#  ___________________________________________________________________________
#
#  Pyomo: Python Optimization Modeling Objects
#  Copyright (c) 2008-2022
#  National Technology and Engineering Solutions of Sandia, LLC
#  Under the terms of Contract DE-NA0003525 with National Technology and
#  Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
#  rights in this software.
#  This software is distributed under the 3-clause BSD License.
#  ___________________________________________________________________________

"""Transformation to propagate a zero value to terms of a sum."""

from pyomo.core.base.transformation import TransformationFactory
from pyomo.core.base.constraint import Constraint
from pyomo.core.expr.numvalue import value
from pyomo.core.plugins.transform.hierarchy import IsomorphicTransformation
from pyomo.repn.standard_repn import generate_standard_repn


@TransformationFactory.register('contrib.propagate_zero_sum',
          doc="Propagate fixed-to-zero for sums of only positive (or negative) vars.")
class ZeroSumPropagator(IsomorphicTransformation):
    """Propagates fixed-to-zero for sums of only positive (or negative) vars.

    If :math:`z` is fixed to zero and :math:`z = x_1 + x_2 + x_3` and
    :math:`x_1`, :math:`x_2`, :math:`x_3` are all non-negative or all
    non-positive, then :math:`x_1`, :math:`x_2`, and :math:`x_3` will be fixed
    to zero.

    """

    def _apply_to(self, instance):
        for constr in instance.component_data_objects(ctype=Constraint,
                                                      active=True,
                                                      descend_into=True):
            if not constr.body.polynomial_degree() == 1:
                continue  # constraint not linear. Skip.

            repn = generate_standard_repn(constr.body)
            if (constr.has_ub() and (
                (repn.constant is None and value(constr.upper) == 0) or
                repn.constant == value(constr.upper)
            )):
                # term1 + term2 + term3 + ... <= 0
                # all var terms need to be non-negative
                if all(
                    # variable has 0 coefficient
                    coef == 0 or
                    # variable is non-negative and has non-negative coefficient
                    (repn.linear_vars[i].has_lb() and
                     value(repn.linear_vars[i].lb) >= 0 and
                     coef >= 0) or
                    # variable is non-positive and has non-positive coefficient
                    (repn.linear_vars[i].has_ub() and
                     value(repn.linear_vars[i].ub) <= 0 and
                     coef <= 0) for i, coef in enumerate(repn.linear_coefs)):
                    for i, coef in enumerate(repn.linear_coefs):
                        if not coef == 0:
                            repn.linear_vars[i].fix(0)
                    continue
            if (constr.has_lb() and (
                (repn.constant is None and value(constr.lower) == 0) or
                repn.constant == value(constr.lower)
            )):
                # term1 + term2 + term3 + ... >= 0
                # all var terms need to be non-positive
                if all(
                    # variable has 0 coefficient
                    coef == 0 or
                    # variable is non-negative and has non-positive coefficient
                    (repn.linear_vars[i].has_lb() and
                     value(repn.linear_vars[i].lb) >= 0 and
                     coef <= 0) or
                    # variable is non-positive and has non-negative coefficient
                    (repn.linear_vars[i].has_ub() and
                     value(repn.linear_vars[i].ub) <= 0 and
                     coef >= 0) for i, coef in enumerate(repn.linear_coefs)):
                    for i, coef in enumerate(repn.linear_coefs):
                        if not coef == 0:
                            repn.linear_vars[i].fix(0)
