import logging
import duckdb
import pandas as pd
import polars as pl
import pyarrow as pa
from coded_flows.types import Union, List, DataFrame, ArrowTable

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


def _coalesce(*values):
    return next((v for v in values if v is not None))


def _sanitize_identifier(identifier):
    """
    Sanitize SQL identifier by escaping special characters.
    Handles double quotes and other problematic characters.
    """
    return identifier.replace('"', '""')


def keep_columns(
    data: Union[DataFrame, ArrowTable], columns: List = None, options=None
) -> Union[DataFrame, ArrowTable]:
    brick_display_name = "Keep Columns"
    options = options or {}
    verbose = options.get("verbose", True)
    columns = _coalesce(columns, options.get("columns", []))
    output_format = options.get("output_format", "pandas")
    safe_mode = options.get("safe_mode", False)
    result = None
    no_filter = False
    if not columns:
        verbose and logger.warning(
            f"[{brick_display_name}] No columns specified. Returning data unchanged."
        )
        result = data
        no_filter = True
    if not no_filter:
        if not isinstance(columns, list) and (
            not all((isinstance(c, str) for c in columns))
        ):
            verbose and logger.error(f"[{brick_display_name}] Invalid columns format!")
            raise ValueError("Columns must be provided as a list!")
        try:
            verbose and logger.info(
                f"[{brick_display_name}] Starting column filter operation with {len(columns)} columns to keep."
            )
            data_type = None
            if isinstance(data, pd.DataFrame):
                data_type = "pandas"
            elif isinstance(data, pl.DataFrame):
                data_type = "polars"
            elif isinstance(data, (pa.Table, pa.lib.Table)):
                data_type = "arrow"
            if data_type is None:
                verbose and logger.error(
                    f"[{brick_display_name}] Input data must be a pandas DataFrame, Polars DataFrame, or Arrow Table"
                )
                raise ValueError(
                    "Input data must be a pandas DataFrame, Polars DataFrame, or Arrow Table"
                )
            verbose and logger.info(
                f"[{brick_display_name}] Detected input format: {data_type}."
            )
            conn = duckdb.connect(":memory:")
            conn.register("input_table", data)
            verbose and logger.info(
                f"[{brick_display_name}] Building SQL query for column filtering."
            )
            column_info = conn.execute("DESCRIBE input_table").fetchall()
            all_columns = [col[0] for col in column_info]
            if not safe_mode:
                missing_columns = [col for col in columns if col not in all_columns]
                if missing_columns:
                    verbose and logger.error(
                        f"[{brick_display_name}] Columns not found in data: {missing_columns}"
                    )
                    conn.close()
                    raise ValueError(f"Columns not found in data: {missing_columns}")
            select_parts = []
            kept_count = 0
            skipped_count = 0
            for col in columns:
                if col in all_columns:
                    sanitized_col = _sanitize_identifier(col)
                    select_parts.append(f'"{sanitized_col}"')
                    verbose and logger.info(
                        f"[{brick_display_name}] Keeping column: {col}."
                    )
                    kept_count += 1
                elif safe_mode:
                    verbose and logger.warning(
                        f"[{brick_display_name}] Safe mode: Skipping non-existent column: {col}"
                    )
                    skipped_count += 1
            if not select_parts:
                verbose and logger.error(
                    f"[{brick_display_name}] No valid columns to keep! Result would be empty."
                )
                conn.close()
                raise ValueError(
                    "No valid columns to keep! All specified columns are missing from the data."
                )
            select_clause = ", ".join(select_parts)
            query = f"SELECT {select_clause} FROM input_table"
            verbose and logger.info(
                f"[{brick_display_name}] Executing query to filter columns."
            )
            if output_format == "pandas":
                result = conn.execute(query).df()
                verbose and logger.info(
                    f"[{brick_display_name}] Converted result to pandas DataFrame."
                )
            elif output_format == "polars":
                result = conn.execute(query).pl()
                verbose and logger.info(
                    f"[{brick_display_name}] Converted result to Polars DataFrame."
                )
            elif output_format == "arrow":
                result = conn.execute(query).fetch_arrow_table()
                verbose and logger.info(
                    f"[{brick_display_name}] Converted result to Arrow Table."
                )
            else:
                verbose and logger.error(
                    f"[{brick_display_name}] Unsupported output format: {output_format}"
                )
                raise ValueError(f"Unsupported output format: {output_format}")
            conn.close()
            verbose and logger.info(
                f"[{brick_display_name}] Column filter operation completed successfully. Kept {kept_count} columns{(f', skipped {skipped_count} missing columns' if safe_mode and skipped_count > 0 else '')}."
            )
        except Exception as e:
            verbose and logger.error(
                f"[{brick_display_name}] Error during column filter operation."
            )
            raise
    return result