"""
The MIT License (MIT)

Copyright (c) 2013-2017 pgmpy

Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
the Software, and to permit persons to whom the Software is furnished to do so,
subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
"""

import copy
import numpy as np


class DiscreteFactor:

    def __init__(self, variables, cardinality, values=None, state_names=None):
        """
        Args
            variables: list,
                variables in the scope of the factor
            cardinality: list,
                cardinalities of each variable, where len(cardinality)=len(variables)
            values: list,
                row vector of values of variables with ordering such that right-most variables
                defined in `variables` cycle through their values the fastest
            state_names: dictionary,
                mapping variables to their states, of format {label_name: ['state1', 'state2']}
        """
        self.variables = list(variables)
        self.cardinality = list(cardinality)
        if values is None:
            self._values = None
        else:
            self._values = np.array(values).reshape(self.cardinality)
        self.state_names = state_names

    def __mul__(self, other):
        return self.product(other)

    def copy(self):
        """Return a copy of the factor"""
        return self.__class__(self.variables,
                              self.cardinality,
                              self._values,
                              copy.deepcopy(self.state_names))

    @property
    def values(self):
        return self._values

    def update_values(self, new_values):
        """We make this available because _values is allowed to be None on init"""
        self._values = np.array(new_values).reshape(self.cardinality)

    def get_value_for_state_vector(self, dict_of_states):
        """
        Return the value for a dictionary of variable states.

        Args
            dict_of_states: dictionary,
                of format {label_name1: 'state1', label_name2: 'True'}
        Returns
            probability, a float, the factor value for a specific combination of variable states
        """
        assert sorted(dict_of_states.keys()) == sorted(self.variables), \
            "The keys for the dictionary of states must match the variables in factor scope."
        state_coordinates = []
        for var in self.variables:
            var_state = dict_of_states[var]
            idx_in_var_axis = self.state_names[var].index(var_state)
            state_coordinates.append(idx_in_var_axis)
        return self.values[tuple(state_coordinates)]

    def add_new_variables_from_other_factor(self, other):
        """Add new variables from `other` factor to the factor."""
        extra_vars = set(other.variables) - set(self.variables)
        # if all of these variables already exist there is nothing to do
        if len(extra_vars) == 0:
            return
        # otherwise, extend the values array
        slice_ = [slice(None)] * len(self.variables)
        slice_.extend([np.newaxis] * len(extra_vars))
        self._values = self._values[slice_]
        self.variables.extend(extra_vars)

        new_card_var = other.get_cardinality(extra_vars)
        self.cardinality.extend([new_card_var[var] for var in extra_vars])

    def get_cardinality(self, variables):
        return {var: self.cardinality[self.variables.index(var)] for var in variables}

    def product(self, other):
        left = self.copy()

        if isinstance(other, (int, float)):
            return self.values * other
        else:
            assert isinstance(other, DiscreteFactor), \
                "__mul__ is only defined between subclasses of DiscreteFactor"
            right = other.copy()
            left.add_new_variables_from_other_factor(right)
            right.add_new_variables_from_other_factor(left)

        # reorder variables in right factor to match order in left
        source_axes = list(range(right.values.ndim))
        destination_axes = [right.variables.index(var) for var in left.variables]
        right.variables = [right.variables[idx] for idx in destination_axes]

        # rearrange values in right factor to correspond to the reordered variables
        right._values = np.moveaxis(right.values, source_axes, destination_axes)
        left._values = left.values * right.values
        return left

    def marginalize(self, vars):
        """
        Args
            vars: list,
                variables over which to marginalize the factor
        Returns
            DiscreteFactor, whose scope is set(self.variables) - set(vars)
        """
        phi = copy.deepcopy(self)

        var_indexes = []
        for var in vars:
            if var not in phi.variables:
                raise ValueError('{} not in scope'.format(var))
            else:
                var_indexes.append(self.variables.index(var))

        index_to_keep = sorted(set(range(len(self.variables))) - set(var_indexes))
        phi.variables = [self.variables[index] for index in index_to_keep]
        phi.cardinality = [self.cardinality[index] for index in index_to_keep]
        phi._values = np.sum(phi.values, axis=tuple(var_indexes))
        return phi