import argparse
import json
import os
import pickle
import warnings
from itertools import chain

import joblib
import numpy as np
import pandas as pd
from galaxy_ml.externals.selene_sdk.utils import compute_score
from galaxy_ml.keras_galaxy_models import _predict_generator
from galaxy_ml.model_validations import train_test_split
from galaxy_ml.utils import (
    clean_params,
    get_main_estimator,
    get_module,
    get_scoring,
    load_model,
    read_columns,
    SafeEval,
    try_get_attr,
)
from scipy.io import mmread
from sklearn.metrics.scorer import _check_multimetric_scoring
from sklearn.model_selection import _search, _validation
from sklearn.model_selection._validation import _score
from sklearn.pipeline import Pipeline
from sklearn.utils import indexable, safe_indexing


_fit_and_score = try_get_attr("galaxy_ml.model_validations", "_fit_and_score")
setattr(_search, "_fit_and_score", _fit_and_score)
setattr(_validation, "_fit_and_score", _fit_and_score)

N_JOBS = int(os.environ.get("GALAXY_SLOTS", 1))
CACHE_DIR = os.path.join(os.getcwd(), "cached")
del os
NON_SEARCHABLE = ("n_jobs", "pre_dispatch", "memory", "_path", "nthread", "callbacks")
ALLOWED_CALLBACKS = (
    "EarlyStopping",
    "TerminateOnNaN",
    "ReduceLROnPlateau",
    "CSVLogger",
    "None",
)


def _eval_swap_params(params_builder):
    swap_params = {}

    for p in params_builder["param_set"]:
        swap_value = p["sp_value"].strip()
        if swap_value == "":
            continue

        param_name = p["sp_name"]
        if param_name.lower().endswith(NON_SEARCHABLE):
            warnings.warn("Warning: `%s` is not eligible for search and was " "omitted!" % param_name)
            continue

        if not swap_value.startswith(":"):
            safe_eval = SafeEval(load_scipy=True, load_numpy=True)
            ev = safe_eval(swap_value)
        else:
            # Have `:` before search list, asks for estimator evaluatio
            safe_eval_es = SafeEval(load_estimators=True)
            swap_value = swap_value[1:].strip()
            # TODO maybe add regular express check
            ev = safe_eval_es(swap_value)

        swap_params[param_name] = ev

    return swap_params


def train_test_split_none(*arrays, **kwargs):
    """extend train_test_split to take None arrays
    and support split by group names.
    """
    nones = []
    new_arrays = []
    for idx, arr in enumerate(arrays):
        if arr is None:
            nones.append(idx)
        else:
            new_arrays.append(arr)

    if kwargs["shuffle"] == "None":
        kwargs["shuffle"] = None

    group_names = kwargs.pop("group_names", None)

    if group_names is not None and group_names.strip():
        group_names = [name.strip() for name in group_names.split(",")]
        new_arrays = indexable(*new_arrays)
        groups = kwargs["labels"]
        n_samples = new_arrays[0].shape[0]
        index_arr = np.arange(n_samples)
        test = index_arr[np.isin(groups, group_names)]
        train = index_arr[~np.isin(groups, group_names)]
        rval = list(chain.from_iterable((safe_indexing(a, train), safe_indexing(a, test)) for a in new_arrays))
    else:
        rval = train_test_split(*new_arrays, **kwargs)

    for pos in nones:
        rval[pos * 2: 2] = [None, None]

    return rval


def _evaluate(y_true, pred_probas, scorer, is_multimetric=True):
    """output scores based on input scorer

    Parameters
    ----------
    y_true : array
        True label or target values
    pred_probas : array
        Prediction values, probability for classification problem
    scorer : dict
        dict of `sklearn.metrics.scorer.SCORER`
    is_multimetric : bool, default is True
    """
    if y_true.ndim == 1 or y_true.shape[-1] == 1:
        pred_probas = pred_probas.ravel()
        pred_labels = (pred_probas > 0.5).astype("int32")
        targets = y_true.ravel().astype("int32")
        if not is_multimetric:
            preds = pred_labels if scorer.__class__.__name__ == "_PredictScorer" else pred_probas
            score = scorer._score_func(targets, preds, **scorer._kwargs)

            return score
        else:
            scores = {}
            for name, one_scorer in scorer.items():
                preds = pred_labels if one_scorer.__class__.__name__ == "_PredictScorer" else pred_probas
                score = one_scorer._score_func(targets, preds, **one_scorer._kwargs)
                scores[name] = score

    # TODO: multi-class metrics
    # multi-label
    else:
        pred_labels = (pred_probas > 0.5).astype("int32")
        targets = y_true.astype("int32")
        if not is_multimetric:
            preds = pred_labels if scorer.__class__.__name__ == "_PredictScorer" else pred_probas
            score, _ = compute_score(preds, targets, scorer._score_func)
            return score
        else:
            scores = {}
            for name, one_scorer in scorer.items():
                preds = pred_labels if one_scorer.__class__.__name__ == "_PredictScorer" else pred_probas
                score, _ = compute_score(preds, targets, one_scorer._score_func)
                scores[name] = score

    return scores


def main(
    inputs,
    infile_estimator,
    infile1,
    infile2,
    outfile_result,
    outfile_object=None,
    outfile_weights=None,
    outfile_y_true=None,
    outfile_y_preds=None,
    groups=None,
    ref_seq=None,
    intervals=None,
    targets=None,
    fasta_path=None,
):
    """
    Parameter
    ---------
    inputs : str
        File path to galaxy tool parameter

    infile_estimator : str
        File path to estimator

    infile1 : str
        File path to dataset containing features

    infile2 : str
        File path to dataset containing target values

    outfile_result : str
        File path to save the results, either cv_results or test result

    outfile_object : str, optional
        File path to save searchCV object

    outfile_weights : str, optional
        File path to save deep learning model weights

    outfile_y_true : str, optional
        File path to target values for prediction

    outfile_y_preds : str, optional
        File path to save deep learning model weights

    groups : str
        File path to dataset containing groups labels

    ref_seq : str
        File path to dataset containing genome sequence file

    intervals : str
        File path to dataset containing interval file

    targets : str
        File path to dataset compressed target bed file

    fasta_path : str
        File path to dataset containing fasta file
    """
    warnings.simplefilter("ignore")

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

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

    estimator = clean_params(estimator)

    # swap hyperparameter
    swapping = params["experiment_schemes"]["hyperparams_swapping"]
    swap_params = _eval_swap_params(swapping)
    estimator.set_params(**swap_params)

    estimator_params = estimator.get_params()

    # 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"))

    # fasta_file input
    elif input_type == "seq_fasta":
        pyfaidx = get_module("pyfaidx")
        sequences = pyfaidx.Fasta(fasta_path)
        n_seqs = len(sequences.keys())
        X = np.arange(n_seqs)[:, np.newaxis]
        for param in estimator_params.keys():
            if param.endswith("fasta_path"):
                estimator.set_params(**{param: fasta_path})
                break
        else:
            raise ValueError(
                "The selected estimator doesn't support "
                "fasta file input! Please consider using "
                "KerasGBatchClassifier with "
                "FastaDNABatchGenerator/FastaProteinBatchGenerator "
                "or having GenomeOneHotEncoder/ProteinOneHotEncoder "
                "in pipeline!"
            )

    elif input_type == "refseq_and_interval":
        path_params = {
            "data_batch_generator__ref_genome_path": ref_seq,
            "data_batch_generator__intervals_path": intervals,
            "data_batch_generator__target_path": targets,
        }
        estimator.set_params(**path_params)
        n_intervals = sum(1 for line in open(intervals))
        X = np.arange(n_intervals)[:, np.newaxis]

    # 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()
    if input_type == "refseq_and_interval":
        estimator.set_params(data_batch_generator__features=y.ravel().tolist())
        y = None
    # end y

    # load groups
    if groups:
        groups_selector = (params["experiment_schemes"]["test_split"]["split_algos"]).pop("groups_selector")

        header = "infer" if groups_selector["header_g"] else None
        column_option = groups_selector["column_selector_options_g"]["selected_column_selector_option_g"]
        if column_option in [
            "by_index_number",
            "all_but_by_index_number",
            "by_header_name",
            "all_but_by_header_name",
        ]:
            c = groups_selector["column_selector_options_g"]["col_g"]
        else:
            c = None

        df_key = groups + repr(header)
        if df_key in loaded_df:
            groups = loaded_df[df_key]

        groups = read_columns(groups,
                              c=c,
                              c_option=column_option,
                              sep='\t',
                              header=header,
                              parse_dates=True)
        groups = groups.ravel()

    # del loaded_df
    del loaded_df

    # cache iraps_core fits could increase search speed significantly
    memory = joblib.Memory(location=CACHE_DIR, verbose=0)
    main_est = get_main_estimator(estimator)
    if main_est.__class__.__name__ == "IRAPSClassifier":
        main_est.set_params(memory=memory)

    # handle scorer, convert to scorer dict
    scoring = params['experiment_schemes']['metrics']['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)

    # handle test (first) split
    test_split_options = params["experiment_schemes"]["test_split"]["split_algos"]

    if test_split_options["shuffle"] == "group":
        test_split_options["labels"] = groups
    if test_split_options["shuffle"] == "stratified":
        if y is not None:
            test_split_options["labels"] = y
        else:
            raise ValueError("Stratified shuffle split is not " "applicable on empty target values!")

    (
        X_train,
        X_test,
        y_train,
        y_test,
        groups_train,
        _groups_test,
    ) = train_test_split_none(X, y, groups, **test_split_options)

    exp_scheme = params["experiment_schemes"]["selected_exp_scheme"]

    # handle validation (second) split
    if exp_scheme == "train_val_test":
        val_split_options = params["experiment_schemes"]["val_split"]["split_algos"]

        if val_split_options["shuffle"] == "group":
            val_split_options["labels"] = groups_train
        if val_split_options["shuffle"] == "stratified":
            if y_train is not None:
                val_split_options["labels"] = y_train
            else:
                raise ValueError("Stratified shuffle split is not " "applicable on empty target values!")

        (
            X_train,
            X_val,
            y_train,
            y_val,
            groups_train,
            _groups_val,
        ) = train_test_split_none(X_train, y_train, groups_train, **val_split_options)

    # train and eval
    if hasattr(estimator, "validation_data"):
        if exp_scheme == "train_val_test":
            estimator.fit(X_train, y_train, validation_data=(X_val, y_val))
        else:
            estimator.fit(X_train, y_train, validation_data=(X_test, y_test))
    else:
        estimator.fit(X_train, y_train)

    if hasattr(estimator, "evaluate"):
        steps = estimator.prediction_steps
        batch_size = estimator.batch_size
        generator = estimator.data_generator_.flow(X_test, y=y_test, batch_size=batch_size)
        predictions, y_true = _predict_generator(estimator.model_, generator, steps=steps)
        scores = _evaluate(y_true, predictions, scorer, is_multimetric=True)

    else:
        if hasattr(estimator, "predict_proba"):
            predictions = estimator.predict_proba(X_test)
        else:
            predictions = estimator.predict(X_test)

        y_true = y_test
        scores = _score(estimator, X_test, y_test, scorer, is_multimetric=True)
    if outfile_y_true:
        try:
            pd.DataFrame(y_true).to_csv(outfile_y_true, sep="\t", index=False)
            pd.DataFrame(predictions).astype(np.float32).to_csv(
                outfile_y_preds,
                sep="\t",
                index=False,
                float_format="%g",
                chunksize=10000,
            )
        except Exception as e:
            print("Error in saving predictions: %s" % e)

    # 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_result, sep="\t", header=True, index=False)

    memory.clear(warn=False)

    if outfile_object:
        main_est = estimator
        if isinstance(estimator, Pipeline):
            main_est = estimator.steps[-1][-1]

        if hasattr(main_est, "model_") and hasattr(main_est, "save_weights"):
            if outfile_weights:
                main_est.save_weights(outfile_weights)
            del main_est.model_
            del main_est.fit_params
            del main_est.model_class_
            if getattr(main_est, "validation_data", None):
                del main_est.validation_data
            if getattr(main_est, "data_generator_", None):
                del main_est.data_generator_

        with open(outfile_object, "wb") as output_handler:
            pickle.dump(estimator, output_handler, pickle.HIGHEST_PROTOCOL)


if __name__ == "__main__":
    aparser = argparse.ArgumentParser()
    aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
    aparser.add_argument("-e", "--estimator", dest="infile_estimator")
    aparser.add_argument("-X", "--infile1", dest="infile1")
    aparser.add_argument("-y", "--infile2", dest="infile2")
    aparser.add_argument("-O", "--outfile_result", dest="outfile_result")
    aparser.add_argument("-o", "--outfile_object", dest="outfile_object")
    aparser.add_argument("-w", "--outfile_weights", dest="outfile_weights")
    aparser.add_argument("-l", "--outfile_y_true", dest="outfile_y_true")
    aparser.add_argument("-p", "--outfile_y_preds", dest="outfile_y_preds")
    aparser.add_argument("-g", "--groups", dest="groups")
    aparser.add_argument("-r", "--ref_seq", dest="ref_seq")
    aparser.add_argument("-b", "--intervals", dest="intervals")
    aparser.add_argument("-t", "--targets", dest="targets")
    aparser.add_argument("-f", "--fasta_path", dest="fasta_path")
    args = aparser.parse_args()

    main(
        args.inputs,
        args.infile_estimator,
        args.infile1,
        args.infile2,
        args.outfile_result,
        outfile_object=args.outfile_object,
        outfile_weights=args.outfile_weights,
        outfile_y_true=args.outfile_y_true,
        outfile_y_preds=args.outfile_y_preds,
        groups=args.groups,
        ref_seq=args.ref_seq,
        intervals=args.intervals,
        targets=args.targets,
        fasta_path=args.fasta_path,
    )