import argparse
import json
import warnings

import pandas as pd
from galaxy_ml.utils import get_scoring, load_model, read_columns
from scipy.io import mmread
from sklearn.metrics.scorer import _check_multimetric_scoring
from sklearn.model_selection._validation import _score
from sklearn.pipeline import Pipeline


def _get_X_y(params, infile1, infile2):
    """read from inputs and output X and y

    Parameters
    ----------
    params : dict
        Tool inputs parameter
    infile1 : str
        File path to dataset containing features
    infile2 : str
        File path to dataset containing target values

    """
    # store read dataframe object
    loaded_df = {}

    input_type = params["input_options"]["selected_input"]
    # tabular input
    if input_type == "tabular":
        header = "infer" if params["input_options"]["header1"] else None
        column_option = params["input_options"]["column_selector_options_1"]["selected_column_selector_option"]
        if column_option in [
            "by_index_number",
            "all_but_by_index_number",
            "by_header_name",
            "all_but_by_header_name",
        ]:
            c = params["input_options"]["column_selector_options_1"]["col1"]
        else:
            c = None

        df_key = infile1 + repr(header)
        df = pd.read_csv(infile1, sep="\t", header=header, parse_dates=True)
        loaded_df[df_key] = df

        X = read_columns(df, c=c, c_option=column_option).astype(float)
    # sparse input
    elif input_type == "sparse":
        X = mmread(open(infile1, "r"))

    # Get target y
    header = "infer" if params["input_options"]["header2"] else None
    column_option = params["input_options"]["column_selector_options_2"]["selected_column_selector_option2"]
    if column_option in [
        "by_index_number",
        "all_but_by_index_number",
        "by_header_name",
        "all_but_by_header_name",
    ]:
        c = params["input_options"]["column_selector_options_2"]["col2"]
    else:
        c = None

    df_key = infile2 + repr(header)
    if df_key in loaded_df:
        infile2 = loaded_df[df_key]
    else:
        infile2 = pd.read_csv(infile2, sep="\t", header=header, parse_dates=True)
        loaded_df[df_key] = infile2

    y = read_columns(infile2, c=c, c_option=column_option, sep="\t", header=header, parse_dates=True)
    if len(y.shape) == 2 and y.shape[1] == 1:
        y = y.ravel()

    return X, y


def main(
    inputs,
    infile_estimator,
    outfile_eval,
    infile_weights=None,
    infile1=None,
    infile2=None,
):
    """
    Parameter
    ---------
    inputs : str
        File path to galaxy tool parameter

    infile_estimator : strgit
        File path to trained estimator input

    outfile_eval : str
        File path to save the evalulation results, tabular

    infile_weights : str
        File path to weights input

    infile1 : str
        File path to dataset containing features

    infile2 : str
        File path to dataset containing target values
    """
    warnings.filterwarnings("ignore")

    with open(inputs, "r") as param_handler:
        params = json.load(param_handler)

    X_test, y_test = _get_X_y(params, infile1, infile2)

    # load model
    with open(infile_estimator, "rb") as est_handler:
        estimator = load_model(est_handler)

    main_est = estimator
    if isinstance(estimator, Pipeline):
        main_est = estimator.steps[-1][-1]
    if hasattr(main_est, "config") and hasattr(main_est, "load_weights"):
        if not infile_weights or infile_weights == "None":
            raise ValueError(
                "The selected model skeleton asks for weights, " "but no dataset for weights was provided!"
            )
        main_est.load_weights(infile_weights)

    # handle scorer, convert to scorer dict
    # Check if scoring is specified
    scoring = params["scoring"]
    if scoring is not None:
        # get_scoring() expects secondary_scoring to be a comma separated string (not a list)
        # Check if secondary_scoring is specified
        secondary_scoring = scoring.get("secondary_scoring", None)
        if secondary_scoring is not None:
            # If secondary_scoring is specified, convert the list into comman separated string
            scoring["secondary_scoring"] = ",".join(scoring["secondary_scoring"])

    scorer = get_scoring(scoring)
    scorer, _ = _check_multimetric_scoring(estimator, scoring=scorer)

    if hasattr(estimator, "evaluate"):
        scores = estimator.evaluate(X_test, y_test=y_test, scorer=scorer, is_multimetric=True)
    else:
        scores = _score(estimator, X_test, y_test, scorer, is_multimetric=True)

    # handle output
    for name, score in scores.items():
        scores[name] = [score]
    df = pd.DataFrame(scores)
    df = df[sorted(df.columns)]
    df.to_csv(path_or_buf=outfile_eval, sep="\t", header=True, index=False)


if __name__ == "__main__":
    aparser = argparse.ArgumentParser()
    aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
    aparser.add_argument("-e", "--infile_estimator", dest="infile_estimator")
    aparser.add_argument("-w", "--infile_weights", dest="infile_weights")
    aparser.add_argument("-X", "--infile1", dest="infile1")
    aparser.add_argument("-y", "--infile2", dest="infile2")
    aparser.add_argument("-O", "--outfile_eval", dest="outfile_eval")
    args = aparser.parse_args()

    main(
        args.inputs,
        args.infile_estimator,
        args.outfile_eval,
        infile_weights=args.infile_weights,
        infile1=args.infile1,
        infile2=args.infile2,
    )