307 lines
11 KiB
Python
307 lines
11 KiB
Python
from __future__ import annotations
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from typing import Any, Callable
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try:
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from . import cast_context as cast_context_policy
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from . import character_profile as character_profile_policy
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from . import pair_clothing
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from . import pair_options
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from . import softcore_text_policy
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except ImportError: # Allows local smoke tests with top-level imports.
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import cast_context as cast_context_policy
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import character_profile as character_profile_policy
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import pair_clothing
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import pair_options
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import softcore_text_policy
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AxisRng = Callable[[dict[str, int], str, int, int], Any]
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Choose = Callable[[Any, list[str]], str]
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CharacterContextForLabel = Callable[
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[str, dict[str, dict[str, Any]], Any, str, str, bool, bool],
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tuple[dict[str, Any], dict[str, Any] | None],
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]
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CharacterSlotLabelMap = Callable[[list[dict[str, Any]]], dict[str, dict[str, Any]]]
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ParseCharacterCast = Callable[[str | dict[str, Any] | list[Any] | None], list[dict[str, Any]]]
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SlotIsPov = Callable[[dict[str, Any] | None], bool]
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SlotSoftcoreOutfit = Callable[[dict[str, Any] | None, Any], str]
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def cast_summary_phrase(women_count: int, men_count: int) -> str:
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return cast_context_policy.cast_summary_phrase(women_count, men_count)
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def insta_descriptor_from_row(row: dict[str, Any]) -> str:
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return character_profile_policy.descriptor_from_parts(
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"woman",
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row.get("age_band") or row.get("age"),
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row.get("body_phrase"),
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row.get("skin"),
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row.get("hair"),
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row.get("eyes"),
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row.get("descriptor_detail"),
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)
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def insta_descriptor_from_context(context: dict[str, Any]) -> str:
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subject = str(context.get("subject") or context.get("subject_type") or "person").strip()
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return character_profile_policy.descriptor_from_parts(
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subject,
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context.get("age"),
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context.get("body_phrase"),
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context.get("skin"),
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context.get("hair"),
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context.get("eyes"),
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context.get("descriptor_detail"),
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)
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def prompt_cast_descriptors(text: str) -> str:
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return str(text or "").replace("Woman A / primary creator:", "Woman A:")
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def cast_descriptor_entries_from_slots(
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*,
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seed_config: dict[str, int],
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seed: int,
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row_number: int,
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ethnicity: str,
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figure: str,
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no_plus_women: bool,
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no_black: bool,
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women_count: int,
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men_count: int,
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character_slots: list[dict[str, Any]],
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character_slot_map: dict[str, dict[str, Any]],
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primary_descriptor: str = "",
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axis_rng: AxisRng,
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character_context_for_label: CharacterContextForLabel,
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slot_is_pov: SlotIsPov,
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) -> tuple[list[str], list[dict[str, Any]]]:
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rng = axis_rng(seed_config, "person", seed, row_number + 997)
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descriptors: list[str] = []
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for index in range(max(0, women_count)):
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label = f"Woman {chr(ord('A') + index)}"
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if index == 0 and primary_descriptor:
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descriptors.append(f"Woman A / primary creator: {primary_descriptor}")
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continue
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context, _slot = character_context_for_label(
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label,
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character_slot_map,
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rng,
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ethnicity,
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figure,
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no_plus_women,
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no_black,
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)
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descriptors.append(f"{label}: {insta_descriptor_from_context(context)}")
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for index in range(max(0, men_count)):
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label = f"Man {chr(ord('A') + index)}"
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if slot_is_pov(character_slot_map.get(label)):
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continue
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context, _slot = character_context_for_label(
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label,
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character_slot_map,
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rng,
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ethnicity,
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figure,
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no_plus_women,
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no_black,
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)
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descriptors.append(f"{label}: {insta_descriptor_from_context(context)}")
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return descriptors, character_slots
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def cast_descriptor_entries(
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*,
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seed_config: dict[str, int],
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seed: int,
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row_number: int,
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ethnicity: str,
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figure: str,
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no_plus_women: bool,
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no_black: bool,
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women_count: int,
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men_count: int,
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character_cast: str | dict[str, Any] | list[Any] | None = "",
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primary_descriptor: str = "",
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parse_character_cast: ParseCharacterCast,
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character_slot_label_map: CharacterSlotLabelMap,
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axis_rng: AxisRng,
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character_context_for_label: CharacterContextForLabel,
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slot_is_pov: SlotIsPov,
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) -> tuple[list[str], list[dict[str, Any]]]:
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slots = parse_character_cast(character_cast)
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label_map = character_slot_label_map(slots)
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return cast_descriptor_entries_from_slots(
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seed_config=seed_config,
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seed=seed,
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row_number=row_number,
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ethnicity=ethnicity,
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figure=figure,
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no_plus_women=no_plus_women,
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no_black=no_black,
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women_count=women_count,
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men_count=men_count,
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character_slots=slots,
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character_slot_map=label_map,
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primary_descriptor=primary_descriptor,
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axis_rng=axis_rng,
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character_context_for_label=character_context_for_label,
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slot_is_pov=slot_is_pov,
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)
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def softcore_partner_styling(
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*,
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seed_config: dict[str, int],
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seed: int,
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row_number: int,
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women_count: int,
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men_count: int,
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pov_labels: list[str] | None,
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label_map: dict[str, dict[str, Any]] | None,
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axis_rng: AxisRng,
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choose: Choose,
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slot_softcore_outfit: SlotSoftcoreOutfit,
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) -> dict[str, Any]:
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clothing_rng = axis_rng(seed_config, "clothing", seed, row_number + 421)
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pose_rng = axis_rng(seed_config, "pose", seed, row_number + 421)
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pov_set = set(pov_labels or [])
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outfits: list[str] = []
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for index in range(max(0, women_count - 1)):
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label = chr(ord("B") + index)
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full_label = f"Woman {label}"
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outfit = slot_softcore_outfit((label_map or {}).get(full_label), clothing_rng) or choose(
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clothing_rng,
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pair_options.INSTA_OF_SOFTCORE_PARTNER_WOMEN_OUTFITS,
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)
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sentence = pair_clothing.softcore_outfit_sentence(full_label, outfit)
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if sentence:
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outfits.append(sentence)
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for index in range(max(0, men_count)):
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label = chr(ord("A") + index)
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full_label = f"Man {label}"
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if full_label in pov_set:
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continue
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outfit = slot_softcore_outfit((label_map or {}).get(full_label), clothing_rng) or choose(
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clothing_rng,
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pair_options.INSTA_OF_SOFTCORE_PARTNER_MEN_OUTFITS,
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)
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sentence = pair_clothing.softcore_outfit_sentence(full_label, outfit)
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if sentence:
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outfits.append(sentence)
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return {
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"outfits": outfits,
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"pose": choose(pose_rng, pair_options.SOFTCORE_CAST_POSES),
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}
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def resolve_insta_pair_cast_context(
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*,
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soft_row: dict[str, Any],
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options: dict[str, Any],
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parsed_seed_config: dict[str, int],
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seed: int,
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row_number: int,
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ethnicity: str,
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figure: str,
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no_plus_women: bool,
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no_black: bool,
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hard_women_count: int,
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hard_men_count: int,
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character_slots: list[dict[str, Any]],
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character_slot_map: dict[str, dict[str, Any]],
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pov_character_labels: list[str],
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platform_styles: dict[str, str],
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soft_levels: dict[str, str],
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hardcore_levels: dict[str, str],
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axis_rng: AxisRng,
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character_context_for_label: CharacterContextForLabel,
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slot_is_pov: SlotIsPov,
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choose: Choose,
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slot_softcore_outfit: SlotSoftcoreOutfit,
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parsed_softcore_seed_config: dict[str, int] | None = None,
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) -> dict[str, Any]:
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soft_seed_config = parsed_softcore_seed_config or parsed_seed_config
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descriptor = insta_descriptor_from_row(soft_row)
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cast_descriptors, _descriptor_slots = cast_descriptor_entries_from_slots(
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seed_config=parsed_seed_config,
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seed=seed,
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row_number=row_number,
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ethnicity=ethnicity,
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figure=figure,
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no_plus_women=no_plus_women,
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no_black=no_black,
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women_count=hard_women_count,
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men_count=hard_men_count,
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character_slots=character_slots,
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character_slot_map=character_slot_map,
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primary_descriptor=descriptor,
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axis_rng=axis_rng,
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character_context_for_label=character_context_for_label,
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slot_is_pov=slot_is_pov,
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)
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cast_descriptor_text = prompt_cast_descriptors("; ".join(cast_descriptors))
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same_softcore_cast = options["softcore_cast"] == "same_as_hardcore"
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soft_cast_descriptor_text = cast_descriptor_text if same_softcore_cast else f"Woman A: {descriptor}"
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soft_partner_styling = softcore_partner_styling(
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seed_config=soft_seed_config,
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seed=seed,
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row_number=row_number,
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women_count=hard_women_count if same_softcore_cast else 1,
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men_count=hard_men_count if same_softcore_cast else 0,
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pov_labels=pov_character_labels if same_softcore_cast else [],
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label_map=character_slot_map,
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axis_rng=axis_rng,
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choose=choose,
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slot_softcore_outfit=slot_softcore_outfit,
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)
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if not same_softcore_cast:
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soft_partner_styling = {"outfits": [], "pose": ""}
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soft_partner_outfit_text = "; ".join(soft_partner_styling["outfits"])
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soft_cast = (
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"solo creator setup with Woman A alone"
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if options["softcore_cast"] == "solo"
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else f"soft creator-teaser setup with {cast_summary_phrase(hard_women_count, hard_men_count)}"
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)
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soft_cast_presence = (
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softcore_text_policy.softcore_cast_presence_phrase(
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same_cast=same_softcore_cast,
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pov_labels=pov_character_labels,
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cast_label="Woman A and the listed partners",
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woman_label="Woman A",
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)
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+ ". "
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)
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soft_cast_styling_sentence = (
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f"Partner softcore styling: {soft_partner_outfit_text}. Cast pose: {soft_partner_styling['pose']}. "
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if same_softcore_cast and soft_partner_outfit_text
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else ""
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)
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hard_cast = cast_summary_phrase(hard_women_count, hard_men_count)
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soft_descriptor_sentence = (
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f"Cast descriptors: {soft_cast_descriptor_text}. "
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if same_softcore_cast
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else f"Woman A: {descriptor}. "
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)
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return {
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"descriptor": descriptor,
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"cast_descriptors": cast_descriptors,
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"cast_descriptor_text": cast_descriptor_text,
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"soft_cast_descriptor_text": soft_cast_descriptor_text,
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"soft_partner_styling": soft_partner_styling,
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"soft_partner_outfit_text": soft_partner_outfit_text,
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"platform_style": platform_styles[options["platform_style"]],
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"soft_level": soft_levels[options["softcore_level"]],
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"hard_level": hardcore_levels[options["hardcore_level"]],
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"soft_cast": soft_cast,
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"soft_cast_presence": soft_cast_presence,
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"soft_cast_styling_sentence": soft_cast_styling_sentence,
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"hard_cast": hard_cast,
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"soft_descriptor_sentence": soft_descriptor_sentence,
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}
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