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

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


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


def force_numerical_range(
    data: Union[DataFrame, ArrowTable], input_columns: List = None, options=None
) -> Union[DataFrame, ArrowTable]:
    brick_display_name = "Force Numerical Range"
    options = options or {}
    verbose = options.get("verbose", True)
    input_columns = input_columns or options.get("input_columns", [])
    action = options.get("action", "clip_outliers")
    minimum_value = options.get("minimum_value", 0)
    use_minimum = options.get("use_minimum", False)
    maximum_value = options.get("maximum_value", 100)
    use_maximum = options.get("use_maximum", False)
    safe_mode = options.get("safe_mode", False)
    output_format = options.get("output_format", "pandas")
    result = None
    try:
        verbose and logger.info(
            f"[{brick_display_name}] Starting force numerical range operation."
        )
        if not input_columns or len(input_columns) == 0:
            verbose and logger.error(
                f"[{brick_display_name}] At least one input column must be specified."
            )
            raise ValueError("At least one input column must be specified")
        if not isinstance(input_columns, list) or not all(
            (isinstance(c, str) for c in input_columns)
        ):
            verbose and logger.error(
                f"[{brick_display_name}] Input columns must be provided as a list of strings."
            )
            raise ValueError("Input columns must be provided as a list of strings")
        if not use_minimum and (not use_maximum):
            verbose and logger.error(
                f"[{brick_display_name}] At least one bound (lower or upper) must be specified."
            )
            raise ValueError("At least one bound (lower or upper) must be specified")
        if use_minimum and use_maximum and (minimum_value >= maximum_value):
            verbose and logger.error(
                f"[{brick_display_name}] Lower bound ({minimum_value}) must be less than upper bound ({maximum_value})."
            )
            raise ValueError(
                f"Lower bound ({minimum_value}) must be less than upper bound ({maximum_value})"
            )
        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)
        column_info = conn.execute("DESCRIBE input_table").fetchall()
        all_columns = {col[0]: col[1] for col in column_info}
        verbose and logger.info(
            f"[{brick_display_name}] Processing {len(input_columns)} column(s) for range enforcement."
        )
        numeric_types = [
            "INTEGER",
            "BIGINT",
            "SMALLINT",
            "TINYINT",
            "DOUBLE",
            "FLOAT",
            "REAL",
            "DECIMAL",
            "NUMERIC",
            "HUGEINT",
        ]
        valid_columns = []
        invalid_columns = []
        for col in input_columns:
            if col not in all_columns:
                if safe_mode:
                    verbose and logger.warning(
                        f"[{brick_display_name}] Column '{col}' not found in data. Skipping."
                    )
                    invalid_columns.append(col)
                    continue
                else:
                    verbose and logger.error(
                        f"[{brick_display_name}] Column '{col}' not found in data."
                    )
                    conn.close()
                    raise ValueError(f"Column '{col}' not found in data")
            column_type = all_columns[col].upper()
            is_numeric = any((num_type in column_type for num_type in numeric_types))
            if not is_numeric:
                error_message = f"Column '{col}' is not numeric (type: {column_type})"
                if safe_mode:
                    verbose and logger.warning(
                        f"[{brick_display_name}] {error_message}. Skipping."
                    )
                    invalid_columns.append(col)
                    continue
                else:
                    verbose and logger.error(f"[{brick_display_name}] {error_message}.")
                    conn.close()
                    raise ValueError(error_message)
            valid_columns.append(col)
            verbose and logger.info(
                f"[{brick_display_name}] Column '{col}' is numeric (type: {column_type})."
            )
        if not valid_columns:
            if safe_mode:
                verbose and logger.warning(
                    f"[{brick_display_name}] No valid numeric columns to process. Returning original data."
                )
                conn.close()
                result = data
            else:
                verbose and logger.error(
                    f"[{brick_display_name}] No valid numeric columns to process."
                )
                conn.close()
                raise ValueError("No valid numeric columns to process")
        else:
            verbose and logger.info(
                f"[{brick_display_name}] {len(valid_columns)} valid column(s) will be processed."
            )
            if action == "clip_outliers":
                verbose and logger.info(
                    f"[{brick_display_name}] Action: Clip outliers (replace with bounds)."
                )
            elif action == "clear_outliers":
                verbose and logger.info(
                    f"[{brick_display_name}] Action: Clear outliers (set to NULL)."
                )
            else:
                verbose and logger.error(
                    f"[{brick_display_name}] Unknown action: {action}"
                )
                conn.close()
                raise ValueError(f"Unknown action: {action}")
            if use_minimum:
                verbose and logger.info(
                    f"[{brick_display_name}] Lower bound: {minimum_value}"
                )
            if use_maximum:
                verbose and logger.info(
                    f"[{brick_display_name}] Upper bound: {maximum_value}"
                )
            select_parts = []
            for col_name in all_columns.keys():
                sanitized_col = _sanitize_identifier(col_name)
                if col_name in valid_columns:
                    if action == "clip_outliers":
                        case_parts = []
                        if use_minimum and use_maximum:
                            case_parts.append(
                                f'WHEN "{sanitized_col}" < {minimum_value} THEN {minimum_value}'
                            )
                            case_parts.append(
                                f'WHEN "{sanitized_col}" > {maximum_value} THEN {maximum_value}'
                            )
                            case_parts.append(f'ELSE "{sanitized_col}"')
                        elif use_minimum:
                            case_parts.append(
                                f'WHEN "{sanitized_col}" < {minimum_value} THEN {minimum_value}'
                            )
                            case_parts.append(f'ELSE "{sanitized_col}"')
                        elif use_maximum:
                            case_parts.append(
                                f'WHEN "{sanitized_col}" > {maximum_value} THEN {maximum_value}'
                            )
                            case_parts.append(f'ELSE "{sanitized_col}"')
                        case_statement = "CASE " + " ".join(case_parts) + " END"
                        select_parts.append(f'{case_statement} AS "{sanitized_col}"')
                    elif action == "clear_outliers":
                        case_parts = []
                        if use_minimum and use_maximum:
                            case_parts.append(
                                f'WHEN "{sanitized_col}" >= {minimum_value} AND "{sanitized_col}" <= {maximum_value} THEN "{sanitized_col}"'
                            )
                            case_parts.append("ELSE NULL")
                        elif use_minimum:
                            case_parts.append(
                                f'WHEN "{sanitized_col}" >= {minimum_value} THEN "{sanitized_col}"'
                            )
                            case_parts.append("ELSE NULL")
                        elif use_maximum:
                            case_parts.append(
                                f'WHEN "{sanitized_col}" <= {maximum_value} THEN "{sanitized_col}"'
                            )
                            case_parts.append("ELSE NULL")
                        case_statement = "CASE " + " ".join(case_parts) + " END"
                        select_parts.append(f'{case_statement} AS "{sanitized_col}"')
                else:
                    select_parts.append(f'"{sanitized_col}"')
            select_clause = ", ".join(select_parts)
            query = f"SELECT {select_clause} FROM input_table"
            verbose and logger.info(
                f"[{brick_display_name}] Executing range enforcement query."
            )
            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}"
                )
                conn.close()
                raise ValueError(f"Unsupported output format: {output_format}")
            conn.close()
            result_rows = len(result)
            verbose and logger.info(
                f"[{brick_display_name}] Range enforcement completed successfully. Processed {len(valid_columns)} column(s). Returned {result_rows} rows."
            )
    except Exception as e:
        verbose and logger.error(
            f"[{brick_display_name}] Error during range enforcement operation."
        )
        raise
    return result