"""
AAM Diffusion LLM — Synthetic Data Generator

Generates synthetic Graph→Narrative training pairs for
pre-training the diffusion model before real data is available.

The synthetic data follows the AAM pattern:
- Graph conditioning: evidence, compositions, anomalies, reasoning
- Target narrative: natural language text that represents the graph data

This is essential because:
1. We need training data before the model can be used
2. The data must follow the Graph→Narrative format specifically
3. Synthetic data helps bootstrap the model's ability to
   arrange sentences from structured evidence

Analogi: Seperti Jin Soun berlatih dengan kasus-kasus fiktif
sebelum menghadapi kasus nyata — data sintetis memberikan
"latihan dasar" sebelum data asli tersedia.
"""

from __future__ import annotations

import json
import logging
import random
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional

logger = logging.getLogger(__name__)


# --- Templates for synthetic data generation ---

# Indonesian narrative templates
ID_TEMPLATES = {
    "analysis": [
        "Berdasarkan analisis terhadap {trigger}: {evidence_summary}. {reasoning_summary}. Tingkat keyakinan: {confidence_pct}.",
        "Analisis menunjukkan bahwa {trigger} terkait dengan {evidence_summary}. {anomaly_summary}. Kesimpulan: {reasoning_summary}.",
        "Dari data yang tersedia, {trigger} memiliki koneksi ke {evidence_summary}. {reasoning_summary}. Confidence: {confidence_pct}.",
        "Hasil investigasi: {trigger}. Bukti: {evidence_summary}. {anomaly_summary}. {reasoning_summary}.",
        "Temuan: {trigger} berkorelasi dengan {evidence_summary}. Catatan: {anomaly_summary}. Analisis: {reasoning_summary}.",
    ],
    "evidence_summary": [
        "bukti menunjukkan {nodes}",
        "data dari {nodes} mengindikasikan",
        "{nodes} menjadi kunci",
        "informasi dari {nodes} mengarah ke",
        "sumber {nodes} mengkonfirmasi",
    ],
    "anomaly_summary": [
        "Anomali terdeteksi: {anomalies}",
        "Perhatian: {anomalies}",
        "Pola tidak lazim: {anomalies}",
        "Ketidaksesuaian ditemukan: {anomalies}",
        "Terdapat kejanggalan: {anomalies}",
    ],
    "reasoning_summary": [
        "Langkah penalaran: {steps}",
        "Proses deduksi: {steps}",
        "Analisis bertahap: {steps}",
        "Penelusuran logika: {steps}",
        "Rantai penalaran: {steps}",
    ],
}

# English narrative templates
EN_TEMPLATES = {
    "analysis": [
        "Based on analysis of {trigger}: {evidence_summary}. {reasoning_summary}. Confidence: {confidence_pct}.",
        "Analysis indicates that {trigger} is related to {evidence_summary}. {anomaly_summary}. Conclusion: {reasoning_summary}.",
        "From available data, {trigger} has connections to {evidence_summary}. {reasoning_summary}. Confidence level: {confidence_pct}.",
        "Investigation results: {trigger}. Evidence: {evidence_summary}. {anomaly_summary}. {reasoning_summary}.",
        "Findings: {trigger} correlates with {evidence_summary}. Note: {anomaly_summary}. Analysis: {reasoning_summary}.",
    ],
    "evidence_summary": [
        "evidence shows {nodes}",
        "data from {nodes} indicates",
        "{nodes} are key factors",
        "information from {nodes} points to",
        "sources {nodes} confirm",
    ],
    "anomaly_summary": [
        "Anomaly detected: {anomalies}",
        "Note: {anomalies}",
        "Unusual pattern: {anomalies}",
        "Inconsistency found: {anomalies}",
        "Irregularity observed: {anomalies}",
    ],
    "reasoning_summary": [
        "Reasoning steps: {steps}",
        "Deductive process: {steps}",
        "Step-by-step analysis: {steps}",
        "Logical trace: {steps}",
        "Reasoning chain: {steps}",
    ],
}

# Sample graph data for synthetic generation
SAMPLE_EVIDENCE_NODES = {
    "id": [
        "Hefei", "Diancang Five Swords", "Ju Jangmok", "Snow Plum Pill",
        "Gyeryong Merchant Guild", "Simhyeon Pavilion", "Martial Alliance",
        "Gu Ilmu", "Jang Hangi", "Blood Serpent Dance Step",
        "taeul_sect", "dark_faction", "hefei_branch",
    ],
    "en": [
        "Hefei", "Diancang Five Swords", "Ju Jangmok", "Snow Plum Pill",
        "Gyeryong Merchant Guild", "Simhyeon Pavilion", "Martial Alliance",
        "Gu Ilmu", "Jang Hangi", "Blood Serpent Dance Step",
        "taeul_sect", "dark_faction", "hefei_branch",
    ],
}

SAMPLE_TRIGGERS = {
    "id": [
        "Siapa yang mencuri Snow Plum Pill?",
        "Analisis pergerakan Diancang Five Swords",
        "Hubungan antara Ju Jangmok dan pencurian",
        "Anomali dalam laporan Hefei",
        "Investigasi inside job di Diancang",
        "Pola konsumsi Snow Plum Pill",
        "Cross-reference kejadian di Hefei",
        "Evaluasi kepercayaan sumber informasi",
        "Prediksi tindakan berikutnya tersangka",
        "Pattern completion dari bukti terpisah",
    ],
    "en": [
        "Who stole the Snow Plum Pill?",
        "Analysis of Diancang Five Swords movements",
        "Connection between Ju Jangmok and the theft",
        "Anomalies in the Hefei reports",
        "Investigation of inside job at Diancang",
        "Pattern of Snow Plum Pill consumption",
        "Cross-referencing events in Hefei",
        "Source trustworthiness evaluation",
        "Predicting next suspect actions",
        "Pattern completion from disparate evidence",
    ],
}

SAMPLE_ANOMALIES = {
    "id": [
        "Tidak ada konsumsi pil baru di pasar gelap",
        "Pencuri menghilang tanpa jejak",
        "Success rate pair lebih tinggi dari biasanya",
        "Misi di-assign dari dalam Diancang sendiri",
        "Ju Jangmok menghilang hari yang sama dengan pencurian",
        "Tidak ada pencuri baru setelah Ju Jangmok menghilang",
    ],
    "en": [
        "No new pill consumption in black market",
        "Thief disappeared without a trace",
        "Pair success rate unusually high",
        "Mission assigned from within Diancang itself",
        "Ju Jangmok disappeared same day as theft",
        "No new thief appeared after Ju Jangmok vanished",
    ],
}

SAMPLE_REASONING_STEPS = {
    "id": [
        "Recall: Ingat semua laporan terkait Hefei",
        "Cross-reference: Bandingkan tanggal kejadian",
        "Filter: Eliminasi yang tidak relevan",
        "Anomaly: Deteksi ketidaksesuaian pola",
        "Pattern: Hubungkan fragmen terpisah",
        "Compose: Susun kesimpulan dari bukti",
        "Predict: Perkirakan tindakan berikutnya",
        "Verify: Cek konsistensi kesimpulan",
    ],
    "en": [
        "Recall: Remember all reports related to Hefei",
        "Cross-reference: Compare event dates",
        "Filter: Eliminate irrelevant data",
        "Anomaly: Detect pattern inconsistency",
        "Pattern: Connect disparate fragments",
        "Compose: Assemble conclusion from evidence",
        "Predict: Estimate next actions",
        "Verify: Check conclusion consistency",
    ],
}


class SyntheticDataGenerator:
    """Generate synthetic Graph→Narrative training pairs.

    This generator creates training data that follows the AAM
    pattern: structured graph conditioning → natural language narrative.

    The generated data covers:
    - Various trigger types (questions, analysis requests)
    - Different numbers of evidence nodes (1-50)
    - Various anomaly patterns
    - Different reasoning chain lengths
    - Confidence distributions
    - Both Indonesian and English

    Usage:
        generator = SyntheticDataGenerator()
        examples = generator.generate(n=1000, language="id")
        generator.save(examples, "training_data.jsonl")
    """

    def __init__(
        self,
        seed: int = 42,
        language: str = "id",
    ):
        """Initialize the synthetic data generator.

        Args:
            seed: Random seed for reproducibility.
            language: Default language for generation.
        """
        self.seed = seed
        self.language = language
        random.seed(seed)

    def generate(
        self,
        n: int = 1000,
        language: Optional[str] = None,
        min_evidence: int = 2,
        max_evidence: int = 15,
        anomaly_probability: float = 0.6,
        reasoning_probability: float = 0.8,
    ) -> list[dict]:
        """Generate synthetic training examples.

        Args:
            n: Number of examples to generate.
            language: Language override.
            min_evidence: Minimum evidence nodes per example.
            max_evidence: Maximum evidence nodes per example.
            anomaly_probability: Probability of including anomalies.
            reasoning_probability: Probability of including reasoning steps.

        Returns:
            List of training example dictionaries.
        """
        lang = language or self.language
        templates = ID_TEMPLATES if lang == "id" else EN_TEMPLATES
        evidence_pool = SAMPLE_EVIDENCE_NODES.get(lang, SAMPLE_EVIDENCE_NODES["en"])
        trigger_pool = SAMPLE_TRIGGERS.get(lang, SAMPLE_TRIGGERS["en"])
        anomaly_pool = SAMPLE_ANOMALIES.get(lang, SAMPLE_ANOMALIES["en"])
        reasoning_pool = SAMPLE_REASONING_STEPS.get(lang, SAMPLE_REASONING_STEPS["en"])

        examples = []
        for _ in range(n):
            # Random trigger
            trigger = random.choice(trigger_pool)

            # Random evidence nodes
            n_evidence = random.randint(min_evidence, max_evidence)
            evidence = random.sample(evidence_pool, min(n_evidence, len(evidence_pool)))

            # Random confidence map
            confidence_map = {
                node: round(random.uniform(0.3, 1.0), 2)
                for node in evidence
            }

            # Random anomalies
            anomalies = []
            if random.random() < anomaly_probability:
                n_anomalies = random.randint(1, 3)
                anomalies = random.sample(anomaly_pool, min(n_anomalies, len(anomaly_pool)))

            # Random reasoning steps
            reasoning_steps = []
            if random.random() < reasoning_probability:
                n_steps = random.randint(2, 6)
                reasoning_steps = random.sample(reasoning_pool, min(n_steps, len(reasoning_pool)))

            # Source trust
            source_trust = round(random.uniform(0.5, 1.0), 2)

            # Generate narrative from template
            narrative = self._generate_narrative(
                trigger=trigger,
                evidence=evidence,
                anomalies=anomalies,
                reasoning_steps=reasoning_steps,
                confidence_map=confidence_map,
                templates=templates,
                lang=lang,
            )

            example = {
                "narrative": narrative,
                "trigger": trigger,
                "evidence_nodes": evidence,
                "compositions": [],
                "confidence_map": confidence_map,
                "anomalies": anomalies,
                "reasoning_steps": reasoning_steps,
                "source_trust": source_trust,
                "language": lang,
                "source": "synthetic",
            }

            examples.append(example)

        logger.info("Generated %d synthetic examples (language=%s)", n, lang)
        return examples

    def _generate_narrative(
        self,
        trigger: str,
        evidence: list[str],
        anomalies: list[str],
        reasoning_steps: list[str],
        confidence_map: dict[str, float],
        templates: dict,
        lang: str,
    ) -> str:
        """Generate a narrative from templates.

        Args:
            trigger: Trigger text.
            evidence: Evidence node labels.
            anomalies: Anomaly descriptions.
            reasoning_steps: Reasoning step descriptions.
            confidence_map: Confidence scores.
            templates: Template dictionary.
            lang: Language code.

        Returns:
            Generated narrative string.
        """
        # Build narrative parts
        evidence_str = ", ".join(evidence[:5])
        avg_confidence = sum(confidence_map.values()) / max(len(confidence_map), 1)

        # Fill templates
        evidence_summary = random.choice(templates["evidence_summary"]).format(
            nodes=evidence_str
        )

        anomaly_summary = ""
        if anomalies:
            anomaly_summary = random.choice(templates["anomaly_summary"]).format(
                anomalies="; ".join(anomalies[:3])
            )

        reasoning_summary = ""
        if reasoning_steps:
            reasoning_summary = random.choice(templates["reasoning_summary"]).format(
                steps="; ".join(reasoning_steps[:4])
            )

        # Main narrative
        narrative = random.choice(templates["analysis"]).format(
            trigger=trigger,
            evidence_summary=evidence_summary,
            anomaly_summary=anomaly_summary,
            reasoning_summary=reasoning_summary,
            confidence_pct=f"{avg_confidence:.0%}",
        )

        return narrative

    def save(
        self,
        examples: list[dict],
        path: str | Path,
    ) -> None:
        """Save examples to JSONL file.

        Args:
            examples: List of example dictionaries.
            path: Output file path.
        """
        path = Path(path)
        path.parent.mkdir(parents=True, exist_ok=True)

        with open(path, "w", encoding="utf-8") as f:
            for example in examples:
                f.write(json.dumps(example, ensure_ascii=False) + "\n")

        logger.info("Saved %d examples to %s", len(examples), path)

    @classmethod
    def generate_training_split(
        cls,
        output_dir: str | Path,
        n_train: int = 10000,
        n_val: int = 500,
        language: str = "id",
        seed: int = 42,
    ) -> tuple[Path, Path]:
        """Generate and save train/val splits.

        Args:
            output_dir: Output directory.
            n_train: Number of training examples.
            n_val: Number of validation examples.
            language: Language for generation.
            seed: Random seed.

        Returns:
            Tuple of (train_path, val_path).
        """
        output_dir = Path(output_dir)
        output_dir.mkdir(parents=True, exist_ok=True)

        generator = cls(seed=seed, language=language)

        # Generate training data
        train_examples = generator.generate(n=n_train, language=language)
        train_path = output_dir / "train.jsonl"
        generator.save(train_examples, train_path)

        # Generate validation data (different seed)
        val_generator = cls(seed=seed + 1, language=language)
        val_examples = val_generator.generate(n=n_val, language=language)
        val_path = output_dir / "val.jsonl"
        val_generator.save(val_examples, val_path)

        logger.info(
            "Generated training split: %d train, %d val",
            n_train, n_val,
        )

        return train_path, val_path