import numpy as np

from Orange import data


def _get_variable(variable, dat, attr_name, expected_type=None, expected_name=""):
    failed = False
    if isinstance(variable, data.Variable):
        datvar = getattr(dat, "variable", None)
        if datvar is not None and datvar is not variable:
            raise ValueError("variable does not match the variable in the data")
    elif hasattr(dat, "domain"):
        variable = dat.domain[variable]
    elif hasattr(dat, attr_name):
        variable = dat.variable
    else:
        failed = True
    if failed or (
        expected_type is not None and not isinstance(variable, expected_type)
    ):
        if not expected_type or isinstance(variable, data.Variable):
            raise ValueError("expected %s variable not %s" % (expected_name, variable))
        else:
            raise ValueError(
                "expected %s, not '%s'"
                % (expected_type.__name__, type(variable).__name__)
            )
    return variable


def _create_discrete(cls, *args):
    return cls(*args)


class Discrete(np.ndarray):
    def __new__(
        cls,
        dat=None,
        col_variable=None,
        row_variable=None,
        unknowns=None,
        unknown_rows=None,
    ):
        if isinstance(dat, data.Storage):
            if unknowns is not None:
                raise TypeError("incompatible arguments (data storage and 'unknowns'")
            return cls.from_data(dat, col_variable, row_variable)

        if row_variable is not None:
            row_variable = _get_variable(row_variable, dat, "row_variable")
            rows = len(row_variable.values)
        else:
            rows = dat.shape[0]
        if col_variable is not None:
            col_variable = _get_variable(col_variable, dat, "col_variable")
            cols = len(col_variable.values)
        else:
            cols = dat.shape[1]

        self = super().__new__(cls, (rows, cols))
        self.row_variable = row_variable
        self.col_variable = col_variable
        if dat is None:
            self[:] = 0
            self.unknowns = unknowns or 0
            self.unknown_rows = unknown_rows or 0
        else:
            self[...] = dat
            self.unknowns = (
                unknowns if unknowns is not None else getattr(dat, "unknowns", 0)
            )
            self.unknown_rows = unknown_rows if unknown_rows is not None else 0
        return self

    @classmethod
    def from_data(cls, data, col_variable, row_variable=None):
        if row_variable is None:
            row_variable = data.domain.class_var
            if row_variable is None:
                raise ValueError(
                    "row_variable needs to be specified (data has no class)"
                )
        row_variable = _get_variable(row_variable, data, "row_variable")
        col_variable = _get_variable(col_variable, data, "col_variable")
        try:
            conts, unknown_rows = data._compute_contingency(
                [col_variable], row_variable
            )
            dist, unknowns = conts[0]

            self = super().__new__(cls, dist.shape)
            self[...] = dist
            self.unknowns = unknowns
            self.unknown_rows = unknown_rows
        except NotImplementedError:
            shape = len(row_variable.values), len(col_variable.values)
            self = super().__new__(cls, shape)
            self[...] = np.zeros(shape)
            self.unknowns = 0
            self.unknown_rows = 0
            rind = data.domain.index(row_variable)
            cind = data.domain.index(col_variable)
            for row in data:
                rval, cval = row[rind], row[cind]
                w = row.weight
                if np.isnan(rval):
                    self.unknown_rows += w
                    continue
                if np.isnan(cval):
                    self.unknowns[cval] += w
                else:
                    self[int(rval), int(cval)] += w
        self.row_variable = row_variable
        self.col_variable = col_variable
        return self

    def __eq__(self, other):
        return np.array_equal(self, other) and (
            not hasattr(other, "unknowns")
            or np.array_equal(self.unknowns, other.unknowns)
        )

    def __getitem__(self, index):
        if isinstance(index, str):
            if len(self.shape) == 2:  # contingency
                index = self.row_variable.to_val(index)
                contingency_row = super().__getitem__(index)
                contingency_row.col_variable = self.col_variable
                return contingency_row
            else:  # Contingency row
                column = self.strides == self.base.strides[:1]
                if column:
                    index = self.row_variable.to_val(index)
                else:
                    index = self.col_variable.to_val(index)

        elif isinstance(index, tuple):
            if isinstance(index[0], str):
                index = (self.row_variable.to_val(index[0]), index[1])
            if isinstance(index[1], str):
                index = (index[0], self.col_variable.to_val(index[1]))
        result = super().__getitem__(index)
        if result.strides:
            result.col_variable = self.col_variable
            result.row_variable = self.row_variable
        return result

    def __setitem__(self, index, value):
        if isinstance(index, str):
            index = self.row_variable.to_val(index)
        elif isinstance(index, tuple):
            if isinstance(index[0], str):
                index = (self.row_variable.to_val(index[0]), index[1])
            if isinstance(index[1], str):
                index = (index[0], self.col_variable.to_val(index[1]))
        super().__setitem__(index, value)

    def normalize(self, axis=None):
        t = np.sum(self, axis=axis)
        if t > 1e-6:
            self[:] /= t
            if axis is None or axis == 1:
                self.unknowns /= t

    def __reduce__(self):
        return (
            _create_discrete,
            (
                Discrete,
                np.copy(self),
                self.col_variable,
                self.row_variable,
                self.unknowns,
            ),
        )


class Continuous:
    def __init__(
        self,
        dat=None,
        col_variable=None,
        row_variable=None,
        unknowns=None,
        unknown_rows=None,
    ):
        if isinstance(dat, data.Storage):
            if unknowns is not None:
                raise TypeError("incompatible arguments (data storage and 'unknowns'")
            return self.from_data(dat, col_variable, row_variable)

        if row_variable is not None:
            row_variable = _get_variable(row_variable, dat, "row_variable")
        if col_variable is not None:
            col_variable = _get_variable(col_variable, dat, "col_variable")

        self.values, self.counts = dat

        self.row_variable = row_variable
        self.col_variable = col_variable
        if unknowns is not None:
            self.unknowns = unknowns
        elif row_variable:
            self.unknowns = np.zeros(len(row_variable.values))
        else:
            self.unknowns = None
        if unknown_rows is not None:
            self.unknown_rows = unknown_rows
        elif row_variable:
            self.unknown_rows = 0
        else:
            self.unknown_rows = None

    def from_data(self, data, col_variable, row_variable=None):
        if row_variable is None:
            row_variable = data.domain.class_var
            if row_variable is None:
                raise ValueError(
                    "row_variable needs to be specified (data has no class)"
                )
        self.row_variable = _get_variable(row_variable, data, "row_variable")
        self.col_variable = _get_variable(col_variable, data, "col_variable")
        try:
            conts, self.unknown_rows = data._compute_contingency(
                [col_variable], row_variable
            )
            (self.values, self.counts), self.unknowns = conts[0]
        except NotImplementedError:
            raise NotImplementedError(
                "Fallback method for computation of contingencies is not implemented yet"
            )

    def __eq__(self, other):
        return (
            np.array_equal(self.values, other.values)
            and np.array_equal(self.counts, other.counts)
            and (
                not hasattr(other, "unknowns")
                or np.array_equal(self.unknowns, other.unknowns)
            )
        )

    def __getitem__(self, index):
        """ Return contingencies for a given class value. """
        if isinstance(index, (str, float)):
            index = self.row_variable.to_val(index)
        C = self.counts[index]
        ind = C > 0
        return np.vstack((self.values[ind], C[ind]))

    def __len__(self):
        return self.counts.shape[0]

    def __setitem__(self, index, value):
        raise NotImplementedError(
            "Setting individual class contingencies is not implemented yet. "
            "Set .values and .counts."
        )

    def normalize(self, axis=None):
        if axis is None:
            t = sum(np.sum(x[:, 1]) for x in self)
            if t > 1e-6:
                for x in self:
                    x[:, 1] /= t
        elif axis != 1:
            raise ValueError(
                "contingencies can be normalized only with axis=1 or without axis"
            )
        else:
            for i, x in enumerate(self):
                t = np.sum(x[:, 1])
                if t > 1e-6:
                    x[:, 1] /= t
                    self.unknowns[i] /= t
                else:
                    if self.unknowns[i] > 1e-6:
                        self.unknowns[i] = 1


def get_contingency(
    dat, col_variable, row_variable=None, unknowns=None, unknown_rows=None
):
    variable = _get_variable(col_variable, dat, "col_variable")
    if variable.is_discrete:
        return Discrete(dat, col_variable, row_variable, unknowns, unknown_rows)
    elif variable.is_continuous:
        return Continuous(dat, col_variable, row_variable, unknowns, unknown_rows)
    else:
        raise TypeError("cannot compute distribution of '%s'" % type(variable).__name__)


def get_contingencies(dat, skip_discrete=False, skip_continuous=False):
    vars = dat.domain.attributes
    row_var = dat.domain.class_var
    if row_var is None:
        raise ValueError("data has no target variable")
    if skip_discrete:
        if skip_continuous:
            return []
        columns = [i for i, var in enumerate(vars) if var.is_continuous]
    elif skip_continuous:
        columns = [i for i, var in enumerate(vars) if var.is_discrete]
    else:
        columns = None
    try:
        dist_unks, unknown_rows = dat._compute_contingency(columns)
        if columns is None:
            columns = np.arange(len(vars))
        contigs = []
        for col, (cont, unks) in zip(columns, dist_unks):
            contigs.append(
                get_contingency(cont, vars[col], row_var, unks, unknown_rows)
            )
    except NotImplementedError:
        if columns is None:
            columns = range(len(vars))
        contigs = [get_contingency(dat, i) for i in columns]
    return contigs

import numpy as np

from Orange import data


def _get_variable(variable, dat, attr_name, expected_type=None, expected_name=""):
    failed = False
    if isinstance(variable, data.Variable):
        datvar = getattr(dat, "variable", None)
        if datvar is not None and datvar is not variable:
            raise ValueError("variable does not match the variable in the data")
    elif hasattr(dat, "domain"):
        variable = dat.domain[variable]
    elif hasattr(dat, attr_name):
        variable = dat.variable
    else:
        failed = True
    if failed or (
        expected_type is not None and not isinstance(variable, expected_type)
    ):
        if not expected_type or isinstance(variable, data.Variable):
            raise ValueError("expected %s variable not %s" % (expected_name, variable))
        else:
            raise ValueError(
                "expected %s, not '%s'"
                % (expected_type.__name__, type(variable).__name__)
            )
    return variable


def _create_discrete(cls, *args):
    return cls(*args)


class Discrete(np.ndarray):
    def __new__(
        cls,
        dat=None,
        col_variable=None,
        row_variable=None,
        unknowns=None,
        unknown_rows=None,
    ):
        if isinstance(dat, data.Storage):
            if unknowns is not None:
                raise TypeError("incompatible arguments (data storage and 'unknowns'")
            return cls.from_data(dat, col_variable, row_variable)

        if row_variable is not None:
            row_variable = _get_variable(row_variable, dat, "row_variable")
            rows = len(row_variable.values)
        else:
            rows = dat.shape[0]
        if col_variable is not None:
            col_variable = _get_variable(col_variable, dat, "col_variable")
            cols = len(col_variable.values)
        else:
            cols = dat.shape[1]

        self = super().__new__(cls, (rows, cols))
        self.row_variable = row_variable
        self.col_variable = col_variable
        if dat is None:
            self[:] = 0
            self.unknowns = unknowns or 0
            self.unknown_rows = unknown_rows or 0
        else:
            self[...] = dat
            self.unknowns = (
                unknowns if unknowns is not None else getattr(dat, "unknowns", 0)
            )
            self.unknown_rows = unknown_rows if unknown_rows is not None else 0
        return self

    @classmethod
    def from_data(cls, data, col_variable, row_variable=None):
        if row_variable is None:
            row_variable = data.domain.class_var
            if row_variable is None:
                raise ValueError(
                    "row_variable needs to be specified (data has no class)"
                )
        row_variable = _get_variable(row_variable, data, "row_variable")
        col_variable = _get_variable(col_variable, data, "col_variable")
        try:
            conts, unknown_rows = data._compute_contingency(
                [col_variable], row_variable
            )
            dist, unknowns = conts[0]

            self = super().__new__(cls, dist.shape)
            self[...] = dist
            self.unknowns = unknowns
            self.unknown_rows = unknown_rows
        except NotImplementedError:
            shape = len(row_variable.values), len(col_variable.values)
            self = super().__new__(cls, shape)
            self[...] = np.zeros(shape)
            self.unknowns = 0
            self.unknown_rows = 0
            rind = data.domain.index(row_variable)
            cind = data.domain.index(col_variable)
            for row in data:
                rval, cval = row[rind], row[cind]
                w = row.weight
                if np.isnan(rval):
                    self.unknown_rows += w
                    continue
                if np.isnan(cval):
                    self.unknowns[cval] += w
                else:
                    self[int(rval), int(cval)] += w
        self.row_variable = row_variable
        self.col_variable = col_variable
        return self

    def __eq__(self, other):
        return np.array_equal(self, other) and (
            not hasattr(other, "unknowns")
            or np.array_equal(self.unknowns, other.unknowns)
        )

    def __getitem__(self, index):
        if isinstance(index, str):
            if len(self.shape) == 2:  # contingency
                index = self.row_variable.to_val(index)
                contingency_row = super().__getitem__(index)
                contingency_row.col_variable = self.col_variable
                return contingency_row
            else:  # Contingency row
                column = self.strides == self.base.strides[:1]
                if column:
                    index = self.row_variable.to_val(index)
                else:
                    index = self.col_variable.to_val(index)

        elif isinstance(index, tuple):
            if isinstance(index[0], str):
                index = (self.row_variable.to_val(index[0]), index[1])
            if isinstance(index[1], str):
                index = (index[0], self.col_variable.to_val(index[1]))
        result = super().__getitem__(index)
        if result.strides:
            result.col_variable = self.col_variable
            result.row_variable = self.row_variable
        return result

    def __setitem__(self, index, value):
        if isinstance(index, str):
            index = self.row_variable.to_val(index)
        elif isinstance(index, tuple):
            if isinstance(index[0], str):
                index = (self.row_variable.to_val(index[0]), index[1])
            if isinstance(index[1], str):
                index = (index[0], self.col_variable.to_val(index[1]))
        super().__setitem__(index, value)

    def normalize(self, axis=None):
        t = np.sum(self, axis=axis)
        if t > 1e-6:
            self[:] /= t
            if axis is None or axis == 1:
                self.unknowns /= t

    def __reduce__(self):
        return (
            _create_discrete,
            (
                Discrete,
                np.copy(self),
                self.col_variable,
                self.row_variable,
                self.unknowns,
            ),
        )


class Continuous:
    def __init__(
        self,
        dat=None,
        col_variable=None,
        row_variable=None,
        unknowns=None,
        unknown_rows=None,
    ):
        if isinstance(dat, data.Storage):
            if unknowns is not None:
                raise TypeError("incompatible arguments (data storage and 'unknowns'")
            return self.from_data(dat, col_variable, row_variable)

        if row_variable is not None:
            row_variable = _get_variable(row_variable, dat, "row_variable")
        if col_variable is not None:
            col_variable = _get_variable(col_variable, dat, "col_variable")

        self.values, self.counts = dat

        self.row_variable = row_variable
        self.col_variable = col_variable
        if unknowns is not None:
            self.unknowns = unknowns
        elif row_variable:
            self.unknowns = np.zeros(len(row_variable.values))
        else:
            self.unknowns = None
        if unknown_rows is not None:
            self.unknown_rows = unknown_rows
        elif row_variable:
            self.unknown_rows = 0
        else:
            self.unknown_rows = None

    def from_data(self, data, col_variable, row_variable=None):
        if row_variable is None:
            row_variable = data.domain.class_var
            if row_variable is None:
                raise ValueError(
                    "row_variable needs to be specified (data has no class)"
                )
        self.row_variable = _get_variable(row_variable, data, "row_variable")
        self.col_variable = _get_variable(col_variable, data, "col_variable")
        try:
            conts, self.unknown_rows = data._compute_contingency(
                [col_variable], row_variable
            )
            (self.values, self.counts), self.unknowns = conts[0]
        except NotImplementedError:
            raise NotImplementedError(
                "Fallback method for computation of contingencies is not implemented yet"
            )

    def __eq__(self, other):
        return (
            np.array_equal(self.values, other.values)
            and np.array_equal(self.counts, other.counts)
            and (
                not hasattr(other, "unknowns")
                or np.array_equal(self.unknowns, other.unknowns)
            )
        )

    def __getitem__(self, index):
        """ Return contingencies for a given class value. """
        if isinstance(index, (str, float)):
            index = self.row_variable.to_val(index)
        C = self.counts[index]
        ind = C > 0
        return np.vstack((self.values[ind], C[ind]))

    def __len__(self):
        return self.counts.shape[0]

    def __setitem__(self, index, value):
        raise NotImplementedError(
            "Setting individual class contingencies is not implemented yet. "
            "Set .values and .counts."
        )

    def normalize(self, axis=None):
        if axis is None:
            t = sum(np.sum(x[:, 1]) for x in self)
            if t > 1e-6:
                for x in self:
                    x[:, 1] /= t
        elif axis != 1:
            raise ValueError(
                "contingencies can be normalized only with axis=1 or without axis"
            )
        else:
            for i, x in enumerate(self):
                t = np.sum(x[:, 1])
                if t > 1e-6:
                    x[:, 1] /= t
                    self.unknowns[i] /= t
                else:
                    if self.unknowns[i] > 1e-6:
                        self.unknowns[i] = 1


def get_contingency(
    dat, col_variable, row_variable=None, unknowns=None, unknown_rows=None
):
    variable = _get_variable(col_variable, dat, "col_variable")
    if variable.is_discrete:
        return Discrete(dat, col_variable, row_variable, unknowns, unknown_rows)
    elif variable.is_continuous:
        return Continuous(dat, col_variable, row_variable, unknowns, unknown_rows)
    else:
        raise TypeError("cannot compute distribution of '%s'" % type(variable).__name__)


def get_contingencies(dat, skip_discrete=False, skip_continuous=False):
    vars = dat.domain.attributes
    row_var = dat.domain.class_var
    if row_var is None:
        raise ValueError("data has no target variable")
    if skip_discrete:
        if skip_continuous:
            return []
        columns = [i for i, var in enumerate(vars) if var.is_continuous]
    elif skip_continuous:
        columns = [i for i, var in enumerate(vars) if var.is_discrete]
    else:
        columns = None
    try:
        dist_unks, unknown_rows = dat._compute_contingency(columns)
        if columns is None:
            columns = np.arange(len(vars))
        contigs = []
        for col, (cont, unks) in zip(columns, dist_unks):
            contigs.append(
                get_contingency(cont, vars[col], row_var, unks, unknown_rows)
            )
    except NotImplementedError:
        if columns is None:
            columns = range(len(vars))
        contigs = [get_contingency(dat, i) for i in columns]
    return contigs

# contingency # Contingency row Return contingencies for a given class value.