308 lines
9.6 KiB
Python
308 lines
9.6 KiB
Python
from __future__ import annotations
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import re
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from typing import Any, Callable
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try:
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from . import caption_metadata_routes
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from . import caption_policy
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from . import formatter_input as input_policy
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from . import krea_cast as cast_policy
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from . import route_metadata as route_metadata_policy
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from . import softcore_text_policy
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except ImportError: # Allows local smoke tests with `python -c`.
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import caption_metadata_routes
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import caption_policy
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import formatter_input as input_policy
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import krea_cast as cast_policy
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import route_metadata as route_metadata_policy
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import softcore_text_policy
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OLD_TRIGGER = caption_policy.OLD_TRIGGER
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DEFAULT_TRIGGER = caption_policy.DEFAULT_TRIGGER
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PROMPT_FIELD_LABELS = input_policy.prompt_field_labels()
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ITEM_LABELS = caption_policy.ITEM_LABELS
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def clean_text(value: Any) -> str:
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return input_policy.clean_text(value)
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def is_false(value: Any) -> bool:
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if isinstance(value, bool):
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return value is False
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if isinstance(value, str):
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return value.strip().lower() in ("false", "0", "no", "off")
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return False
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def expression_disabled(row: dict[str, Any]) -> bool:
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return bool(row.get("expression_disabled")) or is_false(row.get("expression_enabled", True))
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def cap_first(text: str) -> str:
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text = clean_text(text).strip(" ,")
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return text[:1].upper() + text[1:] if text else ""
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def article(noun_phrase: str) -> str:
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word = noun_phrase.lstrip().lower()
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if word.startswith("hour") or word[:1] in "aeiou":
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return "an"
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return "a"
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def sentence(text: str) -> str:
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text = clean_text(text).strip(" ,;")
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if not text:
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return ""
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if text[-1] not in ".!?":
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text += "."
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return cap_first(text)
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def join_sentences(parts: list[str]) -> str:
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return " ".join(part for part in (sentence(part) for part in parts) if part)
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def formatter_hint_parts(row: dict[str, Any]) -> list[str]:
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hints: list[str] = []
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if not isinstance(row, dict):
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return hints
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for hint in route_metadata_policy.row_formatter_hints(row, "caption"):
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hint = clean_text(hint).strip(" .")
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if hint and hint not in hints:
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hints.append(hint)
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return hints
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def append_formatter_hints(prose: str, row: dict[str, Any]) -> str:
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hints = formatter_hint_parts(row)
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if not hints:
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return prose
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return join_sentences([prose, *hints])
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def human_join(parts: list[str]) -> str:
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parts = [part for part in (clean_text(part) for part in parts) if part]
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if len(parts) <= 1:
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return "".join(parts)
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if len(parts) == 2:
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return f"{parts[0]} and {parts[1]}"
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return f"{', '.join(parts[:-1])}, and {parts[-1]}"
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def metadata_action_label(row: dict[str, Any], default: str = "sexual pose") -> str:
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return caption_policy.metadata_action_label(row, default)
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def prompt_cast_descriptors(text: str) -> str:
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return cast_policy.prompt_cast_descriptors(text)
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def cast_entries(text: str) -> list[tuple[str, str]]:
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return cast_policy.cast_entries(text)
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def natural_cast_descriptor_text(text: str) -> str:
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return cast_policy.natural_cast_descriptor_text(text)
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def cast_labels(text: str) -> list[str]:
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return cast_policy.cast_labels(text)
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def natural_label_text(text: Any, labels: list[str]) -> str:
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return cast_policy.natural_label_text(text, labels, capitalize_sentence_starts=False)
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def strip_style_tail(text: str) -> str:
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return caption_policy.strip_style_tail(text)
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def remove_trigger(text: str, trigger: str) -> str:
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return input_policy.strip_trigger_prefix(
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text,
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(trigger, OLD_TRIGGER, DEFAULT_TRIGGER),
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remove_exact=True,
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)
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def with_trigger(text: str, trigger: str, include_trigger: bool) -> str:
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text = join_sentences([text]) if "." not in text else clean_text(text)
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trigger = clean_text(trigger or DEFAULT_TRIGGER)
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if not include_trigger or not trigger:
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return text
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if text.lower().startswith(trigger.lower() + "."):
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return text
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return f"{trigger}. {text}"
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def prompt_field(text: str, label: str) -> str:
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return input_policy.prompt_field(text, label, field_labels=PROMPT_FIELD_LABELS)
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def row_value(row: dict[str, Any], key: str, labels: tuple[str, ...] = ()) -> str:
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return input_policy.row_value(row, key, labels, field_labels=PROMPT_FIELD_LABELS)
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def field_row_value(row: dict[str, Any], key: str) -> str:
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return row_value(row, key)
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def field_from_any_prompt(text: str, labels: tuple[str, ...]) -> str:
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for label in labels:
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value = input_policy.prompt_field(text, label, field_labels=PROMPT_FIELD_LABELS)
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if value:
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return value
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return ""
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def normalize_composition(text: str) -> str:
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return caption_policy.normalize_composition(text)
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def clean_clothing(text: str) -> str:
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return caption_policy.clean_clothing(text)
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def body_phrase(body: Any, figure_note: Any = "") -> str:
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body = clean_text(body)
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figure_note = clean_text(figure_note)
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if not body:
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return figure_note
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if not figure_note:
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return f"{body} figure"
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if "figure" in figure_note.lower():
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return f"{body} build and {figure_note}"
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return f"{body} figure with {figure_note}"
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def single_caption_front(row: dict[str, Any]) -> dict[str, str]:
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caption = clean_text(row.get("caption"))
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if not caption:
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return {}
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caption = remove_trigger(strip_style_tail(caption), clean_text(row.get("trigger")) or DEFAULT_TRIGGER)
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caption = remove_trigger(caption, OLD_TRIGGER)
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subject = clean_text(row.get("primary_subject"))
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age = clean_text(row.get("age_band") or row.get("age"))
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phrase = clean_text(row.get("body_phrase"))
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if not phrase:
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body = clean_text(row.get("body_type") or row.get("body"))
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figure = clean_text(row.get("figure"))
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phrase = body_phrase(body, figure)
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front = f"{subject}, {age}, {phrase}, "
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if subject in ("woman", "man") and age and phrase and caption.startswith(front):
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try:
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skin, hair, eyes, _rest = caption[len(front) :].split(", ", 3)
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except ValueError:
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return {}
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else:
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pieces = [piece.strip() for piece in caption.split(", ", 6)]
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if len(pieces) < 7:
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return {}
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subject, age, phrase, skin, hair, eyes, _rest = pieces
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if subject not in ("woman", "man"):
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return {}
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return {
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"caption_subject": subject,
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"caption_age": age,
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"caption_body_phrase": phrase,
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"caption_skin": skin,
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"caption_hair": hair,
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"caption_eyes": eyes,
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}
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def pose_clause(pose: str) -> str:
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pose = clean_text(pose)
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if not pose:
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return ""
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first = pose.split(None, 1)[0].lower()
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if first.endswith("ing") or first in ("seated", "reclined", "posed"):
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return pose
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return f"posing in {pose}"
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def age_subject(age: str, subject: str) -> str:
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age = clean_text(age)
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subject = clean_text(subject) or "person"
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if not age:
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return f"An adult {subject}"
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clean_age = re.sub(r"\s+adults?$", "", age).strip()
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if "year-old" in clean_age:
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return f"A {clean_age} adult {subject}"
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if re.search(r"\d", clean_age):
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poss = "her" if subject == "woman" else "his"
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return f"An adult {subject} in {poss} {clean_age}"
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return f"An adult {clean_age} {subject}"
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def clean_age_phrase(age: str) -> str:
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age = clean_text(age)
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age = re.sub(r"\s+adults?$", "", age).strip()
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return age.replace("-year-old", " years old")
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def subject_phrase_from_counts(row: dict[str, Any]) -> str:
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subject = clean_text(row.get("subject_phrase"))
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if subject:
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return subject
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try:
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women = int(row.get("women_count") or 0)
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men = int(row.get("men_count") or 0)
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except (TypeError, ValueError):
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return clean_text(row.get("primary_subject")) or "adult scene"
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parts = []
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if women:
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parts.append(f"{women} adult {'woman' if women == 1 else 'women'}")
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if men:
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parts.append(f"{men} adult {'man' if men == 1 else 'men'}")
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if not parts:
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return clean_text(row.get("primary_subject")) or "adult scene"
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return " and ".join(parts)
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def verb_for_row(row: dict[str, Any]) -> str:
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try:
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return "is" if int(row.get("person_count") or 0) == 1 else "are"
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except (TypeError, ValueError):
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return "are"
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def detail_allows(level: str, dense_only: bool = False) -> bool:
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return caption_policy.detail_allows(level, dense_only=dense_only)
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def metadata_route_dependencies(
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metadata_to_prose: Callable[..., tuple[str, str]],
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) -> caption_metadata_routes.CaptionMetadataRouteDependencies:
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return caption_metadata_routes.CaptionMetadataRouteDependencies(
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item_labels=ITEM_LABELS,
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clean_text=clean_text,
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row_value=row_value,
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field_row_value=field_row_value,
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clean_clothing=clean_clothing,
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normalize_composition=normalize_composition,
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expression_disabled=expression_disabled,
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detail_allows=detail_allows,
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join_sentences=join_sentences,
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human_join=human_join,
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article=article,
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cap_first=cap_first,
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body_phrase=body_phrase,
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single_caption_front=single_caption_front,
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pose_clause=pose_clause,
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age_subject=age_subject,
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clean_age_phrase=clean_age_phrase,
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subject_phrase_from_counts=subject_phrase_from_counts,
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verb_for_row=verb_for_row,
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metadata_action_label=metadata_action_label,
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natural_cast_descriptor_text=natural_cast_descriptor_text,
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cast_labels=cast_labels,
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natural_label_text=natural_label_text,
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softcore_caption_setup_phrase=softcore_text_policy.softcore_caption_setup_phrase,
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metadata_to_prose=metadata_to_prose,
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)
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