"""
Utilities for loading BIRD dataset column/value descriptions from
database_description/*.csv files (one CSV per table).

Adapted from CHESS (https://github.com/ShayanTalaei/CHESS)
chess/src/database_utils/db_catalog/csv_utils.py.

Schema returned:
    {
      table_name_lower: {
        column_name_lower: {
          "original_column_name": str,
          "column_name": str,          # expanded/human-readable name
          "column_description": str,
          "data_format": str,
          "value_description": str,
        }
      }
    }
"""

import logging
from pathlib import Path
from typing import Dict

import pandas as pd


def load_db_descriptions(
    db_directory_path: str,
    use_value_description: bool = True,
) -> Dict[str, Dict[str, Dict[str, str]]]:
    """Load table/column descriptions from BIRD database_description/*.csv.

    Args:
        db_directory_path: Path to the database directory (containing
            database_description/ sub-folder).
        use_value_description: Whether to include value_description field.

    Returns:
        Nested dict  table → column → field → value.
        Returns {} when the description folder does not exist.
    """
    encoding_types = ["utf-8-sig", "cp1252", "latin-1"]
    description_path = Path(db_directory_path) / "database_description"

    if not description_path.exists():
        return {}

    table_description: Dict[str, Dict[str, Dict[str, str]]] = {}

    for csv_file in sorted(description_path.glob("*.csv")):
        table_name = csv_file.stem.lower().strip()
        table_description[table_name] = {}
        loaded = False

        for encoding in encoding_types:
            try:
                df = pd.read_csv(csv_file, index_col=False, encoding=encoding)
                for _, row in df.iterrows():
                    col_key = str(row.get("original_column_name", "")).lower().strip()
                    if not col_key:
                        continue

                    def _clean(val, remove_prefix: str = "") -> str:
                        if not pd.notna(val):
                            return ""
                        s = str(val).replace("\n", " ").strip()
                        if remove_prefix and s.lower().startswith(remove_prefix):
                            s = s[len(remove_prefix):].strip()
                        return s

                    col_description = _clean(
                        row.get("column_description", ""),
                        remove_prefix="commonsense evidence:",
                    )
                    value_desc = ""
                    if use_value_description:
                        value_desc = _clean(
                            row.get("value_description", ""),
                            remove_prefix="commonsense evidence:",
                        )
                        if value_desc.lower().startswith("not useful"):
                            value_desc = value_desc[10:].strip()

                    table_description[table_name][col_key] = {
                        "original_column_name": str(row.get("original_column_name", col_key)),
                        "column_name": _clean(row.get("column_name", "")),
                        "column_description": col_description,
                        "data_format": _clean(row.get("data_format", "")),
                        "value_description": value_desc,
                    }

                logging.debug("Loaded descriptions from %s (%s)", csv_file, encoding)
                loaded = True
                break
            except Exception:
                continue

        if not loaded:
            logging.warning("Could not read descriptions from %s", csv_file)

    return table_description


def load_all_db_descriptions(
    bird_dataset_path: str,
    use_value_description: bool = True,
) -> Dict[str, Dict[str, Dict[str, Dict[str, str]]]]:
    """Load descriptions for every database under a BIRD split directory.

    Args:
        bird_dataset_path: Path to a BIRD split, e.g.
            /data/bird/train/train_databases
            Each sub-directory is one db_id.
        use_value_description: Whether to include value_description.

    Returns:
        {db_id: load_db_descriptions(db_dir)}
    """
    root = Path(bird_dataset_path)
    all_descriptions: Dict[str, Dict] = {}

    for db_dir in sorted(root.iterdir()):
        if db_dir.is_dir():
            db_id = db_dir.name
            all_descriptions[db_id] = load_db_descriptions(
                str(db_dir), use_value_description=use_value_description
            )

    return all_descriptions