"""Run quality control on phenotypes-specific files in the bundle."""
import sys
import shutil
import tempfile
from pathlib import Path
from zipfile import ZipFile
import multiprocessing as mproc
from functools import reduce, partial
from typing import Callable, Optional, Sequence
from logging import Logger, getLogger, StreamHandler

import MySQLdb as mdb

from r_qtl import r_qtl2 as rqtl2
from r_qtl import r_qtl2_qc as rqc
from r_qtl import exceptions as rqe
from r_qtl.fileerrors import InvalidValue

from quality_control.checks import decimal_places_pattern

from uploader.files import sha256_digest_over_file
from uploader.samples.models import samples_by_species_and_population

from scripts.rqtl2.entry import build_main
from scripts.rqtl2.cli_parser import add_bundle_argument
from scripts.cli_parser import init_cli_parser, add_global_data_arguments
from scripts.rqtl2.bundleutils import build_line_joiner, build_line_splitter

def validate(phenobundle: Path, logger: Logger) -> dict:
    """Check that the bundle is generally valid"""
    try:
        rqc.validate_bundle(phenobundle)
    except rqe.RQTLError as rqtlerr:
        # logger.error("Bundle file validation failed!", exc_info=True)
        return {
            "skip": True,
            "logger": logger,
            "phenobundle": phenobundle,
            "errors": (" ".join(rqtlerr.args),)
        }
    return {
        "errors": tuple(),
        "skip": False,
        "phenobundle": phenobundle,
        "logger": logger
    }


def check_for_mandatory_pheno_keys(
        phenobundle: Path,
        logger: Logger,
        **kwargs
) -> dict:
    """Check that the mandatory keys exist for phenotypes."""
    if kwargs.get("skip", False):
        return {
            **kwargs,
            "logger": logger,
            "phenobundle": phenobundle
        }

    _mandatory_keys = ("pheno", "phenocovar")
    _cdata = rqtl2.read_control_file(phenobundle)
    _errors = kwargs.get("errors", tuple()) + tuple(
        f"Expected '{key}' file(s) are not declared in the bundle."
        for key in _mandatory_keys if key not in _cdata.keys())
    return {
        **kwargs,
        "logger": logger,
        "phenobundle": phenobundle,
        "errors": _errors,
        "skip": len(_errors) > 0
    }


def check_for_averages_files(
        phenobundle: Path,
        logger: Logger,
        **kwargs
) -> dict:
    """Check that averages files appear together"""
    if kwargs.get("skip", False):
        return {
            **kwargs,
            "logger": logger,
            "phenobundle": phenobundle
        }

    _together = (("phenose", "phenonum"), ("phenonum", "phenose"))
    _cdata = rqtl2.read_control_file(phenobundle)
    _errors = kwargs.get("errors", tuple()) + tuple(
        f"'{first}' is defined in the control file but there is no "
        f"corresponding '{second}'"
        for first, second in _together
        if ((first in _cdata.keys()) and (second not in _cdata.keys())))
    return {
        **kwargs,
        "logger": logger,
        "phenobundle": phenobundle,
        "errors": _errors,
        "skip": len(_errors) > 0
    }


def extract_bundle(bundle: Path, workdir: Path) -> tuple[Path, tuple[Path, ...]]:
    """Extract the bundle."""
    with ZipFile(bundle) as zfile:
        extractiondir = workdir.joinpath(
            f"{sha256_digest_over_file(bundle)}_phenotype_qc_{bundle.name}")
        return extractiondir, rqtl2.extract(zfile, extractiondir)


def undo_transpose(filetype: str, cdata: dict, extractiondir):
    """Undo transposition of all files of type `filetype` in thebundle."""
    if len(cdata.get(filetype, [])) > 0 and cdata.get(f"{filetype}_transposed", False):
        files = (extractiondir.joinpath(_file) for _file in cdata[filetype])
        for _file in files:
            rqtl2.transpose_csv_with_rename(
                _file,
                build_line_splitter(cdata),
                build_line_joiner(cdata))


def qc_phenocovar_file(phenocovarfile: Path, separator: str, comment_char: str):
    """Check that `phenocovar` files are structured correctly."""
    _csvfile = rqtl2.read_csv_file(phenocovarfile, separator, comment_char)
    _headings = tuple(heading.lower() for heading in next(_csvfile))
    _errors = tuple()
    for heading in ("description", "units"):
        if heading not in _headings:
            _errors = (InvalidValue(
                phenocovarfile.name,
                "header row",
                "-",
                "-",
                (f"File {phenocovarfile.name} is missing the {heading} heading "
                 "in the header line.")),)

    def collect_errors(errors_and_linecount, line):
        _errs, _lc = errors_and_linecount
        if len(line) != len(_headings):
            _errs = _errs + (InvalidValue(
                phenocovarfile.name,
                line[0],
                "-",
                "-",
                (f"Record {_lc} in file {phenocovarfile.name} has a different "
                    "number of columns than the number of headings")),)
        _line = dict(zip(_headings, line))
        if not bool(_line["description"]):
            _errs = _errs + (
                InvalidValue(phenocovarfile.name,
                             _line[_headings[0]],
                             "description",
                             _line["description"],
                             "The description is not provided!"),)

        return _errs, _lc+1

    return {
        phenocovarfile.name: dict(zip(
            ("errors", "linecount"),
            reduce(collect_errors, _csvfile, (_errors, 1))))
    }


def merge_dicts(*dicts):
    """Merge multiple dicts into a single one."""
    return reduce(lambda merged, dct: {**merged, **dct}, dicts, {})


def decimal_points_error(# pylint: disable=[too-many-arguments]
        filename: str,
        rowtitle: str,
        coltitle: str,
        cellvalue: str,
        message: str,
        decimal_places: int = 1
) -> Optional[InvalidValue]:
    """Returns an error if the value does not meet the checks."""
    if not bool(decimal_places_pattern(decimal_places).match(cellvalue)):
        return InvalidValue(filename, rowtitle, coltitle, cellvalue, message)
    return None


def integer_error(
        filename: str,
        rowtitle: str,
        coltitle: str,
        cellvalue: str,
        message: str
) -> Optional[InvalidValue]:
    """Returns an error if the value does not meet the checks."""
    try:
        value = int(cellvalue)
        if value <= 0:
            raise ValueError("Must be a non-zero, positive number.")
        return None
    except ValueError as _verr:
        return InvalidValue(filename, rowtitle, coltitle, cellvalue, message)


def qc_pheno_file(# pylint: disable=[too-many-arguments]
        filepath: Path,
        samples: tuple[str, ...],
        phenonames: tuple[str, ...],
        separator: str,
        comment_char: str,
        na_strings: Sequence[str],
        error_fn: Callable = decimal_points_error
):
    """Run QC/QA on a `pheno` file."""
    _csvfile = rqtl2.read_csv_file(filepath, separator, comment_char)
    _headings = tuple(heading.lower() for heading in next(_csvfile))
    _errors = tuple()

    _absent = tuple(pheno for pheno in _headings[1:] if pheno not in phenonames)
    if len(_absent) > 0:
        _errors = _errors + (InvalidValue(
            filepath.name,
            "header row",
            "-",
            ", ".join(_absent),
            (f"The phenotype names ({', '.join(samples)}) do not exist in any "
             "of the provided phenocovar files.")),)

    def collect_errors(errors_and_linecount, line):
        _errs, _lc = errors_and_linecount
        if line[0] not in samples:
            _errs = _errs + (InvalidValue(
            filepath.name,
            line[0],
            _headings[0],
            line[0],
            (f"The sample named '{line[0]}' does not exist in the database. "
             "You will need to upload that first.")),)

        for field, value in zip(_headings[1:], line[1:]):
            if value in na_strings:
                continue
            _err = error_fn(
                filepath.name,
                line[0],
                field,
                value)
            _errs = _errs + ((_err,) if bool(_err) else tuple())

        return _errs, _lc+1

    return {
        filepath.name: dict(zip(
            ("errors", "linecount"),
            reduce(collect_errors, _csvfile, (_errors, 1))))
    }


def phenotype_names(filepath: Path,
                    separator: str,
                    comment_char: str) -> tuple[str, ...]:
    """Read phenotype names from `phenocovar` file."""
    return reduce(lambda tpl, line: tpl + (line[0],),
                  rqtl2.read_csv_file(filepath, separator, comment_char),
                  tuple())[1:]


def run_qc(# pylint: disable=[too-many-arguments]
        dbconn: mdb.Connection,
        phenobundle: Path,
        workingdir: Path,
        speciesid: int,
        populationid: int,
        logger: Logger
) -> int:
    """Run quality control checks on the bundle."""
    results = check_for_averages_files(
        **check_for_mandatory_pheno_keys(
            **validate(phenobundle, logger)))
    errors = results.get("errors", tuple())
    if len(errors) > 0:
        logger.error("We found the following errors:\n%s",
                     "\n".join(f" - {error}" for error in errors))
        return 1
    # Run QC on actual values
    #       Steps:
    #       - Extract file to specific directory
    extractiondir, *_bundlefiles = extract_bundle(phenobundle, workingdir)

    #       - For every pheno, phenocovar, phenose, phenonum file, undo
    #         transposition where relevant
    cdata = rqtl2.control_data(extractiondir)
    with mproc.Pool(mproc.cpu_count() - 1) as pool:
        pool.starmap(
            undo_transpose,
            ((ftype, cdata, extractiondir)
             for ftype in ("pheno", "phenocovar", "phenose", "phenonum")))

    #       - Fetch samples/individuals from database.
    samples = tuple(
        item for item in set(reduce(
            lambda acc, item: acc + (
                item["Name"], item["Name2"], item["Symbol"], item["Alias"]),
            samples_by_species_and_population(dbconn, speciesid, populationid),
            tuple()))
        if bool(item))

    #       - Check that `description` and `units` is present in phenocovar for
    #         all phenotypes
    with mproc.Pool(mproc.cpu_count() - 1) as pool:
        phenocovar_qc_res = merge_dicts(*pool.starmap(qc_phenocovar_file, tuple(
            (extractiondir.joinpath(_file), cdata["sep"], cdata["comment.char"])
            for _file in cdata.get("phenocovar", []))))

        #       - Check all samples in pheno files exist in database
        #       - Check all phenotypes in pheno files exist in phenocovar files
        #       - Check all numeric values in pheno files
        phenonames = tuple(set(
            name for names in pool.starmap(phenotype_names, tuple(
            (extractiondir.joinpath(_file), cdata["sep"], cdata["comment.char"])
            for _file in cdata.get("phenocovar", [])))
            for name in names))

        dec_err_fn = partial(decimal_points_error, message=(
            "Expected a non-negative number with at least one decimal "
            "place."))
        pheno_qc_res = merge_dicts(*pool.starmap(qc_pheno_file, tuple((
            extractiondir.joinpath(_file),
            samples,
            phenonames,
            cdata["sep"],
            cdata["comment.char"],
            cdata["na.strings"],
            dec_err_fn
        ) for _file in cdata.get("pheno", []))))

        #       - Check the 3 checks above for phenose and phenonum values too
        # qc_phenose_files(…)
        # qc_phenonum_files(…)
        phenose_qc_res = merge_dicts(*pool.starmap(qc_pheno_file, tuple((
            extractiondir.joinpath(_file),
            samples,
            phenonames,
            cdata["sep"],
            cdata["comment.char"],
            cdata["na.strings"],
            dec_err_fn
        ) for _file in cdata.get("phenose", []))))

        phenonum_qc_res = merge_dicts(*pool.starmap(qc_pheno_file, tuple((
            extractiondir.joinpath(_file),
            samples,
            phenonames,
            cdata["sep"],
            cdata["comment.char"],
            cdata["na.strings"],
            partial(integer_error, message=(
                "Expected a non-negative, non-zero integer value."))
        ) for _file in cdata.get("phenonum", []))))

    #       - Delete all extracted files
    shutil.rmtree(extractiondir)
    raise NotImplementedError("WIP!")


if __name__ == "__main__":

    def cli_args():
        """Process command-line arguments for `install_phenos`"""
        parser = add_bundle_argument(add_global_data_arguments(init_cli_parser(
            program="PhenotypesQC",
            description=(
                "Perform Quality Control checks on a phenotypes bundle file"))))
        parser.add_argument(
            "--working-dir",
            default=f"{tempfile.gettempdir()}/phenotypes_qc",
            help=("The directory where this script will put its intermediate "
                  "files."),
            type=Path)
        return parser.parse_args()

    main = build_main(cli_args(), run_qc, __MODULE__)
    sys.exit(main())