from __future__ import annotations

import re
from typing import Any, Callable

try:
    from . import caption_metadata_routes
    from . import caption_policy
    from . import formatter_input as input_policy
    from . import item_axis_policy
    from . import krea_cast as cast_policy
    from . import route_metadata as route_metadata_policy
    from . import softcore_text_policy
except ImportError:  # Allows local smoke tests with `python -c`.
    import caption_metadata_routes
    import caption_policy
    import formatter_input as input_policy
    import item_axis_policy
    import krea_cast as cast_policy
    import route_metadata as route_metadata_policy
    import softcore_text_policy


OLD_TRIGGER = caption_policy.OLD_TRIGGER
DEFAULT_TRIGGER = caption_policy.DEFAULT_TRIGGER
PROMPT_FIELD_LABELS = input_policy.prompt_field_labels()
ITEM_LABELS = caption_policy.ITEM_LABELS


def clean_text(value: Any) -> str:
    return input_policy.clean_text(value)


def is_false(value: Any) -> bool:
    if isinstance(value, bool):
        return value is False
    if isinstance(value, str):
        return value.strip().lower() in ("false", "0", "no", "off")
    return False


def expression_disabled(row: dict[str, Any]) -> bool:
    return bool(row.get("expression_disabled")) or is_false(row.get("expression_enabled", True))


def cap_first(text: str) -> str:
    text = clean_text(text).strip(" ,")
    return text[:1].upper() + text[1:] if text else ""


def article(noun_phrase: str) -> str:
    word = noun_phrase.lstrip().lower()
    if word.startswith("hour") or word[:1] in "aeiou":
        return "an"
    return "a"


def sentence(text: str) -> str:
    text = clean_text(text).strip(" ,;")
    if not text:
        return ""
    if text[-1] not in ".!?":
        text += "."
    return cap_first(text)


def join_sentences(parts: list[str]) -> str:
    return " ".join(part for part in (sentence(part) for part in parts) if part)


def formatter_hint_parts(row: dict[str, Any]) -> list[str]:
    hints: list[str] = []
    if not isinstance(row, dict):
        return hints
    for hint in route_metadata_policy.row_formatter_hints(row, "caption"):
        hint = clean_text(hint).strip(" .")
        if hint and hint not in hints:
            hints.append(hint)
    return hints


def append_formatter_hints(prose: str, row: dict[str, Any]) -> str:
    hints = formatter_hint_parts(row)
    if not hints:
        return prose
    return join_sentences([prose, *hints])


def human_join(parts: list[str]) -> str:
    parts = [part for part in (clean_text(part) for part in parts) if part]
    if len(parts) <= 1:
        return "".join(parts)
    if len(parts) == 2:
        return f"{parts[0]} and {parts[1]}"
    return f"{', '.join(parts[:-1])}, and {parts[-1]}"


def metadata_action_label(row: dict[str, Any], default: str = "sexual pose") -> str:
    return caption_policy.metadata_action_label(row, default)


def item_axis_detail_text(row: dict[str, Any], existing_text: str = "") -> str:
    details = item_axis_policy.row_axis_value_texts(
        row,
        skip_keys=item_axis_policy.METADATA_AXIS_KEYS,
        existing_text=existing_text,
    )
    return human_join(details)


def prompt_cast_descriptors(text: str) -> str:
    return cast_policy.prompt_cast_descriptors(text)


def cast_entries(text: str) -> list[tuple[str, str]]:
    return cast_policy.cast_entries(text)


def natural_cast_descriptor_text(text: str) -> str:
    return cast_policy.natural_cast_descriptor_text(text)


def cast_labels(text: str) -> list[str]:
    return cast_policy.cast_labels(text)


def natural_label_text(text: Any, labels: list[str]) -> str:
    return cast_policy.natural_label_text(text, labels, capitalize_sentence_starts=False)


def strip_style_tail(text: str) -> str:
    return caption_policy.strip_style_tail(text)


def remove_trigger(text: str, trigger: str) -> str:
    return input_policy.strip_trigger_prefix(
        text,
        (trigger, OLD_TRIGGER, DEFAULT_TRIGGER),
        remove_exact=True,
    )


def with_trigger(text: str, trigger: str, include_trigger: bool) -> str:
    text = join_sentences([text]) if "." not in text else clean_text(text)
    trigger = clean_text(trigger or DEFAULT_TRIGGER)
    if not include_trigger or not trigger:
        return text
    if text.lower().startswith(trigger.lower() + "."):
        return text
    return f"{trigger}. {text}"


def prompt_field(text: str, label: str) -> str:
    return input_policy.prompt_field(text, label, field_labels=PROMPT_FIELD_LABELS)


def row_value(row: dict[str, Any], key: str, labels: tuple[str, ...] = ()) -> str:
    return input_policy.row_value(row, key, labels, field_labels=PROMPT_FIELD_LABELS)


def field_row_value(row: dict[str, Any], key: str) -> str:
    return row_value(row, key)


def field_from_any_prompt(text: str, labels: tuple[str, ...]) -> str:
    for label in labels:
        value = input_policy.prompt_field(text, label, field_labels=PROMPT_FIELD_LABELS)
        if value:
            return value
    return ""


def normalize_composition(text: str) -> str:
    return caption_policy.normalize_composition(text)


def clean_clothing(text: str) -> str:
    return caption_policy.clean_clothing(text)


def body_phrase(body: Any, figure_note: Any = "") -> str:
    body = clean_text(body)
    figure_note = clean_text(figure_note)
    if not body:
        return figure_note
    if not figure_note:
        return f"{body} figure"
    if "figure" in figure_note.lower():
        return f"{body} build and {figure_note}"
    return f"{body} figure with {figure_note}"


def single_caption_front(row: dict[str, Any]) -> dict[str, str]:
    caption = clean_text(row.get("caption"))
    if not caption:
        return {}
    caption = remove_trigger(strip_style_tail(caption), clean_text(row.get("trigger")) or DEFAULT_TRIGGER)
    caption = remove_trigger(caption, OLD_TRIGGER)
    subject = clean_text(row.get("primary_subject"))
    age = clean_text(row.get("age_band") or row.get("age"))
    phrase = clean_text(row.get("body_phrase"))
    if not phrase:
        body = clean_text(row.get("body_type") or row.get("body"))
        figure = clean_text(row.get("figure"))
        phrase = body_phrase(body, figure)
    front = f"{subject}, {age}, {phrase}, "
    if subject in ("woman", "man") and age and phrase and caption.startswith(front):
        try:
            skin, hair, eyes, _rest = caption[len(front) :].split(", ", 3)
        except ValueError:
            return {}
    else:
        pieces = [piece.strip() for piece in caption.split(", ", 6)]
        if len(pieces) < 7:
            return {}
        subject, age, phrase, skin, hair, eyes, _rest = pieces
    if subject not in ("woman", "man"):
        return {}
    return {
        "caption_subject": subject,
        "caption_age": age,
        "caption_body_phrase": phrase,
        "caption_skin": skin,
        "caption_hair": hair,
        "caption_eyes": eyes,
    }


def pose_clause(pose: str) -> str:
    pose = clean_text(pose)
    if not pose:
        return ""
    first = pose.split(None, 1)[0].lower()
    if first.endswith("ing") or first in ("seated", "reclined", "posed"):
        return pose
    return f"posing in {pose}"


def age_subject(age: str, subject: str) -> str:
    age = clean_text(age)
    subject = clean_text(subject) or "person"
    if not age:
        return f"An adult {subject}"
    clean_age = re.sub(r"\s+adults?$", "", age).strip()
    if "year-old" in clean_age:
        return f"A {clean_age} adult {subject}"
    if re.search(r"\d", clean_age):
        poss = "her" if subject == "woman" else "his"
        return f"An adult {subject} in {poss} {clean_age}"
    return f"An adult {clean_age} {subject}"


def clean_age_phrase(age: str) -> str:
    age = clean_text(age)
    age = re.sub(r"\s+adults?$", "", age).strip()
    return age.replace("-year-old", " years old")


def subject_phrase_from_counts(row: dict[str, Any]) -> str:
    subject = clean_text(row.get("subject_phrase"))
    if subject:
        return subject
    try:
        women = int(row.get("women_count") or 0)
        men = int(row.get("men_count") or 0)
    except (TypeError, ValueError):
        return clean_text(row.get("primary_subject")) or "adult scene"
    parts = []
    if women:
        parts.append(f"{women} adult {'woman' if women == 1 else 'women'}")
    if men:
        parts.append(f"{men} adult {'man' if men == 1 else 'men'}")
    if not parts:
        return clean_text(row.get("primary_subject")) or "adult scene"
    return " and ".join(parts)


def verb_for_row(row: dict[str, Any]) -> str:
    try:
        return "is" if int(row.get("person_count") or 0) == 1 else "are"
    except (TypeError, ValueError):
        return "are"


def detail_allows(level: str, dense_only: bool = False) -> bool:
    return caption_policy.detail_allows(level, dense_only=dense_only)


def metadata_route_dependencies(
    metadata_to_prose: Callable[..., tuple[str, str]],
) -> caption_metadata_routes.CaptionMetadataRouteDependencies:
    return caption_metadata_routes.CaptionMetadataRouteDependencies(
        item_labels=ITEM_LABELS,
        clean_text=clean_text,
        row_value=row_value,
        field_row_value=field_row_value,
        clean_clothing=clean_clothing,
        normalize_composition=normalize_composition,
        expression_disabled=expression_disabled,
        detail_allows=detail_allows,
        join_sentences=join_sentences,
        human_join=human_join,
        article=article,
        cap_first=cap_first,
        body_phrase=body_phrase,
        single_caption_front=single_caption_front,
        pose_clause=pose_clause,
        age_subject=age_subject,
        clean_age_phrase=clean_age_phrase,
        subject_phrase_from_counts=subject_phrase_from_counts,
        verb_for_row=verb_for_row,
        metadata_action_label=metadata_action_label,
        item_axis_detail_text=item_axis_detail_text,
        natural_cast_descriptor_text=natural_cast_descriptor_text,
        cast_labels=cast_labels,
        natural_label_text=natural_label_text,
        softcore_caption_setup_phrase=softcore_text_policy.softcore_caption_setup_phrase,
        metadata_to_prose=metadata_to_prose,
    )