import argparse
import json
import warnings

import numpy as np
import pandas as pd
from galaxy_ml.utils import get_module, load_model, read_columns, try_get_attr
from scipy.io import mmread
from sklearn.pipeline import Pipeline

N_JOBS = int(__import__("os").environ.get("GALAXY_SLOTS", 1))


def main(
    inputs,
    infile_estimator,
    outfile_predict,
    infile_weights=None,
    infile1=None,
    fasta_path=None,
    ref_seq=None,
    vcf_path=None,
):
    """
    Parameter
    ---------
    inputs : str
        File path to galaxy tool parameter

    infile_estimator : strgit
        File path to trained estimator input

    outfile_predict : str
        File path to save the prediction results, tabular

    infile_weights : str
        File path to weights input

    infile1 : str
        File path to dataset containing features

    fasta_path : str
        File path to dataset containing fasta file

    ref_seq : str
        File path to dataset containing the reference genome sequence.

    vcf_path : str
        File path to dataset containing variants info.
    """
    warnings.filterwarnings("ignore")

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

    # 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 dataset for weights wan not selected!"
            )
        main_est.load_weights(infile_weights)

    # handle data input
    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 = pd.read_csv(infile1, sep="\t", header=header, parse_dates=True)

        X = read_columns(df, c=c, c_option=column_option).astype(float)

        if params["method"] == "predict":
            preds = estimator.predict(X)
        else:
            preds = estimator.predict_proba(X)

    # sparse input
    elif input_type == "sparse":
        X = mmread(open(infile1, "r"))
        if params["method"] == "predict":
            preds = estimator.predict(X)
        else:
            preds = estimator.predict_proba(X)

    # fasta input
    elif input_type == "seq_fasta":
        if not hasattr(estimator, "data_batch_generator"):
            raise ValueError(
                "To do prediction on sequences in fasta input, "
                "the estimator must be a `KerasGBatchClassifier`"
                "equipped with data_batch_generator!"
            )
        pyfaidx = get_module("pyfaidx")
        sequences = pyfaidx.Fasta(fasta_path)
        n_seqs = len(sequences.keys())
        X = np.arange(n_seqs)[:, np.newaxis]
        seq_length = estimator.data_batch_generator.seq_length
        batch_size = getattr(estimator, "batch_size", 32)
        steps = (n_seqs + batch_size - 1) // batch_size

        seq_type = params["input_options"]["seq_type"]
        klass = try_get_attr("galaxy_ml.preprocessors", seq_type)

        pred_data_generator = klass(fasta_path, seq_length=seq_length)

        if params["method"] == "predict":
            preds = estimator.predict(
                X, data_generator=pred_data_generator, steps=steps
            )
        else:
            preds = estimator.predict_proba(
                X, data_generator=pred_data_generator, steps=steps
            )

    # vcf input
    elif input_type == "variant_effect":
        klass = try_get_attr("galaxy_ml.preprocessors", "GenomicVariantBatchGenerator")

        options = params["input_options"]
        options.pop("selected_input")
        if options["blacklist_regions"] == "none":
            options["blacklist_regions"] = None

        pred_data_generator = klass(
            ref_genome_path=ref_seq, vcf_path=vcf_path, **options
        )

        pred_data_generator.set_processing_attrs()

        variants = pred_data_generator.variants

        # predict 1600 sample at once then write to file
        gen_flow = pred_data_generator.flow(batch_size=1600)

        file_writer = open(outfile_predict, "w")
        header_row = "\t".join(["chrom", "pos", "name", "ref", "alt", "strand"])
        file_writer.write(header_row)
        header_done = False

        steps_done = 0

        # TODO: multiple threading
        try:
            while steps_done < len(gen_flow):
                index_array = next(gen_flow.index_generator)
                batch_X = gen_flow._get_batches_of_transformed_samples(index_array)

                if params["method"] == "predict":
                    batch_preds = estimator.predict(
                        batch_X,
                        # The presence of `pred_data_generator` below is to
                        # override model carrying data_generator if there
                        # is any.
                        data_generator=pred_data_generator,
                    )
                else:
                    batch_preds = estimator.predict_proba(
                        batch_X,
                        # The presence of `pred_data_generator` below is to
                        # override model carrying data_generator if there
                        # is any.
                        data_generator=pred_data_generator,
                    )

                if batch_preds.ndim == 1:
                    batch_preds = batch_preds[:, np.newaxis]

                batch_meta = variants[index_array]
                batch_out = np.column_stack([batch_meta, batch_preds])

                if not header_done:
                    heads = np.arange(batch_preds.shape[-1]).astype(str)
                    heads_str = "\t".join(heads)
                    file_writer.write("\t%s\n" % heads_str)
                    header_done = True

                for row in batch_out:
                    row_str = "\t".join(row)
                    file_writer.write("%s\n" % row_str)

                steps_done += 1

        finally:
            file_writer.close()
            # TODO: make api `pred_data_generator.close()`
            pred_data_generator.close()
        return 0
    # end input

    # output
    if len(preds.shape) == 1:
        rval = pd.DataFrame(preds, columns=["Predicted"])
    else:
        rval = pd.DataFrame(preds)

    rval.to_csv(outfile_predict, 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("-O", "--outfile_predict", dest="outfile_predict")
    aparser.add_argument("-f", "--fasta_path", dest="fasta_path")
    aparser.add_argument("-r", "--ref_seq", dest="ref_seq")
    aparser.add_argument("-v", "--vcf_path", dest="vcf_path")
    args = aparser.parse_args()

    main(
        args.inputs,
        args.infile_estimator,
        args.outfile_predict,
        infile_weights=args.infile_weights,
        infile1=args.infile1,
        fasta_path=args.fasta_path,
        ref_seq=args.ref_seq,
        vcf_path=args.vcf_path,
    )