"""Utility functions for data processing."""

import json
import logging
from pathlib import Path
from typing import Any, Dict, List, Optional

import numpy as np

logger = logging.getLogger(__name__)


def load_jsonl(file_path: str) -> List[Dict[str, Any]]:
    """Load JSONL file."""
    data = []
    with open(file_path, 'r', encoding='utf-8') as f:
        for line in f:
            line = line.strip()
            if line:
                try:
                    data.append(json.loads(line))
                except json.JSONDecodeError as e:
                    logger.warning(f"Failed to parse line: {e}")
    return data


def save_jsonl(data: List[Dict[str, Any]], file_path: str):
    """Save to JSONL file."""
    Path(file_path).parent.mkdir(parents=True, exist_ok=True)
    with open(file_path, 'w', encoding='utf-8') as f:
        for item in data:
            f.write(json.dumps(item, ensure_ascii=False) + '\n')


def load_json(file_path: str) -> Any:
    """Load JSON file."""
    with open(file_path, 'r', encoding='utf-8') as f:
        return json.load(f)


def save_json(data: Any, file_path: str):
    """Save to JSON file."""
    Path(file_path).parent.mkdir(parents=True, exist_ok=True)
    with open(file_path, 'w', encoding='utf-8') as f:
        json.dump(data, f, ensure_ascii=False, indent=2)


def merge_datasets(
    datasets: List[Any],
    weights: Optional[List[float]] = None,
) -> List[Dict[str, Any]]:
    """Merge multiple datasets with optional weighting."""
    if weights is None:
        weights = [1.0] * len(datasets)

    # Normalize weights
    total_weight = sum(weights)
    weights = [w / total_weight for w in weights]

    merged = []
    for ds, weight in zip(datasets, weights):
        # Calculate number of samples to take
        n_samples = int(len(ds) * weight)
        if n_samples > len(ds):
            n_samples = len(ds)

        # Sample
        indices = np.random.choice(len(ds), size=n_samples, replace=False)
        for idx in indices:
            merged.append(ds[idx])

    logger.info(f"Merged dataset size: {len(merged)}")
    return merged


def compute_dataset_statistics(
    dataset: Any,
    text_key: str = "text",
) -> Dict[str, Any]:
    """Compute comprehensive statistics for dataset."""
    lengths = []
    domains = []
    has_thoughts = []

    for sample in dataset:
        text = sample.get(text_key, "")
        lengths.append(len(text.split()))

        domain = sample.get("domain", "unknown")
        domains.append(domain)

        has_thoughts.append(1 if sample.get("thoughts") else 0)

    return {
        "num_samples": len(dataset),
        "length_stats": compute_length_statistics(lengths),
        "domain_distribution": {d: domains.count(d) / len(domains) for d in set(domains)},
        "thoughts_coverage": sum(has_thoughts) / len(has_thoughts),
    }


def validate_dataset(dataset: Any, required_keys: List[str]) -> List[str]:
    """Validate dataset structure."""
    errors = []

    for i, sample in enumerate(dataset):
        for key in required_keys:
            if key not in sample:
                errors.append(f"Sample {i} missing required key: {key}")

    return errors


def deduplicate_dataset(dataset: List[Dict[str, Any]], key: str = "text") -> List[Dict[str, Any]]:
    """Remove duplicate samples based on key."""
    seen = set()
    deduplicated = []

    for sample in dataset:
        value = sample.get(key, "")
        if value not in seen:
            seen.add(value)
            deduplicated.append(sample)

    logger.info(f"Deduplicated: {len(dataset)} -> {len(deduplicated)}")
    return deduplicated


def balance_dataset(
    dataset: List[Dict[str, Any]],
    by_key: str = "domain",
    max_per_category: Optional[int] = None,
) -> List[Dict[str, Any]]:
    """Balance dataset across categories."""
    categories = {}
    for sample in dataset:
        category = sample.get(by_key, "unknown")
        if category not in categories:
            categories[category] = []
        categories[category].append(sample)

    # Determine max samples per category
    if max_per_category is None:
        max_per_category = min(len(cat) for cat in categories.values())

    # Sample equally from each category
    balanced = []
    for category, samples in categories.items():
        if len(samples) > max_per_category:
            samples = np.random.choice(samples, size=max_per_category, replace=False).tolist()
        balanced.extend(samples)

    logger.info(f"Balanced dataset: {len(dataset)} -> {len(balanced)}")
    return balanced