src/compliment_forest/data_builder.py

from __future__ import annotations

import json
import random
from collections.abc import Iterable, Sequence
from typing import Any

from .prompts import AUTHOR_SYSTEM
from .quality import is_situation_grounded
from .safety import guard_input
from .schema import ForestDraft

CREATURES = (
    ("The Patient Fox", "patience under pressure", "I am allowed to learn."),
    ("The Listening Owl", "careful perspective", "I can listen before I leap."),
    ("The Brave Snail", "quiet courage", "I make progress at my pace."),
    ("The Steady Deer", "steadiness through change", "I can meet one moment."),
    ("The Clear-Voiced Wren", "honest self-expression", "I can speak gently and clearly."),
    ("The Curious Otter", "playful curiosity", "I can stay curious here."),
    ("The Gentle Bear", "self-compassion", "I can be kind to myself."),
    ("The Building Beaver", "practical resourcefulness", "I can shape one next step."),
)

IMAGE_PROMPTS = {
    "The Patient Fox": "a gentle russet fox sitting in a mossy clearing, soft kind eyes",
    "The Listening Owl": "a round tawny owl resting on a low branch, attentive kind eyes",
    "The Brave Snail": "a tiny snail crossing a fern frond, softly glowing spiral shell",
    "The Steady Deer": "a small deer standing in morning mist, calm gentle expression",
    "The Clear-Voiced Wren": "a tiny wren singing beside dusty rose wildflowers",
    "The Curious Otter": "a small otter floating beside reeds, bright gentle eyes",
    "The Gentle Bear": "a sleepy bear cub resting under dappled canopy light",
    "The Building Beaver": "a friendly beaver holding one smooth twig beside a stream",
}

TEMPLATE_NAMES = (
    "Mika",
    "Noor",
    "Ari",
    "Sam",
    "Leila",
    "Jun",
    "Robin",
    "Maya",
    "Theo",
    "Nia",
    "Alex",
    "Bao",
    "Clara",
    "Dev",
    "Esme",
    "Farah",
    "Gabe",
    "Hana",
    "Iris",
    "Jules",
    "Kai",
    "Lina",
    "Mateo",
    "Niko",
    "Omar",
    "Priya",
    "Quinn",
    "Ravi",
    "Sora",
    "Tess",
    "Uma",
    "Vera",
    "Will",
    "Xia",
    "Yara",
    "Zane",
    "An",
    "Bea",
    "Cole",
    "Dara",
)

TEACHER_NAMES = (
    "Cora",
    "Eli",
    "Imani",
    "Pax",
    "Ren",
    "Sol",
    "Tala",
    "Zoe",
)

LINE_TEMPLATES = (
    (
        "{situation} can be difficult without meaning you are failing. "
        "Your {strength} can help you choose one honest next step."
    ),
    (
        "There is real uncertainty in {situation}. {strength_capitalized} does not erase it; "
        "it gives you room to respond without rushing."
    ),
    (
        "When {situation_lower}, you do not have to solve the whole path today. "
        "Your {strength} is enough for the next part."
    ),
    (
        "{situation} asks a lot of you. Let {strength} make the next choice smaller, "
        "clearer, and kinder."
    ),
    (
        "It makes sense that {situation_lower} feels tender. Your {strength} can sit "
        "beside the difficulty instead of pretending it is easy."
    ),
    (
        "You can acknowledge the hard part of {situation_lower} and still trust your "
        "{strength} to help with what comes next."
    ),
)

REFLECTION_TEMPLATES = (
    "What would change if you asked only for the next kind and concrete step?",
    "Which part deserves your attention now, and which part can wait?",
    "What could you notice before deciding you must already have the answer?",
    "What is one choice that would make this moment more workable?",
    "How might you make room for both the difficulty and your own agency?",
    "What support would let you move with care instead of pressure?",
)


def normalize_positive_frame(row: dict[str, Any]) -> dict[str, str]:
    return {
        "situation": str(row.get("original_text", "")).strip(),
        "support_hint": str(row.get("reframed_text", "")).strip(),
        "strategy": str(row.get("strategy", "")).strip(),
        "source": "SALT-NLP/positive_reframing",
    }


def normalize_empathy(row: dict[str, Any]) -> dict[str, str]:
    conversations = row.get("conversations") or []
    assistant_lines = [
        str(message.get("content", "")).strip()
        for message in conversations
        if message.get("role") == "assistant" and message.get("content")
    ]
    return {
        "situation": str(row.get("situation", "")).strip(),
        "support_hint": " ".join(assistant_lines[:2]),
        "strategy": str(row.get("emotion", "")).strip(),
        "source": "Estwld/empathetic_dialogues_llm",
    }


def validate_synthetic_example(example: dict[str, Any]) -> dict[str, Any] | None:
    name = str(example.get("name", "")).strip()
    situation = str(example.get("situation", "")).strip()
    if not guard_input(name, situation).allowed:
        return None
    try:
        forest = ForestDraft.model_validate(example.get("forest"))
    except (TypeError, ValueError):
        return None
    if any(not is_situation_grounded(clearing.line, situation) for clearing in forest.clearings):
        return None
    return {
        "name": name,
        "situation": situation,
        "forest": forest.model_dump(),
        "source": str(example.get("source", "synthetic")),
        "teacher_model": str(example.get("teacher_model", "")),
    }


def build_sft_record(example: dict[str, Any]) -> dict[str, Any]:
    user_content = json.dumps(
        {"name": example["name"], "situation": example["situation"]},
        ensure_ascii=False,
    )
    assistant_content = json.dumps(example["forest"], ensure_ascii=False)
    return {
        "name": example["name"],
        "situation": example["situation"],
        "source": example.get("source", "synthetic"),
        "teacher_model": example.get("teacher_model", ""),
        "messages": [
            {"role": "system", "content": AUTHOR_SYSTEM},
            {"role": "user", "content": user_content},
            {"role": "assistant", "content": assistant_content},
        ],
    }


def deduplicate_records(records: Iterable[dict[str, Any]]) -> list[dict[str, Any]]:
    seen: set[tuple[str, str]] = set()
    result: list[dict[str, Any]] = []
    for record in records:
        identity = (
            str(record["name"]).strip().casefold(),
            str(record["situation"]).strip().casefold(),
        )
        if identity in seen:
            continue
        seen.add(identity)
        result.append(record)
    return result


def split_records(
    records: Sequence[dict[str, Any]],
    *,
    validation_fraction: float = 0.1,
    seed: int = 42,
) -> dict[str, list[dict[str, Any]]]:
    if not 0 < validation_fraction < 1:
        raise ValueError("validation_fraction must be between zero and one")
    shuffled = list(records)
    random.Random(seed).shuffle(shuffled)
    validation_count = max(1, round(len(shuffled) * validation_fraction))
    return {
        "train": shuffled[validation_count:],
        "validation": shuffled[:validation_count],
    }


def template_forest(name: str, situation: str, variant: int) -> dict[str, Any]:
    rotated = list(CREATURES[variant % len(CREATURES) :]) + list(
        CREATURES[: variant % len(CREATURES)]
    )
    selected = rotated[:5]
    clearings = []
    for clearing_index, (creature, strength, spell) in enumerate(selected):
        line_template = LINE_TEMPLATES[(variant + clearing_index) % len(LINE_TEMPLATES)]
        clearings.append(
            {
                "creature": creature,
                "strength": strength,
                "line": line_template.format(
                    situation=situation.rstrip("."),
                    situation_lower=situation.rstrip(".").lower(),
                    strength=strength,
                    strength_capitalized=strength.capitalize(),
                ),
                "reflection": REFLECTION_TEMPLATES[
                    (variant + clearing_index) % len(REFLECTION_TEMPLATES)
                ],
                "spell": spell,
                "image_prompt": IMAGE_PROMPTS[creature],
            }
        )
    return {
        "forest_title": (
            f"{name}'s Path Through This Moment"
            if variant % 2 == 0
            else f"{name}'s Clearing for What Comes Next"
        ),
        "proposed_strengths": [item[1] for item in selected],
        "clearings": clearings,
    }


def build_template_examples(
    situations: Sequence[str],
    *,
    variants_per_situation: int = 4,
) -> list[dict[str, Any]]:
    examples = []
    for situation_index, situation in enumerate(situations):
        for variant in range(variants_per_situation):
            name = TEMPLATE_NAMES[(situation_index + variant) % len(TEMPLATE_NAMES)]
            examples.append(
                {
                    "name": name,
                    "situation": situation,
                    "forest": template_forest(name, situation, variant),
                    "source": "template_coverage",
                }
            )
    return examples


def teacher_requests(situations: Sequence[str], *, start: int) -> list[dict[str, str]]:
    return [
        {
            "name": TEACHER_NAMES[(start + offset) % len(TEACHER_NAMES)],
            "situation": situation,
        }
        for offset, situation in enumerate(situations)
    ]


def forest_batch_json_schema() -> dict[str, Any]:
    clearing = {
        "type": "object",
        "additionalProperties": False,
        "required": [
            "creature",
            "strength",
            "line",
            "reflection",
            "spell",
            "image_prompt",
        ],
        "properties": {
            "creature": {"type": "string"},
            "strength": {"type": "string"},
            "line": {"type": "string"},
            "reflection": {"type": "string"},
            "spell": {"type": "string"},
            "image_prompt": {"type": "string"},
        },
    }
    forest = {
        "type": "object",
        "additionalProperties": False,
        "required": ["forest_title", "proposed_strengths", "clearings"],
        "properties": {
            "forest_title": {"type": "string"},
            "proposed_strengths": {
                "type": "array",
                "items": {"type": "string"},
            },
            "clearings": {
                "type": "array",
                "items": clearing,
            },
        },
    }
    return {
        "type": "object",
        "additionalProperties": False,
        "required": ["examples"],
        "properties": {
            "examples": {
                "type": "array",
                "items": {
                    "type": "object",
                    "additionalProperties": False,
                    "required": ["name", "situation", "forest"],
                    "properties": {
                        "name": {"type": "string"},
                        "situation": {"type": "string"},
                        "forest": forest,
                    },
                },
            }
        },
    }


class CohereForestGenerator:
    def __init__(
        self,
        api_key: str,
        *,
        model: str = "command-a-03-2025",
    ) -> None:
        import cohere

        self.client = cohere.ClientV2(api_key=api_key)
        self.model = model

    def generate(
        self,
        requests: Sequence[dict[str, str]],
        *,
        source_hints: Sequence[dict[str, str]] = (),
        seed: int = 42,
    ) -> list[dict[str, Any]]:
        prompt = {
            "task": (
                "Write one complete Compliment Forest for each request. Return 4-6 distinct "
                "clearings. Every line must repeat at least one concrete noun or phrase from "
                "its situation. Acknowledge difficulty without diagnosis, guarantees, hollow "
                "praise, or toxic positivity. Spells begin with 'I' and use at most 12 words. "
                "Image prompts describe one creature only and contain no style words."
            ),
            "requests": list(requests),
            "voice_hints": list(source_hints)[:8],
        }
        response = self.client.chat(
            model=self.model,
            messages=[
                {"role": "system", "content": AUTHOR_SYSTEM},
                {"role": "user", "content": json.dumps(prompt, ensure_ascii=False)},
            ],
            response_format={
                "type": "json_object",
                "json_schema": forest_batch_json_schema(),
            },
            safety_mode="CONTEXTUAL",
            temperature=0.65,
            max_tokens=5000,
            seed=seed,
        )
        payload = json.loads(response.message.content[0].text)
        return list(payload.get("examples", []))