# Source code for eqc_models.base.quadratic

    # (C) Quantum Computing Inc., 2024.
    from typing import Tuple
    import warnings
    import numpy as np
    from eqc_models.base.base import EqcModel
    from eqc_models.base.constraints import ConstraintsMixIn
    from eqc_models.base.operators import QUBO, Polynomial

    class QuadraticMixIn:
        """
        This class provides an instance method and property that
        manage quadratic models.

        """

        @property
        def sparse(self) -> Tuple[np.ndarray, np.ndarray]:
            """
            Put the linear and quadratic terms in a sparse (poly) format

            Returns
            ----------

            coefficients: List
            indices: List

            """
            C, J = self.H
            C = np.squeeze(C)
            n = self.n
            indices = []
            coefficients = []
            # build a key (ordered tuple of indices) of length 2 for each element
            for i in range(n):
                if C[i] != 0:
                    key = (0, i+1)
                    indices.append(key)
                    coefficients.append(C[i])
            # make J upper triangular
            J = np.triu(J) + np.tril(J, -1).T
            for i in range(n):
                for j in range(i, n):
                    val = J[i, j]
                    if val != 0:
                        key = (i+1, j+1)
                        indices.append(key)
                        coefficients.append(val)

            return np.array(coefficients, dtype=np.float32), np.array(indices, dtype=np.int32)

        @property
        def polynomial(self) -> Polynomial:
            coefficients, indices = self.sparse
            return Polynomial(coefficients=coefficients, indices=indices)

        def evaluate(self, solution: np.ndarray) -> float:
            """
            Evaluate the solution using the original operator.

            """

            sol = np.array(solution)
            C, J = self.H
            return np.squeeze(sol.T @ J @ sol + C.T @ sol)

        @property
        def dynamic_range(self) -> float:
            """
            Dynamic range is a measure in decibels of the ratio of the largest
            magnitude coefficient in a problem to the smallest non-zero magnitude
            coefficient.

            The possible range of values are all greater than or equal to 0. The
            calculation is performed by finding the lowest non-zero of the
            absolute value of all the coefficients, which could be empty. The
            values are in two arrays, so the minimum and maximum values of these
            arrays are compared to each other. When there is no non-zero in either
            of the arrays, then an exception is raised indicating that the dynamic
            range of an operator of all zeros is undefined. If the lowest value is
            positive, then the maximum of the absolute values is divided by the
            lowest. The base-10 logarithm of that value is taken and multiplied by
            10. This is the dynamic range.

            Returns
            ----------

            float

            """
            C, J = self.H
            # if either C or J are all 0, then set min to very large value
            try:
                min_c = np.min(np.abs(C[C!=0]))
            except IndexError:
                min_c = 1e308
            try:
                min_j = np.min(np.abs(J[J!=0]))
            except IndexError:
                min_j = 1e308
            max_c = np.max(np.abs(C))
            max_j = np.max(np.abs(J))
            lowest = min_c if min_c < min_j else min_j
            highest = max_c if max_c > max_j else max_j
            if lowest > highest:
                raise ValueError("Dynamic range of a Hamiltonian of all 0 is undefined")
            return 10*np.log10(highest / lowest)

        @property
        def qubo(self) -> QUBO:
            """
            Transform the model into QUBO form. Use `upper_bound` to determine
            a log-encoding of the variables.

            """
            bin_n = 0
            bits = []
            #
            C, J = self.H
            # upper_bound is an array of the maximum values each variable can take
            upper_bound = self.upper_bound
            if np.sum(upper_bound)!=upper_bound.shape[0]:
                for i in range(upper_bound.shape[0]):
                    bits.append(1+np.floor(np.log2(upper_bound[i])))
                    bin_n += bits[-1]
                bin_n = int(bin_n)
                Q = np.zeros((bin_n, bin_n), dtype=np.float32)
                Q.shape
                powers = [2**np.arange(bit_count) for bit_count in bits]
                blocks = []
                linear_blocks = []
                for i in range(len(powers)):
                    # add the linear terms to the diagonal
                    linear_blocks.append(C[i]*powers[i])
                    row = []
                    for j in range(len(powers)):
                        mult = J[i,j]
                        block = np.outer(powers[i], powers[j])
                        block *= mult
                        row.append(block)
                    blocks.append(row)
                Q[:, :] = np.block(blocks)
                linear_operator = np.hstack(linear_blocks)
                Q += np.diag(linear_operator)
            else:
                # in this case, the fomulation already has only binary variables
                Q = np.zeros_like(J)
                Q[:, :] = J
                Q += np.diag(np.squeeze(C))

            return QUBO(Q)

    class QuadraticModel(QuadraticMixIn, EqcModel):
        """
        Provides a quadratic operator and device sum constraint support.

        Parameters
        -----------

        J: Quadratic hamiltonian array.
        C: Linear hamiltonian array.

        Examples
        ---------

        >>> C = np.array([1, 2])
        >>> J = np.array([[2, 1], [1, 2]])
        >>> from eqc_models.base.quadratic import QuadraticModel
        >>> model = QuadraticModel(C, J)
        >>> model.upper_bound = np.array([1, 1])
        >>> qubo = model.qubo
        >>> qubo.Q # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
        array([[3., 1.],
               [1., 4.]])

        """

        def __init__(self, C: np.ndarray, J: np.ndarray):
            self._H = C, J

    class ConstrainedQuadraticModel(ConstraintsMixIn, QuadraticModel):
        """
        Provides a constrained quadratic operator and device sum constraint support.

        Parameters
        -----------

        J: Quadratic hamiltonian array.
        C: Linear hamiltonian array.
        lhs: Left hand side of the linear constraints.
        rhs: Right hand side of the linear constraints.

        Examples
        -------------------

        >>> C = np.array([1, 2])
        >>> J = np.array([[2, 1], [1, 2]])
        >>> lhs = np.array([[1, 1], [1, 1]])
        >>> rhs = np.array([1, 1])
        >>> from eqc_models.base.quadratic import ConstrainedQuadraticModel
        >>> model = ConstrainedQuadraticModel(C, J, lhs, rhs)
        >>> model.penalty_multiplier = 1
        >>> model.upper_bound = np.array([1, 1])
        >>> qubo = model.qubo
        >>> qubo.Q # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
        array([[1., 3.],
               [3., 2.]])

        """

        def __init__(self, C_obj: np.array, J_obj: np.array, lhs: np.array, rhs: np.array):
            self.quad_objective = J_obj
            self.linear_objective = C_obj
            self.lhs = lhs
            self.rhs = rhs
            self._penalty_multiplier = None

        @property
        def H(self) -> Tuple[np.ndarray, np.ndarray]:
            """
            Return a pair of arrays, the linear and quadratic portions of a quadratic
            operator that has the objective plus penalty functions multiplied by the
            penalty multiplier. The linear terms are the first array and the quadratic
            are the second.

            Returns
            -----------

            `np.ndarray, np.nedarray`

            """

            C = self.linear_objective
            J = self.quad_objective
            pC, pJ = self.penalties
            alpha = self.penalty_multiplier
            return C + alpha * pC, J + alpha * pJ

        @ConstraintsMixIn.penalty_multiplier.setter
        def penalty_multiplier(self, value):
            ConstraintsMixIn.penalty_multiplier.fset(self, value)

        @property
        def constraints(self):
            return self.lhs, self.rhs

        def evaluateObjective(self, solution: np.ndarray) -> float:
            J = self.quad_objective
            C = self.linear_objective
            return np.squeeze(C.T @ solution + solution.T@J@solution)