import logging
import pandas as pd
import polars as pl
import pyarrow as pa
import numpy as np
from coded_flows.types import Union, DataFrame, ArrowTable, Any, List
from coded_flows.utils import CodedFlowsLogger

logger = CodedFlowsLogger(name="Apply PCA Transform", level=logging.INFO)


def _convert_to_pandas(data: Any, verbose: bool) -> pd.DataFrame:
    """Helper to safely convert input to Pandas DataFrame."""
    df = None
    if isinstance(data, pd.DataFrame):
        df = data
    elif isinstance(data, pl.DataFrame):
        verbose and logger.info("Converting Polars DataFrame to Pandas.")
        df = data.to_pandas()
    elif isinstance(data, (pa.Table, pa.lib.Table)):
        verbose and logger.info("Converting Arrow Table to Pandas.")
        df = data.to_pandas()
    elif isinstance(data, np.ndarray):
        verbose and logger.info("Converting NumPy array to Pandas.")
        df = pd.DataFrame(data)
    else:
        raise ValueError(
            f"Unsupported data type: {type(data)}. Expected DataFrame, Table, or Array."
        )
    return df


def _get_feature_columns(df: pd.DataFrame, pca_model: Any, verbose: bool) -> List[str]:
    """
    Helper to determine which columns to use.
    Prioritizes model metadata, falls back to all numeric.
    """
    cols = []
    if hasattr(pca_model, "feature_names_in_"):
        cols = list(pca_model.feature_names_in_)
        verbose and logger.info(
            f"Inferred {len(cols)} columns from trained PCA model metadata."
        )
    else:
        cols = df.select_dtypes(include=["number", "bool"]).columns.tolist()
        verbose and logger.warning(
            "No model metadata found. Using all numeric columns available in input."
        )
    missing = [c for c in cols if c not in df.columns]
    if missing:
        raise ValueError(
            f"The following required columns are missing in data: {missing}"
        )
    return cols


def apply_pca_transform(
    data: Union[DataFrame, ArrowTable], PCA: Any, Scaler: Any = None, options=None
) -> DataFrame:
    options = options or {}
    verbose = options.get("verbose", True)
    col_prefix = options.get("col_prefix", "PC")
    projected_data = pd.DataFrame()
    try:
        verbose and logger.info("Starting PCA Transformation.")
        if PCA is None:
            raise ValueError("PCA Model input is missing (None).")
        df = _convert_to_pandas(data, verbose)
        if df.empty:
            raise ValueError("Input DataFrame is empty.")
        feature_cols = _get_feature_columns(df, PCA, verbose)
        if hasattr(PCA, "n_features_in_"):
            if len(feature_cols) != PCA.n_features_in_:
                raise ValueError(
                    f"Model expects {PCA.n_features_in_} features, but {len(feature_cols)} features were identified in the input."
                )
        X = df[feature_cols].values
        if Scaler is not None:
            verbose and logger.info("Applying provided Scaler to data.")
            try:
                X = Scaler.transform(X)
            except Exception as e:
                raise ValueError(f"Failed to apply Scaler: {e}")
        else:
            verbose and logger.info("No Scaler provided. Proceeding with raw data.")
        verbose and logger.info("Projecting data into PCA space.")
        try:
            X_pca = PCA.transform(X)
        except Exception as e:
            raise ValueError(f"PCA transform failed: {e}")
        n_components = X_pca.shape[1]
        pc_col_names = [f"{col_prefix}{i + 1}" for i in range(n_components)]
        verbose and logger.info(
            f"Generated {n_components} components. Merging with original data."
        )
        projected_data = pd.DataFrame(X_pca, columns=pc_col_names, index=df.index)
        verbose and logger.info(
            f"Transformation complete. Output shape: {projected_data.shape}"
        )
    except Exception as e:
        verbose and logger.error(f"Error during PCA transformation: {e}")
        raise
    return projected_data