from NPC data, culture profile, tell state, and
//! base text. Handles occasional injection gating, epistemic marker extraction,
//! and tell-state tone modification.
//!
//! The composition engine controls what goes into each prompt — the model never
//! decides frequency of cultural markers. It either receives the clause or it
//! doesn't.

use rand::Rng;
use rand::SeedableRng;
use rand_chacha::ChaCha8Rng;

use crate::npc::blueprint::CultureProfile;
use crate::npc::tell_state::TellCategory;

/// Content type determines the task verb in the prompt.
#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord, Hash)]
pub enum ContentType {
    /// Spoken dialogue — re-voiced with "Re-voice".
    Dialogue,
    /// Observable behavior description — re-voiced with "Describe".
    Behavior,
    /// Fact-bearing line — numbers, causal chains, denials.
    ///
    /// Factual lines bypass the LLM entirely and are served as base text.
    /// Spike 2 showed 2B models corrupt quantitative content ("14 crates in
    /// bay seven" → "fourteen crates are missing") and invert denials.
    /// The simulation is the truth layer — the LLM only handles register.
    Factual,
}

/// Result of prompt building, including which injections fired.
#[derive(Debug)]
pub struct BuiltPrompt {
    /// The assembled prompt string ready for LLM inference.
    pub prompt: String,
    /// Which occasional injections were included (by index into culture's list).
    pub injections_fired: Vec<usize>,
}

/// Universal rules prefix — format and output constraints only.
/// Worldbuilding and style constraints belong in the culture persona or
/// the TASK section, not here.
const RULES: &str = "\
RULES: Output exactly one line of voiced text. \
No explanation. No options. No markdown. No labels. Stop after one line.\n\n\
OUTPUT CONSTRAINTS:\n\
- Speak in complete sentences. Short ones. Cut words that don't pull weight \
— but keep the sentence structure.\n\
- Do not summarize. Keep all facts from the input. Shorten phrasing, not content.\n\
- Do not add information that is not in the input. No invented details.\n\
- NOT: \"Parts late. Behind.\" YES: \"Parts never came. We're a shift behind.\"";

/// Tell-state tone injectors (D-024 tell taxonomy).
///
/// Each tell category has a concrete syntactic instruction with an example,
/// tuned for 2B model capacity. Behavioral descriptions alone ("a beat late")
/// don't produce differentiated output at this model size — concrete surface
/// patterns are needed.
///
/// Angry has a length-aware variant: on long content (16+ words), the default
/// "make sentences shorter" instruction causes destructive compression that
/// strips facts. Long-Angry instead preserves the full claim and focuses
/// intensity on one sentence.
///
/// ## Iteration history for Friendly and RoutineDeviation
///
/// Spike 2 showed these two tells produce output indistinguishable from neutral
/// on Gemma 2B. Tracked against a 3-iteration budget (#651):
///
/// - **Iteration 1** (Sprint 26): Replaced abstract instructions with concrete
///   surface-pattern examples. Friendly: closing aside pattern. RoutineDeviation:
///   self-correction/echo pattern. Both more imitable for a 2B model.
fn tell_injector(category: TellCategory) -> &'static str {
    match category {
        TellCategory::Nervous => {
            "TONE: Cut one clause from the sentence. Let a phrase trail off with a dash or ellipsis. \
             Example: \"Yeah, it's just — doesn't matter.\" \
             Do not say they seem nervous."
        }
        TellCategory::Angry => {
            "TONE: Make sentences shorter and more deliberate. One word should hit harder than expected. \
             Example: \"Supervisor wants me in early.\" where \"wants\" carries weight. \
             Do not say they seem angry."
        }
        TellCategory::Friendly => {
            // Iteration 1: replaced abstract "add detail" with a concrete closing-aside
            // pattern. 2B models need a recognisable syntactic target, not a description
            // of intent. "actually" / "so that's something" are learnable short tokens.
            "TONE: End with a brief unprompted aside — a phrase the person didn't need to say. \
             Example: \"Parts are in, so that's something.\" or \"Shift's been quiet, actually.\" \
             Do not use praise or warm adjectives. One short beat after the main point."
        }
        TellCategory::Guarded => {
            "TONE: Use formal, precise words. Answer exactly what was asked, nothing extra. \
             Example: \"That's correct.\" instead of \"Yeah, exactly.\" \
             Do not say they seem guarded."
        }
        TellCategory::RoutineDeviation => {
            // Iteration 1: replaced the two-step interrupted-thought pattern (too complex
            // for 2B) with a simpler self-correction / word-echo pattern. The model only
            // needs to repeat a key word or phrase — one concrete surface action.
            "TONE: Repeat a key word or phrase, as if catching mid-thought. \
             Example: \"Logged it. Got it logged, yeah.\" or \"Pressure's — pressure's holding.\" \
             Do not explain. Just the echo."
        }
    }
}

/// Long-content variant for Angry tell. Used when base_text is 16+ words
/// to prevent destructive compression that strips facts.
const ANGRY_LONG: &str = "\
TONE: Keep the full claim intact — do not cut facts. \
Make one sentence land harder than the rest. \
Example: \"Three reports filed. No budget. But they found half a million for the lounge.\" \
Do not say they seem angry.";

/// Whether to use the long-content Angry variant.
fn is_long_content(base_text: &str) -> bool {
    word_count(base_text) >= 16
}

/// Known epistemic markers that must be preserved through re-voicing.
///
/// When the base text contains these phrases, the LLM is instructed to
/// preserve them. Without this, 2B models strip hedges and evidentials,
/// converting "I heard the night crew stopped the line" to
/// "Line tripped twice. What's the plan?" — losing the epistemic framing
/// that is semantically load-bearing for the perception system.
/// Note: all entries must be lowercase — matched against `base_text.to_lowercase()`.
/// The original-case version is reconstructed from the base text for the PRESERVE clause.
const EPISTEMIC_MARKERS: &[&str] = &[
    "i heard",
    "i think",
    "i saw",
    "i noticed",
    "someone told me",
    "they say",
    "apparently",
    "supposedly",
    "might have",
    "could have",
    "seems like",
    "looks like",
];

/// Count words in a string (whitespace-delimited).
fn word_count(s: &str) -> usize {
    s.split_whitespace().count()
}

/// Determine the length tier for tell-variant gating.
///
/// - Short (≤7 words): neutral only — 2B model produces identical output
/// - Medium (8–15 words): 3 variants (neutral, high-affect, guarded)
/// - Long (16+ words): all applicable tells
fn is_short_content(base_text: &str) -> bool {
    word_count(base_text) <= 7
}

/// Extract epistemic markers present in the base text.
fn extract_epistemic_markers(base_text: &str) -> Vec<&'static str> {
    let lower = base_text.to_lowercase();
    EPISTEMIC_MARKERS
        .iter()
        .filter(|marker| lower.contains(*marker))
        .copied()
        .collect()
}

/// Build a complete LLM prompt for re-voicing.
///
/// The composition engine assembles the prompt from:
/// 1. Universal RULES prefix (format constraints, negative injectors)
/// 2. Culture-specific PERSONA block (from `culture.voice_persona`)
/// 3. Culture-specific examples
/// 4. Tell-state tone modifier (only for medium/long content)
/// 5. Epistemic marker protection (example-based, not keyword-list)
/// 6. Occasional injections (imperative, positioned near TASK for 2B attention)
/// 7. TASK + INPUT + repeated RULES reminder + OUTPUT: stop token
///
/// The prompt is structured so that the most important instructions appear
/// both at the start and immediately before OUTPUT: (double-prompt technique).
/// 2B models de-weight early prompt sections; repeating near the end anchors
/// the instructions in the attention window.
///
/// `seed` should be deterministic per (npc_id, content_index, world_seed)
/// so that the same prompt produces the same injection pattern on re-run.
pub fn build_prompt(
    culture: &CultureProfile,
    base_text: &str,
    content_type: ContentType,
    tell_state: Option<TellCategory>,
    seed: u64,
) -> BuiltPrompt {
    let mut parts: Vec<String> = Vec::with_capacity(16);
    let mut injections_fired: Vec<usize> = Vec::new();

    // 1. Universal rules
    parts.push(RULES.to_string());

    // 2. Culture persona
    if let Some(ref persona) = culture.voice_persona {
        parts.push(String::new());
        parts.push(persona.clone());
    }

    // 3. Culture examples
    if !culture.voice_examples.is_empty() {
        parts.push(String::new());
        parts.push("EXAMPLES:".to_string());
        for ex in &culture.voice_examples {
            parts.push(format!("INPUT: {}", ex.input));
            parts.push(format!("OUTPUT: {}", ex.output));
        }
    }

    // 4. Tell-state tone modifier (skip for short content — 2B model can't differentiate)
    if !is_short_content(base_text) {
        if let Some(tell) = tell_state {
            parts.push(String::new());
            // Angry on long content uses a special variant that preserves facts
            if tell == TellCategory::Angry && is_long_content(base_text) {
                parts.push(ANGRY_LONG.to_string());
            } else {
                parts.push(tell_injector(tell).to_string());
            }
        }
    }

    // 5. Epistemic marker protection — example-based, not keyword-list.
    // The old keyword-list approach ("must appear in the output: i heard, might have")
    // caused 2B models to emit markers as comma-separated lists. Example-based
    // integration teaches the model how to weave them into natural speech.
    let markers = extract_epistemic_markers(base_text);
    if !markers.is_empty() {
        parts.push(String::new());
        if markers.len() == 1 {
            parts.push(format!(
                "PRESERVE: The phrase \"{}\" carries specific meaning. \
                 Use it naturally in the output as part of a sentence, not as a label. \
                 Example: \"I heard they stopped the line — twice, apparently.\"",
                markers[0]
            ));
        } else {
            let marker_list = markers.join("\", \"");
            parts.push(format!(
                "PRESERVE: The phrases \"{}\" carry specific meaning. \
                 Weave them naturally into the output sentence. Do not list them. \
                 Example: \"I heard they rerouted it. Might have been last cycle.\"",
                marker_list
            ));
        }
    }

    // 6. Occasional injections — imperative, positioned near TASK for 2B attention.
    // The composition engine already controls frequency — once an injection fires,
    // the model must execute it without discretion.
    //
    // ## Multiple INJECT blocks: known ambiguity
    //
    // When more than one injection fires on the same prompt, the model sees
    // multiple blocks with the same `INJECT:` label and no numeric index:
    //
    //   INJECT: Include the phrase from this example in your output.
    //   INPUT: discovers a critical part is missing
    //   OUTPUT: Void take it. The coupling's not here.
    //
    //   INJECT: Include the phrase from this example in your output.
    //   INPUT: (second injection)
    //   OUTPUT: (second output)
    //
    // 2B models have no mechanism to distinguish them. In practice they tend
    // to honour the last block (recency bias) and may ignore earlier ones.
    // The REMEMBER reminder at the end of the prompt references only the first
    // fired injection, which compounds the asymmetry.
    //
    // Accepted limitation: true multi-injection compliance is out of scope for
    // a 2B model. The low per-injection frequency (typically ≤0.25) means
    // simultaneous fires are rare. Cultures with multiple injections should
    // keep the set small and frequencies low enough that co-firing is a
    // statistical edge case rather than expected behaviour.
    let mut rng = ChaCha8Rng::seed_from_u64(seed);
    for (i, injection) in culture.occasional_injections.iter().enumerate() {
        // Gate off for suppressive tells
        if let Some(tell) = tell_state {
            if injection.suppress_on_tells.contains(&tell) {
                continue;
            }
        }

        if rng.random::<f32>() < injection.frequency {
            parts.push(String::new());
            // Imperative framing — no "when" conditional, just "include this"
            parts.push(format!("INJECT: Include the phrase from this example in your output."));
            if let Some(ref example) = injection.example {
                parts.push(format!("INPUT: {}", example.input));
                parts.push(format!("OUTPUT: {}", example.output));
            } else {
                parts.push(injection.clause.clone());
            }
            injections_fired.push(i);
        }
    }

    // 7. Task + input + repeated rules reminder + output stop token
    let task_verb = match content_type {
        ContentType::Dialogue => {
            "Re-voice the following in this character's voice. \
             Keep all facts. Use complete sentences"
        }
        ContentType::Behavior => {
            "Describe the following action as a third-person observer. \
             Preserve all individual actions in sequence. Do not extract conclusions"
        }
        ContentType::Factual => {
            // Factual lines must be intercepted before reaching build_prompt.
            // If this branch is hit, the caller has a bug.
            unreachable!("ContentType::Factual must not reach build_prompt — bypass at worker")
        }
    };
    parts.push(String::new());
    parts.push(format!("TASK: {}.", task_verb));
    parts.push(format!("INPUT: {}", base_text));

    // Double-prompt: repeat the critical constraints immediately before OUTPUT:
    // to anchor them in the 2B model's attention window.
    let mut reminder = String::from("REMEMBER:");
    reminder.push_str(" Output exactly one line.");
    reminder.push_str(" Keep all facts from the input. Do not invent new details.");
    reminder.push_str(" Complete sentences, not fragments.");
    if !markers.is_empty() {
        reminder.push_str(&format!(
            " Use \"{}\" naturally in the sentence.",
            markers[0]
        ));
    }
    if !injections_fired.is_empty() {
        // Remind about the first fired injection
        if let Some(inj) = culture
            .occasional_injections
            .get(*injections_fired.first().unwrap())
        {
            if let Some(ref ex) = inj.example {
                // Extract the key phrase from the example output
                let phrase = ex.output.split('.').next().unwrap_or(&ex.output);
                reminder.push_str(&format!(" Include a phrase like \"{}\".", phrase));
            }
        }
    }
    parts.push(reminder);
    parts.push("OUTPUT:".to_string());

    BuiltPrompt {
        prompt: parts.join("\n"),
        injections_fired,
    }
}

// ---------------------------------------------------------------------------
// Tests
// ---------------------------------------------------------------------------

#[cfg(test)]
mod tests {
    use super::*;
    use crate::npc::blueprint::{
        CultureProfile, CulturalValues, NamingConventions, OccasionalInjection, SpeechPatterns,
        VoiceExample,
    };
    use crate::npc::PersonalityTrait;

    fn van_maanens_star_culture() -> CultureProfile {
        CultureProfile {
            id: "van-maanens-star".into(),
            name: "Van Maanen's Star Culture".into(),
            description: "Working-class pragmatic".into(),
            naming: NamingConventions {
                style: "compact".into(),
                given_names: vec!["Kael".into()],
                family_names: vec!["Davan".into()],
                family_name_used_socially: false,
            },
            speech: SpeechPatterns {
                register: "direct".into(),
                filler_words: vec!["look".into()],
                greetings: vec!["hey".into()],
                farewells: vec!["shift's calling".into()],
                exclamations: vec!["void take it".into()],
            },
            values: CulturalValues {
                description: "Pragmatic".into(),
                favored_traits: vec![PersonalityTrait::Bold],
                disfavored_traits: vec![PersonalityTrait::Reclusive],
            },
            voice_persona: Some(
                "PERSONA: You are a Van Maanen's Star station worker.\n\
                 1. Be direct. No pleasantries.\n\
                 2. You're working-class and pragmatic."
                    .into(),
            ),
            voice_examples: vec![
                VoiceExample {
                    input: "declines to answer a question".into(),
                    output: "Look, that's not mine to say.".into(),
                },
            ],
            occasional_injections: vec![OccasionalInjection {
                kind: "oath".into(),
                clause: "When something surprises you, use an oath like \"void take it.\"".into(),
                example: Some(VoiceExample {
                    input: "discovers a critical part is missing".into(),
                    output: "Void take it. The coupling's not here.".into(),
                }),
                frequency: 0.25,
                suppress_on_tells: vec![
                    TellCategory::Guarded,
                    TellCategory::RoutineDeviation,
                    TellCategory::Friendly,
                ],
            }],
            behavior_modifiers: vec![],
        }
    }

    fn bare_culture() -> CultureProfile {
        CultureProfile {
            id: "bare".into(),
            name: "Bare Culture".into(),
            description: "No voice data".into(),
            naming: NamingConventions {
                style: "plain".into(),
                given_names: vec![],
                family_names: vec![],
                family_name_used_socially: true,
            },
            speech: SpeechPatterns {
                register: "neutral".into(),
                filler_words: vec![],
                greetings: vec![],
                farewells: vec![],
                exclamations: vec![],
            },
            values: CulturalValues {
                description: "Neutral".into(),
                favored_traits: vec![],
                disfavored_traits: vec![],
            },
            voice_persona: None,
            voice_examples: vec![],
            occasional_injections: vec![],
            behavior_modifiers: vec![],
        }
    }

    #[test]
    fn prompt_contains_rules_prefix() {
        let culture = bare_culture();
        let result = build_prompt(&culture, "Hello.", ContentType::Dialogue, None, 42);
        assert!(result.prompt.contains("RULES:"));
        assert!(result.prompt.contains("No explanation"));
    }

    #[test]
    fn prompt_contains_persona_when_present() {
        let culture = van_maanens_star_culture();
        let result = build_prompt(&culture, "Hello.", ContentType::Dialogue, None, 42);
        assert!(result.prompt.contains("PERSONA: You are a Van Maanen's Star station worker"));
    }

    #[test]
    fn prompt_omits_persona_when_absent() {
        let culture = bare_culture();
        let result = build_prompt(&culture, "Hello.", ContentType::Dialogue, None, 42);
        assert!(!result.prompt.contains("PERSONA:"));
    }

    #[test]
    fn prompt_contains_examples_when_present() {
        let culture = van_maanens_star_culture();
        let result = build_prompt(&culture, "Hello.", ContentType::Dialogue, None, 42);
        assert!(result.prompt.contains("EXAMPLES:"));
        assert!(result.prompt.contains("Look, that's not mine to say."));
    }

    #[test]
    fn dialogue_uses_re_voice_verb() {
        let culture = bare_culture();
        let result = build_prompt(&culture, "Test line.", ContentType::Dialogue, None, 42);
        assert!(result.prompt.contains("TASK: Re-voice"));
    }

    #[test]
    fn behavior_uses_describe_verb() {
        let culture = bare_culture();
        let result = build_prompt(&culture, "walks away", ContentType::Behavior, None, 42);
        assert!(result.prompt.contains("TASK: Describe"));
    }

    #[test]
    fn prompt_ends_with_output_stop_token() {
        let culture = bare_culture();
        let result = build_prompt(&culture, "Test.", ContentType::Dialogue, None, 42);
        assert!(result.prompt.ends_with("OUTPUT:"));
    }

    #[test]
    fn short_content_skips_tell_injector() {
        let culture = bare_culture();
        let result = build_prompt(
            &culture,
            "Inspection's next week.",
            ContentType::Dialogue,
            Some(TellCategory::Nervous),
            42,
        );
        // 3 words — should skip tell injector
        assert!(!result.prompt.contains("TONE:"));
    }

    #[test]
    fn medium_content_includes_tell_injector() {
        let culture = bare_culture();
        let result = build_prompt(
            &culture,
            "The overnight delivery came in clean and we logged everything properly this time around",
            ContentType::Dialogue,
            Some(TellCategory::Nervous),
            42,
        );
        assert!(result.prompt.contains("TONE:"));
        assert!(result.prompt.contains("trail off"));
    }

    #[test]
    fn guarded_tell_suppresses_oath_injection() {
        let culture = van_maanens_star_culture();
        // Run many seeds — none should fire oath with Guarded tell
        for seed in 0..100 {
            let result = build_prompt(
                &culture,
                "Something went wrong with the shipment.",
                ContentType::Dialogue,
                Some(TellCategory::Guarded),
                seed,
            );
            assert!(
                result.injections_fired.is_empty(),
                "Oath injection fired with Guarded tell at seed {}",
                seed
            );
        }
    }

    #[test]
    fn neutral_tell_allows_oath_injection() {
        let culture = van_maanens_star_culture();
        // With enough seeds, at least one should fire (frequency 0.25)
        let fired_count = (0..100)
            .filter(|&seed| {
                let result = build_prompt(
                    &culture,
                    "Something went wrong.",
                    ContentType::Dialogue,
                    None,
                    seed,
                );
                !result.injections_fired.is_empty()
            })
            .count();
        assert!(
            fired_count > 0,
            "Expected at least 1 oath injection in 100 seeds"
        );
        assert!(
            fired_count < 50,
            "Expected fewer than 50 oath injections in 100 seeds (freq=0.25), got {}",
            fired_count
        );
    }

    #[test]
    fn deterministic_injection_for_same_seed() {
        let culture = van_maanens_star_culture();
        let result1 = build_prompt(&culture, "Test.", ContentType::Dialogue, None, 42);
        let result2 = build_prompt(&culture, "Test.", ContentType::Dialogue, None, 42);
        assert_eq!(result1.prompt, result2.prompt);
        assert_eq!(result1.injections_fired, result2.injections_fired);
    }

    #[test]
    fn epistemic_marker_preserved() {
        let culture = bare_culture();
        let result = build_prompt(
            &culture,
            "I heard the night crew had to stop the line twice.",
            ContentType::Dialogue,
            None,
            42,
        );
        assert!(result.prompt.contains("PRESERVE:"));
        assert!(result.prompt.contains("i heard"));
    }

    #[test]
    fn no_epistemic_marker_no_preserve() {
        let culture = bare_culture();
        let result = build_prompt(
            &culture,
            "The parts arrived yesterday.",
            ContentType::Dialogue,
            None,
            42,
        );
        assert!(!result.prompt.contains("PRESERVE:"));
    }

    #[test]
    fn word_count_short() {
        assert!(is_short_content("Hello there."));
        assert!(is_short_content("Inspection's next week."));
        assert!(is_short_content("One two three four five six seven"));
    }

    #[test]
    fn word_count_medium() {
        assert!(!is_short_content("One two three four five six seven eight"));
    }
}