"""Generate a normalized theme catalog CSV from card datasets.

Outputs `theme_catalog.csv` with deterministic ordering, a reproducible version hash,
and per-source occurrence counts so supplemental theme workflows can reuse the catalog.
"""
from __future__ import annotations

import argparse
import ast
import csv
import hashlib
import json
import os
import shutil
import sys
from collections import Counter, defaultdict
from dataclasses import dataclass
from datetime import datetime, timezone
from pathlib import Path
from typing import Dict, Iterable, List, Optional, Sequence

try:
    import pandas as pd
    HAS_PANDAS = True
except ImportError:
    HAS_PANDAS = False
    pd = None  # type: ignore

ROOT = Path(__file__).resolve().parents[2]
CODE_ROOT = ROOT / "code"
if str(CODE_ROOT) not in sys.path:
    sys.path.insert(0, str(CODE_ROOT))

try:
    from code.settings import CSV_DIRECTORY as DEFAULT_CSV_DIRECTORY
except Exception:  # pragma: no cover - fallback for adhoc execution
    DEFAULT_CSV_DIRECTORY = "csv_files"

# Parquet support requires pandas (imported at top of file, uses pyarrow under the hood)
HAS_PARQUET_SUPPORT = HAS_PANDAS

DEFAULT_OUTPUT_PATH = ROOT / "config" / "themes" / "theme_catalog.csv"
HEADER_COMMENT_PREFIX = "# theme_catalog"


@dataclass(slots=True)
class CatalogRow:
    theme: str
    source_count: int
    commander_count: int
    card_count: int
    last_generated_at: str
    version: str


@dataclass(slots=True)
class CatalogBuildResult:
    rows: List[CatalogRow]
    generated_at: str
    version: str
    output_path: Path


def normalize_theme_display(raw: str) -> str:
    trimmed = " ".join(raw.strip().split())
    return trimmed


def canonical_key(raw: str) -> str:
    return normalize_theme_display(raw).casefold()


def parse_theme_tags(value: object) -> List[str]:
    if value is None:
        return []
    # Handle numpy arrays (from Parquet files)
    if hasattr(value, '__array__') or hasattr(value, 'tolist'):
        try:
            value = value.tolist() if hasattr(value, 'tolist') else list(value)
        except Exception:
            pass
    if isinstance(value, list):
        return [str(v) for v in value if isinstance(v, str) and v.strip()]
    if isinstance(value, str):
        candidate = value.strip()
        if not candidate:
            return []
        # Try JSON parsing first (themeTags often stored as JSON arrays)
        try:
            parsed = json.loads(candidate)
        except json.JSONDecodeError:
            parsed = None
        if isinstance(parsed, list):
            return [str(v) for v in parsed if isinstance(v, str) and v.strip()]
        # Fallback to Python literal lists
        try:
            literal = ast.literal_eval(candidate)
        except (ValueError, SyntaxError):
            literal = None
        if isinstance(literal, list):
            return [str(v) for v in literal if isinstance(v, str) and v.strip()]
        return [candidate]
    return []


def _load_theme_counts_from_parquet(
    parquet_path: Path,
    theme_variants: Dict[str, set[str]]
) -> Counter[str]:
    """Load theme counts from a parquet file using pandas (which uses pyarrow).
    
    Args:
        parquet_path: Path to the parquet file (commander_cards.parquet or all_cards.parquet)
        theme_variants: Dict to accumulate theme name variants
        
    Returns:
        Counter of theme occurrences
    """
    if pd is None:
        print("  pandas not available, skipping parquet load")
        return Counter()
    
    counts: Counter[str] = Counter()
    
    if not parquet_path.exists():
        print(f"  Parquet file does not exist: {parquet_path}")
        return counts
    
    # Read only themeTags column for efficiency
    try:
        df = pd.read_parquet(parquet_path, columns=["themeTags"])
        print(f"  Loaded {len(df)} rows from parquet")
    except Exception as e:
        # If themeTags column doesn't exist, return empty
        print(f"  Failed to read themeTags column: {e}")
        return counts
    
    # Convert to list for fast iteration (faster than iterrows)
    theme_tags_list = df["themeTags"].tolist()
    
    # Debug: check first few entries
    non_empty_count = 0
    for i, raw_value in enumerate(theme_tags_list[:10]):
        if raw_value is not None and not (isinstance(raw_value, float) and pd.isna(raw_value)):
            non_empty_count += 1
            if i < 3:  # Show first 3 non-empty
                print(f"    Sample tag {i}: {raw_value!r} (type: {type(raw_value).__name__})")
    
    if non_empty_count == 0:
        print("  WARNING: No non-empty themeTags found in first 10 rows")
    
    for raw_value in theme_tags_list:
        if raw_value is None or (isinstance(raw_value, float) and pd.isna(raw_value)):
            continue
        tags = parse_theme_tags(raw_value)
        if not tags:
            continue
        seen_in_row: set[str] = set()
        for tag in tags:
            display = normalize_theme_display(tag)
            if not display:
                continue
            key = canonical_key(display)
            if key in seen_in_row:
                continue
            seen_in_row.add(key)
            counts[key] += 1
            theme_variants[key].add(display)
    
    print(f"  Found {len(counts)} unique themes from parquet")
    return counts


# CSV fallback removed in M4 migration - Parquet is now required


def _select_display_name(options: Sequence[str]) -> str:
    if not options:
        return ""

    def ranking(value: str) -> tuple[int, int, str, str]:
        all_upper = int(value == value.upper())
        title_case = int(value != value.title())
        return (all_upper, title_case, value.casefold(), value)

    return min(options, key=ranking)


def _derive_generated_at(now: Optional[datetime] = None) -> str:
    current = now or datetime.now(timezone.utc)
    without_microseconds = current.replace(microsecond=0)
    iso = without_microseconds.isoformat()
    return iso.replace("+00:00", "Z")


def _compute_version_hash(theme_names: Iterable[str]) -> str:
    joined = "\n".join(sorted(theme_names)).encode("utf-8")
    return hashlib.sha256(joined).hexdigest()[:12]


def build_theme_catalog(
    csv_directory: Path,
    output_path: Path,
    *,
    generated_at: Optional[datetime] = None,
    logs_directory: Optional[Path] = None,
    min_card_count: int = 3,
) -> CatalogBuildResult:
    """Build theme catalog from Parquet card data.
    
    Args:
        csv_directory: Base directory (used to locate card_files/processed/all_cards.parquet)
        output_path: Where to write the catalog CSV
        generated_at: Optional timestamp for generation
        logs_directory: Optional directory to copy output to
        min_card_count: Minimum number of cards required to include theme (default: 3)
        
    Returns:
        CatalogBuildResult with generated rows and metadata
        
    Raises:
        RuntimeError: If pandas/pyarrow not available
        FileNotFoundError: If all_cards.parquet doesn't exist
        RuntimeError: If no theme tags found in Parquet file
    """
    csv_directory = csv_directory.resolve()
    output_path = output_path.resolve()

    theme_variants: Dict[str, set[str]] = defaultdict(set)

    # Parquet-only mode (M4 migration: CSV files removed)
    if not HAS_PARQUET_SUPPORT:
        raise RuntimeError(
            "Pandas is required for theme catalog generation. "
            "Install with: pip install pandas pyarrow"
        )
    
    # Use processed parquet files (M4 migration)
    parquet_dir = csv_directory.parent / "card_files" / "processed"
    all_cards_parquet = parquet_dir / "all_cards.parquet"
    
    print(f"Loading theme data from parquet: {all_cards_parquet}")
    print(f"  File exists: {all_cards_parquet.exists()}")
    
    if not all_cards_parquet.exists():
        raise FileNotFoundError(
            f"Required Parquet file not found: {all_cards_parquet}\n"
            f"Run tagging first: python -c \"from code.tagging.tagger import run_tagging; run_tagging()\""
        )
    
    # Load all card counts from all_cards.parquet (includes commanders)
    card_counts = _load_theme_counts_from_parquet(
        all_cards_parquet, theme_variants=theme_variants
    )
    
    # For commander counts, filter all_cards by isCommander column
    df_commanders = pd.read_parquet(all_cards_parquet)
    if 'isCommander' in df_commanders.columns:
        df_commanders = df_commanders[df_commanders['isCommander']]
    else:
        # Fallback: assume all cards could be commanders if column missing
        pass
    commander_counts = Counter()
    for tags in df_commanders['themeTags'].tolist():
        if tags is None or (isinstance(tags, float) and pd.isna(tags)):
            continue
        # Functions are defined at top of this file, no import needed
        parsed = parse_theme_tags(tags)
        if not parsed:
            continue
        seen = set()
        for tag in parsed:
            display = normalize_theme_display(tag)
            if not display:
                continue
            key = canonical_key(display)
            if key not in seen:
                seen.add(key)
                commander_counts[key] += 1
                theme_variants[key].add(display)
    
    # Verify we found theme tags
    total_themes_found = len(card_counts) + len(commander_counts)
    if total_themes_found == 0:
        raise RuntimeError(
            f"No theme tags found in {all_cards_parquet}\n"
            f"The Parquet file exists but contains no themeTags data. "
            f"This usually means tagging hasn't completed or failed.\n"
            f"Check that 'themeTags' column exists and is populated."
        )
    
    print("✓ Loaded theme data from parquet files")
    print(f"  - Commanders: {len(commander_counts)} themes")
    print(f"  - All cards: {len(card_counts)} themes")

    keys = sorted(set(card_counts.keys()) | set(commander_counts.keys()))
    generated_at_iso = _derive_generated_at(generated_at)
    display_names = [_select_display_name(sorted(theme_variants[key])) for key in keys]
    version_hash = _compute_version_hash(display_names)

    rows: List[CatalogRow] = []
    filtered_count = 0
    for key, display in zip(keys, display_names):
        if not display:
            continue
        card_count = int(card_counts.get(key, 0))
        commander_count = int(commander_counts.get(key, 0))
        source_count = card_count + commander_count
        
        # Filter out themes below minimum threshold
        if source_count < min_card_count:
            filtered_count += 1
            continue
        
        rows.append(
            CatalogRow(
                theme=display,
                source_count=source_count,
                commander_count=commander_count,
                card_count=card_count,
                last_generated_at=generated_at_iso,
                version=version_hash,
            )
        )

    rows.sort(key=lambda row: (row.theme.casefold(), row.theme))

    output_path.parent.mkdir(parents=True, exist_ok=True)
    with output_path.open("w", encoding="utf-8", newline="") as handle:
        comment = (
            f"{HEADER_COMMENT_PREFIX} version={version_hash} "
            f"generated_at={generated_at_iso} total_themes={len(rows)}\n"
        )
        handle.write(comment)
        writer = csv.writer(handle)
        writer.writerow([
            "theme",
            "source_count",
            "commander_count",
            "card_count",
            "last_generated_at",
            "version",
        ])
        for row in rows:
            writer.writerow([
                row.theme,
                row.source_count,
                row.commander_count,
                row.card_count,
                row.last_generated_at,
                row.version,
            ])

    if filtered_count > 0:
        print(f"  Filtered {filtered_count} themes with <{min_card_count} cards")

    if logs_directory is not None:
        logs_directory = logs_directory.resolve()
        logs_directory.mkdir(parents=True, exist_ok=True)
        copy_path = logs_directory / output_path.name
        shutil.copyfile(output_path, copy_path)

    if not rows:
        raise RuntimeError(
            "No theme tags found while generating theme catalog; ensure card CSVs contain a themeTags column."
        )

    return CatalogBuildResult(rows=rows, generated_at=generated_at_iso, version=version_hash, output_path=output_path)


def _resolve_csv_directory(value: Optional[str]) -> Path:
    if value:
        return Path(value)
    env_override = os.environ.get("CSV_FILES_DIR")
    if env_override:
        return Path(env_override)
    return ROOT / DEFAULT_CSV_DIRECTORY


def main(argv: Optional[Sequence[str]] = None) -> CatalogBuildResult:
    parser = argparse.ArgumentParser(description="Generate a normalized theme catalog CSV.")
    parser.add_argument(
        "--csv-dir",
        dest="csv_dir",
        type=Path,
        default=None,
        help="Directory containing card CSV files (defaults to CSV_FILES_DIR or settings.CSV_DIRECTORY)",
    )
    parser.add_argument(
        "--output",
        dest="output",
        type=Path,
        default=DEFAULT_OUTPUT_PATH,
        help="Destination CSV path (defaults to config/themes/theme_catalog.csv)",
    )
    parser.add_argument(
        "--logs-dir",
        dest="logs_dir",
        type=Path,
        default=None,
        help="Optional directory to mirror the generated catalog for diffing (e.g., logs/generated)",
    )
    parser.add_argument(
        "--min-cards",
        dest="min_cards",
        type=int,
        default=3,
        help="Minimum number of cards required to include theme (default: 3)",
    )
    args = parser.parse_args(argv)

    csv_dir = _resolve_csv_directory(str(args.csv_dir) if args.csv_dir else None)
    result = build_theme_catalog(
        csv_directory=csv_dir,
        output_path=args.output,
        logs_directory=args.logs_dir,
        min_card_count=args.min_cards,
    )
    print(
        f"Generated {len(result.rows)} themes -> {result.output_path} (version={result.version})",
        file=sys.stderr,
    )
    return result


if __name__ == "__main__":  # pragma: no cover - CLI entrypoint
    main()