"""
This module implements the Sequential Least Squares Programming optimization
algorithm (SLSQP), originally developed by Dieter Kraft.
See http://www.netlib.org/toms/733

Functions
---------
.. autosummary::
   :toctree: generated/

    approx_jacobian
    fmin_slsqp

"""

__all__ = ['approx_jacobian', 'fmin_slsqp']

import numpy as np
from scipy.optimize._slsqp import slsqp
from numpy import (zeros, array, linalg, append, concatenate, finfo,
                   sqrt, vstack, isfinite, atleast_1d)
from ._optimize import (OptimizeResult, _check_unknown_options,
                        _prepare_scalar_function, _clip_x_for_func,
                        _check_clip_x)
from ._numdiff import approx_derivative
from ._constraints import old_bound_to_new, _arr_to_scalar
from scipy._lib._array_api import array_namespace
from scipy._lib import array_api_extra as xpx


__docformat__ = "restructuredtext en"

_epsilon = sqrt(finfo(float).eps)


def approx_jacobian(x, func, epsilon, *args):
    """
    Approximate the Jacobian matrix of a callable function.

    Parameters
    ----------
    x : array_like
        The state vector at which to compute the Jacobian matrix.
    func : callable f(x,*args)
        The vector-valued function.
    epsilon : float
        The perturbation used to determine the partial derivatives.
    args : sequence
        Additional arguments passed to func.

    Returns
    -------
    An array of dimensions ``(lenf, lenx)`` where ``lenf`` is the length
    of the outputs of `func`, and ``lenx`` is the number of elements in
    `x`.

    Notes
    -----
    The approximation is done using forward differences.

    """
    # approx_derivative returns (m, n) == (lenf, lenx)
    jac = approx_derivative(func, x, method='2-point', abs_step=epsilon,
                            args=args)
    # if func returns a scalar jac.shape will be (lenx,). Make sure
    # it's at least a 2D array.
    return np.atleast_2d(jac)


def fmin_slsqp(func, x0, eqcons=(), f_eqcons=None, ieqcons=(), f_ieqcons=None,
               bounds=(), fprime=None, fprime_eqcons=None,
               fprime_ieqcons=None, args=(), iter=100, acc=1.0E-6,
               iprint=1, disp=None, full_output=0, epsilon=_epsilon,
               callback=None):
    """
    Minimize a function using Sequential Least Squares Programming

    Python interface function for the SLSQP Optimization subroutine
    originally implemented by Dieter Kraft.

    Parameters
    ----------
    func : callable f(x,*args)
        Objective function.  Must return a scalar.
    x0 : 1-D ndarray of float
        Initial guess for the independent variable(s).
    eqcons : list, optional
        A list of functions of length n such that
        eqcons[j](x,*args) == 0.0 in a successfully optimized
        problem.
    f_eqcons : callable f(x,*args), optional
        Returns a 1-D array in which each element must equal 0.0 in a
        successfully optimized problem. If f_eqcons is specified,
        eqcons is ignored.
    ieqcons : list, optional
        A list of functions of length n such that
        ieqcons[j](x,*args) >= 0.0 in a successfully optimized
        problem.
    f_ieqcons : callable f(x,*args), optional
        Returns a 1-D ndarray in which each element must be greater or
        equal to 0.0 in a successfully optimized problem. If
        f_ieqcons is specified, ieqcons is ignored.
    bounds : list, optional
        A list of tuples specifying the lower and upper bound
        for each independent variable [(xl0, xu0),(xl1, xu1),...]
        Infinite values will be interpreted as large floating values.
    fprime : callable ``f(x,*args)``, optional
        A function that evaluates the partial derivatives of func.
    fprime_eqcons : callable ``f(x,*args)``, optional
        A function of the form ``f(x, *args)`` that returns the m by n
        array of equality constraint normals. If not provided,
        the normals will be approximated. The array returned by
        fprime_eqcons should be sized as ( len(eqcons), len(x0) ).
    fprime_ieqcons : callable ``f(x,*args)``, optional
        A function of the form ``f(x, *args)`` that returns the m by n
        array of inequality constraint normals. If not provided,
        the normals will be approximated. The array returned by
        fprime_ieqcons should be sized as ( len(ieqcons), len(x0) ).
    args : sequence, optional
        Additional arguments passed to func and fprime.
    iter : int, optional
        The maximum number of iterations.
    acc : float, optional
        Requested accuracy.
    iprint : int, optional
        The verbosity of fmin_slsqp :

        * iprint <= 0 : Silent operation
        * iprint == 1 : Print summary upon completion (default)
        * iprint >= 2 : Print status of each iterate and summary
    disp : int, optional
        Overrides the iprint interface (preferred).
    full_output : bool, optional
        If False, return only the minimizer of func (default).
        Otherwise, output final objective function and summary
        information.
    epsilon : float, optional
        The step size for finite-difference derivative estimates.
    callback : callable, optional
        Called after each iteration, as ``callback(x)``, where ``x`` is the
        current parameter vector.

    Returns
    -------
    out : ndarray of float
        The final minimizer of func.
    fx : ndarray of float, if full_output is true
        The final value of the objective function.
    its : int, if full_output is true
        The number of iterations.
    imode : int, if full_output is true
        The exit mode from the optimizer (see below).
    smode : string, if full_output is true
        Message describing the exit mode from the optimizer.

    See also
    --------
    minimize: Interface to minimization algorithms for multivariate
        functions. See the 'SLSQP' `method` in particular.

    Notes
    -----
    Exit modes are defined as follows:

    - ``-1`` : Gradient evaluation required (g & a)
    - ``0`` : Optimization terminated successfully
    - ``1`` : Function evaluation required (f & c)
    - ``2`` : More equality constraints than independent variables
    - ``3`` : More than 3*n iterations in LSQ subproblem
    - ``4`` : Inequality constraints incompatible
    - ``5`` : Singular matrix E in LSQ subproblem
    - ``6`` : Singular matrix C in LSQ subproblem
    - ``7`` : Rank-deficient equality constraint subproblem HFTI
    - ``8`` : Positive directional derivative for linesearch
    - ``9`` : Iteration limit reached

    Examples
    --------
    Examples are given :ref:`in the tutorial <tutorial-sqlsp>`.

    """
    if disp is not None:
        iprint = disp

    opts = {'maxiter': iter,
            'ftol': acc,
            'iprint': iprint,
            'disp': iprint != 0,
            'eps': epsilon,
            'callback': callback}

    # Build the constraints as a tuple of dictionaries
    cons = ()
    # 1. constraints of the 1st kind (eqcons, ieqcons); no Jacobian; take
    #    the same extra arguments as the objective function.
    cons += tuple({'type': 'eq', 'fun': c, 'args': args} for c in eqcons)
    cons += tuple({'type': 'ineq', 'fun': c, 'args': args} for c in ieqcons)
    # 2. constraints of the 2nd kind (f_eqcons, f_ieqcons) and their Jacobian
    #    (fprime_eqcons, fprime_ieqcons); also take the same extra arguments
    #    as the objective function.
    if f_eqcons:
        cons += ({'type': 'eq', 'fun': f_eqcons, 'jac': fprime_eqcons,
                  'args': args}, )
    if f_ieqcons:
        cons += ({'type': 'ineq', 'fun': f_ieqcons, 'jac': fprime_ieqcons,
                  'args': args}, )

    res = _minimize_slsqp(func, x0, args, jac=fprime, bounds=bounds,
                          constraints=cons, **opts)
    if full_output:
        return res['x'], res['fun'], res['nit'], res['status'], res['message']
    else:
        return res['x']


def _minimize_slsqp(func, x0, args=(), jac=None, bounds=None,
                    constraints=(),
                    maxiter=100, ftol=1.0E-6, iprint=1, disp=False,
                    eps=_epsilon, callback=None, finite_diff_rel_step=None,
                    **unknown_options):
    """
    Minimize a scalar function of one or more variables using Sequential
    Least Squares Programming (SLSQP).

    Options
    -------
    ftol : float
        Precision goal for the value of f in the stopping criterion.
    eps : float
        Step size used for numerical approximation of the Jacobian.
    disp : bool
        Set to True to print convergence messages. If False,
        `verbosity` is ignored and set to 0.
    maxiter : int
        Maximum number of iterations.
    finite_diff_rel_step : None or array_like, optional
        If ``jac in ['2-point', '3-point', 'cs']`` the relative step size to
        use for numerical approximation of `jac`. The absolute step
        size is computed as ``h = rel_step * sign(x) * max(1, abs(x))``,
        possibly adjusted to fit into the bounds. For ``method='3-point'``
        the sign of `h` is ignored. If None (default) then step is selected
        automatically.
    """
    _check_unknown_options(unknown_options)
    iter = maxiter - 1
    acc = ftol
    epsilon = eps

    if not disp:
        iprint = 0

    # Transform x0 into an array.
    xp = array_namespace(x0)
    x0 = xpx.atleast_nd(xp.asarray(x0), ndim=1, xp=xp)
    dtype = xp.float64
    if xp.isdtype(x0.dtype, "real floating"):
        dtype = x0.dtype
    x = xp.reshape(xp.astype(x0, dtype), -1)

    # SLSQP is sent 'old-style' bounds, 'new-style' bounds are required by
    # ScalarFunction
    if bounds is None or len(bounds) == 0:
        new_bounds = (-np.inf, np.inf)
    else:
        new_bounds = old_bound_to_new(bounds)

    # clip the initial guess to bounds, otherwise ScalarFunction doesn't work
    x = np.clip(x, new_bounds[0], new_bounds[1])

    # Constraints are triaged per type into a dictionary of tuples
    if isinstance(constraints, dict):
        constraints = (constraints, )

    cons = {'eq': (), 'ineq': ()}
    for ic, con in enumerate(constraints):
        # check type
        try:
            ctype = con['type'].lower()
        except KeyError as e:
            raise KeyError('Constraint %d has no type defined.' % ic) from e
        except TypeError as e:
            raise TypeError('Constraints must be defined using a '
                            'dictionary.') from e
        except AttributeError as e:
            raise TypeError("Constraint's type must be a string.") from e
        else:
            if ctype not in ['eq', 'ineq']:
                raise ValueError(f"Unknown constraint type '{con['type']}'.")

        # check function
        if 'fun' not in con:
            raise ValueError('Constraint %d has no function defined.' % ic)

        # check Jacobian
        cjac = con.get('jac')
        if cjac is None:
            # approximate Jacobian function. The factory function is needed
            # to keep a reference to `fun`, see gh-4240.
            def cjac_factory(fun):
                def cjac(x, *args):
                    x = _check_clip_x(x, new_bounds)

                    if jac in ['2-point', '3-point', 'cs']:
                        return approx_derivative(fun, x, method=jac, args=args,
                                                 rel_step=finite_diff_rel_step,
                                                 bounds=new_bounds)
                    else:
                        return approx_derivative(fun, x, method='2-point',
                                                 abs_step=epsilon, args=args,
                                                 bounds=new_bounds)

                return cjac
            cjac = cjac_factory(con['fun'])

        # update constraints' dictionary
        cons[ctype] += ({'fun': con['fun'],
                         'jac': cjac,
                         'args': con.get('args', ())}, )

    exit_modes = {-1: "Gradient evaluation required (g & a)",
                   0: "Optimization terminated successfully",
                   1: "Function evaluation required (f & c)",
                   2: "More equality constraints than independent variables",
                   3: "More than 3*n iterations in LSQ subproblem",
                   4: "Inequality constraints incompatible",
                   5: "Singular matrix E in LSQ subproblem",
                   6: "Singular matrix C in LSQ subproblem",
                   7: "Rank-deficient equality constraint subproblem HFTI",
                   8: "Positive directional derivative for linesearch",
                   9: "Iteration limit reached"}

    # Set the parameters that SLSQP will need
    # meq, mieq: number of equality and inequality constraints
    meq = sum(map(len, [atleast_1d(c['fun'](x, *c['args']))
              for c in cons['eq']]))
    mieq = sum(map(len, [atleast_1d(c['fun'](x, *c['args']))
               for c in cons['ineq']]))
    # m = The total number of constraints
    m = meq + mieq
    # la = The number of constraints, or 1 if there are no constraints
    la = array([1, m]).max()
    # n = The number of independent variables
    n = len(x)

    # Define the workspaces for SLSQP
    n1 = n + 1
    mineq = m - meq + n1 + n1
    len_w = (3*n1+m)*(n1+1)+(n1-meq+1)*(mineq+2) + 2*mineq+(n1+mineq)*(n1-meq) \
            + 2*meq + n1 + ((n+1)*n)//2 + 2*m + 3*n + 3*n1 + 1
    len_jw = mineq
    w = zeros(len_w)
    jw = zeros(len_jw)

    # Decompose bounds into xl and xu
    if bounds is None or len(bounds) == 0:
        xl = np.empty(n, dtype=float)
        xu = np.empty(n, dtype=float)
        xl.fill(np.nan)
        xu.fill(np.nan)
    else:
        bnds = array([(_arr_to_scalar(l), _arr_to_scalar(u))
                      for (l, u) in bounds], float)
        if bnds.shape[0] != n:
            raise IndexError('SLSQP Error: the length of bounds is not '
                             'compatible with that of x0.')

        with np.errstate(invalid='ignore'):
            bnderr = bnds[:, 0] > bnds[:, 1]

        if bnderr.any():
            raise ValueError("SLSQP Error: lb > ub in bounds "
                             f"{', '.join(str(b) for b in bnderr)}.")
        xl, xu = bnds[:, 0], bnds[:, 1]

        # Mark infinite bounds with nans; the Fortran code understands this
        infbnd = ~isfinite(bnds)
        xl[infbnd[:, 0]] = np.nan
        xu[infbnd[:, 1]] = np.nan

    # ScalarFunction provides function and gradient evaluation
    sf = _prepare_scalar_function(func, x, jac=jac, args=args, epsilon=eps,
                                  finite_diff_rel_step=finite_diff_rel_step,
                                  bounds=new_bounds)
    # gh11403 SLSQP sometimes exceeds bounds by 1 or 2 ULP, make sure this
    # doesn't get sent to the func/grad evaluator.
    wrapped_fun = _clip_x_for_func(sf.fun, new_bounds)
    wrapped_grad = _clip_x_for_func(sf.grad, new_bounds)

    # Initialize the iteration counter and the mode value
    mode = array(0, int)
    acc = array(acc, float)
    majiter = array(iter, int)
    majiter_prev = 0

    # Initialize internal SLSQP state variables
    alpha = array(0, float)
    f0 = array(0, float)
    gs = array(0, float)
    h1 = array(0, float)
    h2 = array(0, float)
    h3 = array(0, float)
    h4 = array(0, float)
    t = array(0, float)
    t0 = array(0, float)
    tol = array(0, float)
    iexact = array(0, int)
    incons = array(0, int)
    ireset = array(0, int)
    itermx = array(0, int)
    line = array(0, int)
    n1 = array(0, int)
    n2 = array(0, int)
    n3 = array(0, int)

    # Print the header if iprint >= 2
    if iprint >= 2:
        print("%5s %5s %16s %16s" % ("NIT", "FC", "OBJFUN", "GNORM"))

    # mode is zero on entry, so call objective, constraints and gradients
    # there should be no func evaluations here because it's cached from
    # ScalarFunction
    fx = wrapped_fun(x)
    g = append(wrapped_grad(x), 0.0)
    c = _eval_constraint(x, cons)
    a = _eval_con_normals(x, cons, la, n, m, meq, mieq)

    while 1:
        # Call SLSQP
        slsqp(m, meq, x, xl, xu, fx, c, g, a, acc, majiter, mode, w, jw,
              alpha, f0, gs, h1, h2, h3, h4, t, t0, tol,
              iexact, incons, ireset, itermx, line,
              n1, n2, n3)

        if mode == 1:  # objective and constraint evaluation required
            fx = wrapped_fun(x)
            c = _eval_constraint(x, cons)

        if mode == -1:  # gradient evaluation required
            g = append(wrapped_grad(x), 0.0)
            a = _eval_con_normals(x, cons, la, n, m, meq, mieq)

        if majiter > majiter_prev:
            # call callback if major iteration has incremented
            if callback is not None:
                callback(np.copy(x))

            # Print the status of the current iterate if iprint > 2
            if iprint >= 2:
                print("%5i %5i % 16.6E % 16.6E" % (majiter, sf.nfev,
                                                   fx, linalg.norm(g)))

        # If exit mode is not -1 or 1, slsqp has completed
        if abs(mode) != 1:
            break

        majiter_prev = int(majiter)

    # Optimization loop complete. Print status if requested
    if iprint >= 1:
        print(exit_modes[int(mode)] + "    (Exit mode " + str(mode) + ')')
        print("            Current function value:", fx)
        print("            Iterations:", majiter)
        print("            Function evaluations:", sf.nfev)
        print("            Gradient evaluations:", sf.ngev)

    return OptimizeResult(x=x, fun=fx, jac=g[:-1], nit=int(majiter),
                          nfev=sf.nfev, njev=sf.ngev, status=int(mode),
                          message=exit_modes[int(mode)], success=(mode == 0))


def _eval_constraint(x, cons):
    # Compute constraints
    if cons['eq']:
        c_eq = concatenate([atleast_1d(con['fun'](x, *con['args']))
                            for con in cons['eq']])
    else:
        c_eq = zeros(0)

    if cons['ineq']:
        c_ieq = concatenate([atleast_1d(con['fun'](x, *con['args']))
                             for con in cons['ineq']])
    else:
        c_ieq = zeros(0)

    # Now combine c_eq and c_ieq into a single matrix
    c = concatenate((c_eq, c_ieq))
    return c


def _eval_con_normals(x, cons, la, n, m, meq, mieq):
    # Compute the normals of the constraints
    if cons['eq']:
        a_eq = vstack([con['jac'](x, *con['args'])
                       for con in cons['eq']])
    else:  # no equality constraint
        a_eq = zeros((meq, n))

    if cons['ineq']:
        a_ieq = vstack([con['jac'](x, *con['args'])
                        for con in cons['ineq']])
    else:  # no inequality constraint
        a_ieq = zeros((mieq, n))

    # Now combine a_eq and a_ieq into a single a matrix
    if m == 0:  # no constraints
        a = zeros((la, n))
    else:
        a = vstack((a_eq, a_ieq))
    a = concatenate((a, zeros([la, 1])), 1)

    return a
