"""Structured visual attributes for scene windows: shot type, setting, era.

These let footage searches match on *how* a scene is filmed and *when/where*
it is set — "aerial shots of a Kingdom Hall exterior", "modern-day disaster
relief", "Bible-times scenes set indoors" — which a free-text description alone
captures inconsistently.

Single source of truth for the controlled vocabularies, normalization of the
model's free text into those vocabularies, and the search phrase that gets
folded into a window's embedded text so the existing semantic search picks the
attributes up (no query-side changes needed). Both the vision pipeline (going
forward) and the text-backfill pass import from here so the two can't drift.
"""

from __future__ import annotations

# canonical value -> synonym-rich phrase woven into the embedded text. The
# phrase is what makes the attribute searchable: a query like "aerial drone
# shot" embeds near a window whose text contains "aerial drone overhead".
SHOT_TYPES: dict[str, str] = {
    "aerial": "aerial drone overhead bird's-eye shot",
    "establishing": "wide establishing landscape shot",
    "medium": "medium shot",
    "close-up": "close-up tight shot",
    "pov": "point-of-view first-person shot",
    "talking-head": "interview talking-head piece-to-camera shot",
    "unclear": "",
}
SETTINGS: dict[str, str] = {
    "interior": "indoor interior",
    "exterior": "outdoor exterior",
    "mixed": "both indoor and outdoor",
    "unclear": "",
}
ERAS: dict[str, str] = {
    "ancient": "Bible times ancient biblical era",
    "medieval": "medieval middle ages",
    "early-1900s": "early 1900s early twentieth century historical",
    "mid-1900s": "mid 1900s mid twentieth century historical",
    "late-1900s": "1970s 1980s 1990s late twentieth century",
    "modern-day": "modern-day present-day contemporary",
    "unclear": "",
}

# Free-text variants the model may emit -> canonical key. Checked as substrings
# (longest-first) so "close up" / "closeup" / "tight" all map to "close-up".
_SHOT_ALIASES: tuple[tuple[str, str], ...] = (
    ("drone", "aerial"), ("aerial", "aerial"), ("overhead", "aerial"),
    ("bird", "aerial"), ("tilt-shift", "aerial"),
    ("establishing", "establishing"), ("wide", "establishing"),
    ("landscape", "establishing"), ("vista", "establishing"),
    ("close-up", "close-up"), ("close up", "close-up"), ("closeup", "close-up"),
    ("tight", "close-up"), ("portrait", "close-up"), ("macro", "close-up"),
    ("point-of-view", "pov"), ("point of view", "pov"), ("pov", "pov"),
    ("first-person", "pov"),
    ("talking head", "talking-head"), ("talking-head", "talking-head"),
    ("piece to camera", "talking-head"), ("to camera", "talking-head"),
    ("interview", "talking-head"), ("presenter", "talking-head"),
    ("medium", "medium"),
)
_SETTING_ALIASES: tuple[tuple[str, str], ...] = (
    ("interior", "interior"), ("indoor", "interior"), ("inside", "interior"),
    ("exterior", "exterior"), ("outdoor", "exterior"), ("outside", "exterior"),
    ("mixed", "mixed"), ("both", "mixed"),
)
_ERA_ALIASES: tuple[tuple[str, str], ...] = (
    ("biblical", "ancient"), ("bible times", "ancient"), ("ancient", "ancient"),
    ("first century", "ancient"), ("antiquity", "ancient"),
    ("medieval", "medieval"), ("middle ages", "medieval"),
    ("early 1900", "early-1900s"), ("early-1900", "early-1900s"),
    ("early twentieth", "early-1900s"), ("1910", "early-1900s"),
    ("1920", "early-1900s"), ("1930", "early-1900s"),
    ("mid 1900", "mid-1900s"), ("mid-1900", "mid-1900s"),
    ("1940", "mid-1900s"), ("1950", "mid-1900s"), ("1960", "mid-1900s"),
    ("1970", "late-1900s"), ("1980", "late-1900s"), ("1990", "late-1900s"),
    ("late 1900", "late-1900s"), ("late-1900", "late-1900s"),
    ("late twentieth", "late-1900s"),
    ("modern", "modern-day"), ("present", "modern-day"),
    ("contemporary", "modern-day"), ("today", "modern-day"),
    ("2000", "modern-day"), ("2010", "modern-day"), ("2020", "modern-day"),
)


def _normalize(raw: object, aliases: tuple[tuple[str, str], ...], valid: dict[str, str]) -> str:
    """Map the model's free text to a canonical key; 'unclear' when no match."""
    text = str(raw or "").strip().lower()
    if not text:
        return "unclear"
    if text in valid:  # already canonical
        return text
    for needle, canonical in aliases:
        if needle in text:
            return canonical
    return "unclear"


def normalize_shot_type(raw: object) -> str:
    return _normalize(raw, _SHOT_ALIASES, SHOT_TYPES)


def normalize_setting(raw: object) -> str:
    return _normalize(raw, _SETTING_ALIASES, SETTINGS)


def normalize_era(raw: object) -> str:
    return _normalize(raw, _ERA_ALIASES, ERAS)


def attribute_search_phrase(shot_type: str, setting: str, era: str) -> str:
    """Build the phrase appended to a window's description before embedding.

    Empty parts ('unclear') are omitted. Returns '' when nothing is known, so a
    window with no usable attributes embeds exactly as its description did.
    """
    parts: list[str] = []
    shot = SHOT_TYPES.get(shot_type or "unclear", "")
    setting_phrase = SETTINGS.get(setting or "unclear", "")
    era_phrase = ERAS.get(era or "unclear", "")
    if shot:
        parts.append(f"Camera: {shot}.")
    if setting_phrase:
        parts.append(f"Setting: {setting_phrase}.")
    if era_phrase:
        parts.append(f"Era: {era_phrase}.")
    return " ".join(parts)


def embed_text(description: str, shot_type: str, setting: str, era: str) -> str:
    """The full text to embed for a window: description + attribute phrase."""
    phrase = attribute_search_phrase(shot_type, setting, era)
    description = (description or "").strip()
    return f"{description} {phrase}".strip() if phrase else description