"""Context assembly per AI Dungeon's memory system
(help.aidungeon.com/faq/the-memory-system):

    [AI Instructions]        always included
    [Plot Essentials]        always included (classic "Memory")
    [Story Summary]          always included (manual in Phase 3, auto in Phase 6)
    [Used Memories]          top-K memory-bank retrievals (Phase 6, when enabled)
    [Triggered Story Cards]  "World Lore: <entry>", conditional; first dropped when over budget
    [Story history]          newest actions that fit the remaining token budget
    [Author's Note]          injected AUTHORS_NOTE_DEPTH actions before the end of history
    [Latest player action]   (+ script frontMemory right after it, Phase 4)

Which components are present is AI Dungeon's design, above. The *order* they
are laid out in is not: everything fixed is emitted first and everything that
moves after the history, because prompt caching bills on a shared prefix and
one mutable section high up re-prices the whole prompt under it. See the two
"static block" / "live sections" comments in `build_context`.
"""

import functools
from dataclasses import dataclass

import tiktoken

from .. import models, worldstate
from . import history

AUTHORS_NOTE_DEPTH = 3  # actions from the end of history
CARD_BUDGET_SHARE = 0.4  # max share of non-reserved budget that story cards may take
NPC_WINDOW = 6  # actions of story searched for NPC trigger words ("in scene")
SEPARATOR = "\n\n"

# Output-length guidance. max_output_tokens is a hard wall the endpoint enforces
# mid-sentence: hitting it truncates whatever is being written, and since the
# state block is emitted last, it is what gets lost. Asking the model to land
# just inside the wall keeps the cut from happening in the first place.
LENGTH_HEADROOM = 50  # tokens held back from the cap for the state block itself
# Models cannot count their own tokens, but they do follow a word budget, so the
# reserved budget is stated in words. ~0.75 words per token for English prose.
WORDS_PER_TOKEN = 0.75
# A word budget is a suggestion the model routinely overshoots, and the cap it is
# protecting is a hard wall — so aim 10% short of the real ceiling and let the
# overshoot land in the slack instead of in the state block.
LENGTH_BUFFER = 0.90
MIN_LENGTH_HINT_WORDS = 40  # below this the hint is noise; a tiny cap speaks for itself
# A ceiling alone is a one-sided instruction, and models read it very differently:
# a verbose one is held back by it, while a terse one has nothing to act on except
# the "write only as much as the moment needs" clause and collapses to two
# paragraphs. Stating a floor as well turns the guidance into a band, so the same
# prompt lands in the same place regardless of which way the model leans. Set as a
# share of the ceiling so the floor can never approach it.
LENGTH_FLOOR_SHARE = 0.35
# Below this a floor is meaningless — at a tight cap a short turn is the correct
# turn — and the tight-cap wording is the one measured to keep the state block
# alive, so it is left exactly as it was.
MIN_LENGTH_FLOOR_WORDS = 60
# The floor exists to stop a collapse to two paragraphs, not to demand an essay:
# at a 2400-token cap the share alone would ask for 555 words *minimum*. Past this
# point a reader wanting more length can say so in the author's note.
MAX_LENGTH_FLOOR_WORDS = 300


@functools.lru_cache(maxsize=1)
def _encoding() -> tiktoken.Encoding:
    return tiktoken.get_encoding("cl100k_base")


def count_tokens(text: str) -> int:
    return len(_encoding().encode(text))


def truncate_to_last_tokens(text: str, budget: int) -> str:
    tokens = _encoding().encode(text)
    if len(tokens) <= budget:
        return text
    return _encoding().decode(tokens[-budget:])


@dataclass
class Section:
    label: str
    text: str

    @property
    def tokens(self) -> int:
        return count_tokens(self.text)


def length_hint(max_output_tokens: int, *, has_ws: bool) -> str:
    """Ask for a turn that fits inside the output cap, stated as a word budget.

    Returns "" when the cap is too small to phrase usefully — the hint is a
    suggestion the model can drift past, so it only earns its tokens when there
    is enough room for the drift to still land inside the wall.
    """
    words = int((max_output_tokens - LENGTH_HEADROOM) * WORDS_PER_TOKEN * LENGTH_BUFFER)
    if words < MIN_LENGTH_HINT_WORDS:
        return ""
    tail = (
        " Finish the narration and append the state block well inside the limit."
        if has_ws
        else " Bring the turn to a close well inside the limit rather than "
        "stopping mid-sentence."
    )
    # Phrased as a ceiling, never as a budget. Measured against this model, "keep
    # this turn under about N words" reads as a target to fill: it moved a 174-word
    # average to 246 (n=5, every run longer than every unhinted one), i.e. the hint
    # pushed turns toward the very wall it exists to keep them away from. Naming
    # the number as a limit, plus saying a typical turn is far shorter, left the
    # average at 170 while still rescuing the state block at tight caps.
    floor = min(int(words * LENGTH_FLOOR_SHARE), MAX_LENGTH_FLOOR_WORDS)
    if floor < MIN_LENGTH_FLOOR_WORDS:
        return (
            f"[Hard limit: this turn must not exceed {words} words. Write only as "
            f"much as the moment needs — a typical turn is much shorter.{tail}]"
        )
    # Both numbers are bounds, and deliberately asymmetric ones: "must not exceed"
    # for the wall the endpoint enforces, "should not stop short of" for the floor.
    # Neither is a target, which is what the measurement above says matters. The
    # "prefer the lower end" clause does the job the old "a typical turn is much
    # shorter" line did — holding a verbose model off the wall — but now with a
    # number under it, so a terse model reading the same clause lands on the floor
    # instead of at forty words.
    return (
        f"[Hard limit: this turn must not exceed {words} words, and it should not "
        f"stop short of about {floor}. Prefer the lower end of that range unless "
        f"the scene genuinely needs more.{tail}]"
    )


def _script_memory(adventure: models.Adventure) -> dict:
    """Script-provided memory overrides (populated by Phase 4 scripting)."""
    state = adventure.script_state if isinstance(adventure.script_state, dict) else {}
    memory = state.get("memory")
    return memory if isinstance(memory, dict) else {}


def _history_text(action: models.Action) -> str:
    """An AI turn as the model should see it in replayed history: its narration
    with the state block it emitted re-appended (reconstructed from the stored
    delta). The block is stripped before storage/UI, so without this every past
    AI turn would look like one that emitted nothing — biasing the model, by
    imitation, to stop emitting too. Player turns and blockless turns are
    returned unchanged.

    Reads `world_delta`, not `context_snapshot`: this runs for every action in
    the replayed history, and the snapshot is deferred precisely so a turn
    never drags the prompt archive out of the database."""
    text = action.text
    wd = action.world_delta if isinstance(action.world_delta, dict) else None
    if wd:
        block = worldstate.render_delta_block(wd.get("delta") or {})
        if block:
            text = f"{text}\n{block}"
    return text


def _visible_npcs(actions: list[models.Action], stat_schema: dict) -> dict[str, str]:
    """Defined NPCs whose trigger words appear in the recent story — the ones
    "in scene", so only their stats get injected. Maps npc id -> display name.

    `actions` is already the last handful (see NPC_WINDOW)."""
    recent = SEPARATOR.join(a.text for a in actions).lower()
    visible: dict[str, str] = {}
    for npc_key, ndef in (stat_schema.get("npcs") or {}).items():
        if not isinstance(ndef, dict):
            continue
        if any(trigger in recent for trigger in worldstate.npc_triggers(ndef, npc_key)):
            visible[npc_key] = worldstate.npc_name(ndef, npc_key)
    return visible


def _match_cards(cards: list[models.StoryCard], window_text: str) -> list[dict]:
    """AI Dungeon trigger rules: case-insensitive, space-sensitive, partial-word
    ('boat' triggers on 'boats'). Returns one record per card with the keyword that fired."""
    haystack = window_text.lower()
    matched = []
    for card in cards:
        for key in (k.strip().lower() for k in card.keys.split(",")):
            if key and key in haystack:
                matched.append(
                    {"id": card.id, "name": card.name, "keyword": key, "entry": card.entry}
                )
                break
    return matched


def build_context(
    adventure: models.Adventure,
    settings: models.Settings,
    memory_bank: dict | None = None,
    exclude_action_id: int | None = None,
) -> tuple[str, str, dict]:
    """Returns (system_text, story_text, context_report). `memory_bank` is the
    result of memorybank.retrieve_memories (None when the bank is off);
    `exclude_action_id` omits one action from the story (see history.py)."""
    script_mem = _script_memory(adventure)

    # ----- The static block: byte-for-byte the same prompt every turn -----
    # The ordering here is a billing decision, not a stylistic one. Prompt
    # caching matches a *prefix*: an endpoint reuses the prompt up to the first
    # byte that differs from last time and no further. So one mutable section
    # near the top re-prices everything below it, and what is below it is the
    # story history, which is the bulk of the prompt. Anything that changes
    # turn to turn therefore goes *after* the history, in the live sections —
    # which is also where recency serves it best, the same reasoning that
    # already puts EMIT_REMINDER last.
    system_sections: list[Section] = [Section("narrator", settings.narrator_prompt.strip())]

    # RPG world state (Phase 12): how to report changes. The live values are a
    # live section below; the schema-derived guide and the emit rule are fixed
    # for as long as the scenario is.
    stat_schema = adventure.scenario.stat_schema if adventure.scenario else None
    has_ws = worldstate.has_schema(stat_schema)
    if has_ws:
        guide = worldstate.render_reference(stat_schema)
        if guide:
            system_sections.append(Section("world_state_guide", guide))
        system_sections.append(Section("world_state_rule", worldstate.EMIT_RULE))

    if isinstance(script_mem.get("context"), str) and script_mem["context"].strip():
        system_sections.append(Section("script_context", script_mem["context"].strip()))
    if adventure.ai_instructions.strip():
        system_sections.append(Section("ai_instructions", adventure.ai_instructions.strip()))
    if adventure.memory.strip():
        system_sections.append(
            Section("plot_essentials", f"Plot essentials:\n{adventure.memory.strip()}")
        )

    # ----- Live sections: everything that moves, built here, placed after the
    # history further down. Ordered least-volatile first, so a turn that
    # changes only the fastest-moving one keeps the others cached too: the
    # summary is rewritten every few turns, lore turns over with the scene, the
    # retrieved memories change on most turns, the stat values on nearly all.
    # `world_lore` joins them below — it is the history window that triggers
    # the cards, so it cannot be known yet.
    summary_section = (
        Section("story_summary", f"Story summary:\n{adventure.story_summary.strip()}")
        if adventure.story_summary.strip()
        else None
    )
    memories_section = None
    if memory_bank and memory_bank.get("used"):
        lines_text = "\n".join(f"- {m['text']}" for m in memory_bank["used"])
        memories_section = Section("used_memories", f"Memories:\n{lines_text}")
    world_state_section = None
    if has_ws:
        block = worldstate.render_state_section(
            adventure.world_state, stat_schema,
            _visible_npcs(
                history.tail(adventure, NPC_WINDOW, exclude_action_id), stat_schema
            ),
        )
        if block:
            world_state_section = Section("world_state", block)

    authors_note_text = adventure.authors_note.strip()
    if isinstance(script_mem.get("authorsNote"), str) and script_mem["authorsNote"].strip():
        authors_note_text = script_mem["authorsNote"].strip()
    authors_note = f"[Author's note: {authors_note_text}]" if authors_note_text else ""

    front_memory = ""
    if isinstance(script_mem.get("frontMemory"), str):
        front_memory = script_mem["frontMemory"].strip()

    length_note = length_hint(settings.max_output_tokens, has_ws=has_ws)

    # The live sections moved below the history but they are still in the
    # prompt, so they are still reserved against the budget. (`world_lore` is
    # not: it is budgeted out of `available` further down, as it always was.)
    reserved = (
        sum(s.tokens for s in system_sections)
        + sum(
            s.tokens
            for s in (summary_section, memories_section, world_state_section)
            if s is not None
        )
        + count_tokens(authors_note)
        + count_tokens(front_memory)
        + count_tokens(length_note)
        + (count_tokens(worldstate.EMIT_REMINDER) if has_ws else 0)
    )
    available = max(256, settings.context_token_budget - reserved)

    # Only the newest actions can reach the prompt: everything below is either
    # truncated to `available` tokens or stops at the budget. Fetch a window
    # that is provably larger than that and no more — a long adventure would
    # otherwise read its entire history every turn to use the tail of it.
    actions = history.window_covering(
        adventure, available, count_tokens, exclude_action_id
    )

    # ----- Story cards: triggered by recent story text (the window history could fill) -----
    trigger_window = truncate_to_last_tokens(SEPARATOR.join(a.text for a in actions), available)
    triggered = _match_cards(adventure.story_cards, trigger_window)

    card_budget = int(available * CARD_BUDGET_SHARE)
    card_records = []
    lore_lines: list[str] = []
    used = 0
    for match in triggered:
        line = f"World Lore: {match['entry'].strip()}"
        tokens = count_tokens(line)
        included = used + tokens <= card_budget
        if included:
            lore_lines.append(line)
            used += tokens
        card_records.append(
            {"id": match["id"], "name": match["name"], "keyword": match["keyword"],
             "included": included}
        )
    lore_section = (
        Section("world_lore", "\n".join(lore_lines)) if lore_lines else None
    )

    # ----- Story history: newest first until the remaining budget is spent -----
    history_budget = available - used
    included_actions: list[models.Action] = []
    spent = 0
    oldest_truncated = False
    for action in reversed(actions):
        # Budget on the text as it will actually appear — with the re-attached
        # state block (B) when this adventure tracks world state.
        rendered = _history_text(action) if has_ws else action.text
        tokens = count_tokens(rendered) + count_tokens(SEPARATOR)
        if spent + tokens > history_budget:
            if not included_actions:
                # Even the newest action alone is over budget: hard-truncate it.
                included_actions.append(
                    models.Action(
                        adventure_id=action.adventure_id, index=action.index,
                        type=action.type,
                        text=truncate_to_last_tokens(action.text, history_budget),
                    )
                )
                oldest_truncated = True
            break
        included_actions.append(action)
        spent += tokens
    included_actions.reverse()

    # ----- Assemble story text with author's note near the end -----
    # Re-attach each AI turn's state block (stripped before storage) so recent
    # history shows the model its own emit pattern to imitate.
    texts = [_history_text(a) if has_ws else a.text for a in included_actions]
    note_sections: list[Section] = []
    if authors_note:
        pos = max(0, len(texts) - AUTHORS_NOTE_DEPTH)
        before, after = texts[:pos], texts[pos:]
        if before:
            note_sections.append(Section("history", SEPARATOR.join(before)))
        note_sections.append(Section("authors_note", authors_note))
        note_sections.append(Section("recent_history", SEPARATOR.join(after)))
    else:
        note_sections.append(Section("history", SEPARATOR.join(texts)))
    # The live sections, least volatile first (see where they are built). They
    # sit below the history so the history caches, and above the tail so the
    # three sections that are last for a reason stay last.
    for live in (summary_section, lore_section, memories_section, world_state_section):
        if live is not None:
            note_sections.append(live)
    if front_memory:
        note_sections.append(Section("front_memory", front_memory))
    # Sits just above the emit reminder, which keeps the strongest recency slot:
    # the length budget is about the narration, the reminder about the block that
    # comes after it, so this is also the order the model has to act in.
    note_sections.append(Section("length_hint", length_note))
    if has_ws:
        # Terminal reminder: the emit rule sits up in the system block, far from
        # where the model generates; repeat it last, in the strongest recency slot.
        note_sections.append(Section("world_state_reminder", worldstate.EMIT_REMINDER))

    story_sections = [s for s in note_sections if s.text]
    system_text = SEPARATOR.join(s.text for s in system_sections if s.text)
    story_text = SEPARATOR.join(s.text for s in story_sections)

    all_sections = [s for s in system_sections if s.text] + story_sections
    report = {
        "sections": [
            {"label": s.label, "text": s.text, "tokens": s.tokens} for s in all_sections
        ],
        "prompt": {"system": system_text, "story": story_text},
        "tokens": {
            "total": count_tokens(system_text) + count_tokens(story_text),
            "budget": settings.context_token_budget,
        },
        "cards": card_records,
        "memories": memory_bank,
        "history": {
            "included": len(included_actions),
            # The whole story, not just the window fetched above — Insights
            # reports "N of M actions included" and M is the real total.
            "total": history.count(adventure, exclude_action_id),
            "oldest_truncated": oldest_truncated,
        },
        "settings": {
            "model": settings.model,
            "api_mode": settings.api_mode,
            "temperature": settings.temperature,
            "max_output_tokens": settings.max_output_tokens,
        },
    }
    return system_text, story_text, report