#  ___________________________________________________________________________
#
#  Pyomo: Python Optimization Modeling Objects
#  Copyright 2017 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.
#  ___________________________________________________________________________
"""
The cyipopt_solver module includes the python interface to the
Cythonized ipopt solver cyipopt (see more:
https://github.com/mechmotum/cyipopt.git). To use the solver,
you can create a derived implementation from the abstract base class
CyIpoptProblemInterface that provides the necessary methods.

Note: This module also includes a default implementation CyIpopt
that works with problems derived from AslNLP as long as those
classes return numpy ndarray objects for the vectors and coo_matrix
objects for the matrices (e.g., AmplNLP and PyomoNLP)
"""
import io
import sys
import logging
import os
import abc

from pyomo.common.dependencies import (
    attempt_import,
    numpy as np, numpy_available,
)
from pyomo.common.tee import redirect_fd, TeeStream

def _cyipopt_importer():
    import cyipopt
    # cyipopt before version 1.0.3 called the problem class "Problem"
    if not hasattr(cyipopt, 'Problem'):
        cyipopt.Problem = cyipopt.problem
    # cyipopt before version 1.0.3 put the __version__ flag in the ipopt
    # module (which was deprecated starting in 1.0.3)
    if not hasattr(cyipopt, '__version__'):
        import ipopt
        cyipopt.__version__ = ipopt.__version__
    # Beginning in 1.0.3, STATUS_MESSAGES is in a separate
    # ipopt_wrapper module
    if not hasattr(cyipopt, 'STATUS_MESSAGES'):
        import ipopt_wrapper
        cyipopt.STATUS_MESSAGES = ipopt_wrapper.STATUS_MESSAGES
    return cyipopt

cyipopt, cyipopt_available = attempt_import(
     'ipopt',
     error_message='cyipopt solver relies on the ipopt module from cyipopt. '
     'See https://github.com/mechmotum/cyipopt.git for cyipopt '
     'installation instructions.',
     importer=_cyipopt_importer,
)

# Because pynumero.interfaces requires numpy, we will leverage deferred
# imports here so that the solver can be registered even when numpy is
# not available.
pyomo_nlp = attempt_import('pyomo.contrib.pynumero.interfaces.pyomo_nlp')[0]
pyomo_grey_box = attempt_import('pyomo.contrib.pynumero.interfaces.pyomo_grey_box_nlp')[0]
egb = attempt_import('pyomo.contrib.pynumero.interfaces.external_grey_box')[0]

from pyomo.common.config import ConfigBlock, ConfigValue
from pyomo.common.timing import TicTocTimer
from pyomo.core.base import Block, Objective, minimize
from pyomo.opt import (
    SolverStatus, SolverResults, TerminationCondition, ProblemSense
)

logger = logging.getLogger(__name__)

# This maps the cyipopt STATUS_MESSAGES back to string representations
# of the Ipopt ApplicationReturnStatus enum
_cyipopt_status_enum = [
    "Solve_Succeeded", (b"Algorithm terminated successfully at a locally "
                        b"optimal point, satisfying the convergence tolerances "
                        b"(can be specified by options)."),
    "Solved_To_Acceptable_Level", (b"Algorithm stopped at a point that was "
                                   b"converged, not to \"desired\" tolerances, "
                                   b"but to \"acceptable\" tolerances (see the "
                                   b"acceptable-... options)."),
    "Infeasible_Problem_Detected", (b"Algorithm converged to a point of local "
                                    b"infeasibility. Problem may be "
                                    b"infeasible."),
    "Search_Direction_Becomes_Too_Small", (b"Algorithm proceeds with very "
                                           b"little progress."),
    "Diverging_Iterates", b"It seems that the iterates diverge.",
    "User_Requested_Stop", (b"The user call-back function intermediate_callback "
                            b"(see Section 3.3.4 in the documentation) returned "
                            b"false, i.e., the user code requested a premature "
                            b"termination of the optimization."),
    "Feasible_Point_Found", b"Feasible point for square problem found.",
    "Maximum_Iterations_Exceeded", (b"Maximum number of iterations exceeded "
                                    b"(can be specified by an option)."),
    "Restoration_Failed", (b"Restoration phase failed, algorithm doesn\'t know "
                           b"how to proceed."),
    "Error_In_Step_Computation", (b"An unrecoverable error occurred while Ipopt "
                                  b"tried to compute the search direction."),
    "Maximum_CpuTime_Exceeded", b"Maximum CPU time exceeded.",
    "Not_Enough_Degrees_Of_Freedom", b"Problem has too few degrees of freedom.",
    "Invalid_Problem_Definition", b"Invalid problem definition.",
    "Invalid_Option", b"Invalid option encountered.",
    "Invalid_Number_Detected", (b"Algorithm received an invalid number (such as "
                                b"NaN or Inf) from the NLP; see also option "
                                b"check_derivatives_for_naninf."),
    # Note that the concluding "." was missing before cyipopt 1.0.3
    "Invalid_Number_Detected", (b"Algorithm received an invalid number (such as "
                                b"NaN or Inf) from the NLP; see also option "
                                b"check_derivatives_for_naninf"),
    "Unrecoverable_Exception", b"Some uncaught Ipopt exception encountered.",
    "NonIpopt_Exception_Thrown", b"Unknown Exception caught in Ipopt.",
    # Note that the concluding "." was missing before cyipopt 1.0.3
    "NonIpopt_Exception_Thrown", b"Unknown Exception caught in Ipopt",
    "Insufficient_Memory", b"Not enough memory.",
    "Internal_Error", (b"An unknown internal error occurred. Please contact "
                       b"the Ipopt authors through the mailing list."),
]
_cyipopt_status_enum = {
    _cyipopt_status_enum[i+1]: _cyipopt_status_enum[i]
    for i in range(0, len(_cyipopt_status_enum), 2)
}

# This maps Ipopt ApplicationReturnStatus enum strings to an appropriate
# Pyomo TerminationCondition
_ipopt_term_cond = {
    'Solve_Succeeded': TerminationCondition.optimal,
    'Solved_To_Acceptable_Level': TerminationCondition.feasible,
    'Infeasible_Problem_Detected': TerminationCondition.infeasible,
    'Search_Direction_Becomes_Too_Small': TerminationCondition.minStepLength,
    'Diverging_Iterates': TerminationCondition.unbounded,
    'User_Requested_Stop': TerminationCondition.userInterrupt,
    'Feasible_Point_Found': TerminationCondition.feasible,
    'Maximum_Iterations_Exceeded': TerminationCondition.maxIterations,
    'Restoration_Failed': TerminationCondition.noSolution,
    'Error_In_Step_Computation': TerminationCondition.solverFailure,
    'Maximum_CpuTime_Exceeded': TerminationCondition.maxTimeLimit,
    'Not_Enough_Degrees_Of_Freedom': TerminationCondition.invalidProblem,
    'Invalid_Problem_Definition': TerminationCondition.invalidProblem,
    'Invalid_Option': TerminationCondition.error,
    'Invalid_Number_Detected': TerminationCondition.internalSolverError,
    'Unrecoverable_Exception': TerminationCondition.internalSolverError,
    'NonIpopt_Exception_Thrown': TerminationCondition.error,
    'Insufficient_Memory': TerminationCondition.resourceInterrupt,
    'Internal_Error': TerminationCondition.internalSolverError,
}

class CyIpoptProblemInterface(object, metaclass=abc.ABCMeta):
    @abc.abstractmethod
    def x_init(self):
        """Return the initial values for x as a numpy ndarray
        """
        pass

    @abc.abstractmethod
    def x_lb(self):
        """Return the lower bounds on x as a numpy ndarray
        """
        pass

    @abc.abstractmethod
    def x_ub(self):
        """Return the upper bounds on x as a numpy ndarray
        """
        pass

    @abc.abstractmethod
    def g_lb(self):
        """Return the lower bounds on the constraints as a numpy ndarray
        """
        pass

    @abc.abstractmethod
    def g_ub(self):
        """Return the upper bounds on the constraints as a numpy ndarray
        """
        pass

    @abc.abstractmethod
    def scaling_factors(self):
        """Return the values for scaling factors as a tuple
        (objective_scaling, x_scaling, g_scaling). Return None
        if the scaling factors are to be ignored
        """
        pass

    @abc.abstractmethod
    def objective(self, x):
        """Return the value of the objective
        function evaluated at x
        """
        pass

    @abc.abstractmethod
    def gradient(self, x):
        """Return the gradient of the objective
        function evaluated at x as a numpy ndarray
        """
        pass

    @abc.abstractmethod
    def constraints(self, x):
        """Return the residuals of the constraints
        evaluated at x as a numpy ndarray
        """
        pass

    @abc.abstractmethod
    def jacobianstructure(self):
        """Return the structure of the jacobian
        in coordinate format. That is, return (rows,cols)
        where rows and cols are both numpy ndarray
        objects that contain the row and column indices
        for each of the nonzeros in the jacobian.
        """
        pass

    @abc.abstractmethod
    def jacobian(self, x):
        """Return the values for the jacobian evaluated at x
        as a numpy ndarray of nonzero values corresponding
        to the rows and columns specified in the jacobianstructure
        """
        pass

    @abc.abstractmethod
    def hessianstructure(self):
        """Return the structure of the hessian
        in coordinate format. That is, return (rows,cols)
        where rows and cols are both numpy ndarray
        objects that contain the row and column indices
        for each of the nonzeros in the hessian.
        Note: return ONLY the lower diagonal of this symmetric matrix.
        """
        pass

    @abc.abstractmethod
    def hessian(self, x, y, obj_factor):
        """Return the values for the hessian evaluated at x
        as a numpy ndarray of nonzero values corresponding
        to the rows and columns specified in the
        hessianstructure method.
        Note: return ONLY the lower diagonal of this symmetric matrix.
        """
        pass

    def intermediate(self, alg_mod, iter_count, obj_value,
            inf_pr, inf_du, mu, d_norm, regularization_size,
            alpha_du, alpha_pr, ls_trials):
        """Callback that can be used to examine or report intermediate
        results. This method is called each iteration
        """
        # TODO: Document the arguments
        pass


class CyIpoptNLP(CyIpoptProblemInterface):
    def __init__(self, nlp, intermediate_callback=None):
        """This class provides a CyIpoptProblemInterface for use
        with the CyIpoptSolver class that can take in an NLP
        as long as it provides vectors as numpy ndarrays and
        matrices as scipy.sparse.coo_matrix objects. This class
        provides the interface between AmplNLP or PyomoNLP objects
        and the CyIpoptSolver
        """
        self._nlp = nlp
        self._intermediate_callback = intermediate_callback

        x = nlp.init_primals()
        y = nlp.init_duals()
        if np.any(np.isnan(y)):
            # did not get initial values for y, use this default
            y.fill(1.0)

        self._cached_x = x.copy()
        self._cached_y = y.copy()
        self._cached_obj_factor = 1.0

        nlp.set_primals(self._cached_x)
        nlp.set_duals(self._cached_y)

        # get jacobian and hessian structures
        self._jac_g = nlp.evaluate_jacobian()
        try:
            self._hess_lag = nlp.evaluate_hessian_lag()
            self._hess_lower_mask = self._hess_lag.row >= self._hess_lag.col
            self._hessian_available = True
        except (AttributeError, NotImplementedError):
            self._hessian_available = False
            self._hess_lag = None
            self._hess_lower_mask = None

    def _set_primals_if_necessary(self, x):
        if not np.array_equal(x, self._cached_x):
            self._nlp.set_primals(x)
            self._cached_x = x.copy()

    def _set_duals_if_necessary(self, y):
        if not np.array_equal(y, self._cached_y):
            self._nlp.set_duals(y)
            self._cached_y = y.copy()

    def _set_obj_factor_if_necessary(self, obj_factor):
        if obj_factor != self._cached_obj_factor:
            self._nlp.set_obj_factor(obj_factor)
            self._cached_obj_factor = obj_factor

    def x_init(self):
        return self._nlp.init_primals()

    def x_lb(self):
        return self._nlp.primals_lb()

    def x_ub(self):
        return self._nlp.primals_ub()

    def g_lb(self):
        return self._nlp.constraints_lb()

    def g_ub(self):
        return self._nlp.constraints_ub()

    def scaling_factors(self):
        obj_scaling = self._nlp.get_obj_scaling()
        x_scaling = self._nlp.get_primals_scaling()
        g_scaling = self._nlp.get_constraints_scaling()
        return obj_scaling, x_scaling, g_scaling

    def objective(self, x):
        self._set_primals_if_necessary(x)
        return self._nlp.evaluate_objective()

    def gradient(self, x):
        self._set_primals_if_necessary(x)
        return self._nlp.evaluate_grad_objective()

    def constraints(self, x):
        self._set_primals_if_necessary(x)
        return self._nlp.evaluate_constraints()

    def jacobianstructure(self):
        return self._jac_g.row, self._jac_g.col

    def jacobian(self, x):
        self._set_primals_if_necessary(x)
        self._nlp.evaluate_jacobian(out=self._jac_g)
        return self._jac_g.data

    def hessianstructure(self):
        if not self._hessian_available:
            return np.zeros(0), np.zeros(0)

        row = np.compress(self._hess_lower_mask, self._hess_lag.row)
        col = np.compress(self._hess_lower_mask, self._hess_lag.col)
        return row, col


    def hessian(self, x, y, obj_factor):
        if not self._hessian_available:
            raise ValueError("Hessian requested, but not supported by the NLP")

        self._set_primals_if_necessary(x)
        self._set_duals_if_necessary(y)
        self._set_obj_factor_if_necessary(obj_factor)
        self._nlp.evaluate_hessian_lag(out=self._hess_lag)
        data = np.compress(self._hess_lower_mask, self._hess_lag.data)
        return data

    def intermediate(
            self,
            alg_mod,
            iter_count,
            obj_value,
            inf_pr,
            inf_du,
            mu,
            d_norm,
            regularization_size,
            alpha_du,
            alpha_pr,
            ls_trials
    ):
        if self._intermediate_callback is not None:
            return self._intermediate_callback(self._nlp, alg_mod, iter_count, obj_value,
                                               inf_pr, inf_du, mu, d_norm, regularization_size,
                                               alpha_du, alpha_pr, ls_trials)
        return True


class CyIpoptSolver(object):
    def __init__(self, problem_interface, options=None):
        """Create an instance of the CyIpoptSolver. You must
        provide a problem_interface that corresponds to
        the abstract class CyIpoptProblemInterface
        options can be provided as a dictionary of key value
        pairs
        """
        self._problem = problem_interface

        self._options = options
        if options is not None:
            assert isinstance(options, dict)
        else:
            self._options = dict()

    def solve(self, x0=None, tee=False):
        xl = self._problem.x_lb()
        xu = self._problem.x_ub()
        gl = self._problem.g_lb()
        gu = self._problem.g_ub()

        if x0 is None:
            x0 = self._problem.x_init()
        xstart = x0

        nx = len(xstart)
        ng = len(gl)

        cyipopt_solver = cyipopt.Problem(
            n=nx,
            m=ng,
            problem_obj=self._problem,
            lb=xl,
            ub=xu,
            cl=gl,
            cu=gu
        )

        # check if we need scaling
        obj_scaling, x_scaling, g_scaling = self._problem.scaling_factors()
        if any(_ is not None for _ in (obj_scaling, x_scaling, g_scaling)):
            # need to set scaling factors
            if obj_scaling is None:
                obj_scaling = 1.0
            if x_scaling is None:
                x_scaling = np.ones(nx)
            if g_scaling is None:
                g_scaling = np.ones(ng)
            try:
                set_scaling = cyipopt_solver.set_problem_scaling
            except AttributeError:
                # Fall back to pre-1.0.0 API
                set_scaling = cyipopt_solver.setProblemScaling
            set_scaling(obj_scaling, x_scaling, g_scaling)

        # add options
        try:
            add_option = cyipopt_solver.add_option
        except AttributeError:
            # Fall back to pre-1.0.0 API
            add_option = cyipopt_solver.addOption
        for k, v in self._options.items():
            add_option(k, v)

        # We preemptively set up the TeeStream, even if we aren't
        # going to use it: the implementation is such that the
        # context manager does nothing (i.e., doesn't start up any
        # processing threads) until afer a client accesses
        # STDOUT/STDERR
        with TeeStream(sys.stdout) as _teeStream:
            if tee:
                try:
                    fd = sys.stdout.fileno()
                except (io.UnsupportedOperation, AttributeError):
                    # If sys,stdout doesn't have a valid fileno,
                    # then create one using the TeeStream
                    fd = _teeStream.STDOUT.fileno()
            else:
                fd = None
            with redirect_fd(fd=1, output=fd, synchronize=False):
                x, info = cyipopt_solver.solve(xstart)

        return x, info


def _numpy_vector(val):
    ans = np.array(val, np.float64)
    if len(ans.shape) != 1:
        raise ValueError("expected a vector, but recieved a matrix "
                         "with shape %s" % (ans.shape,))
    return ans


class PyomoCyIpoptSolver(object):

    CONFIG = ConfigBlock("cyipopt")
    CONFIG.declare("tee", ConfigValue(
        default=False,
        domain=bool,
        description="Stream solver output to console",
    ))
    CONFIG.declare("load_solutions", ConfigValue(
        default=True,
        domain=bool,
        description="Store the final solution into the original Pyomo model",
    ))
    CONFIG.declare("return_nlp", ConfigValue(
        default=False,
        domain=bool,
        description="Return the results object and the underlying nlp"
                    " NLP object from the solve call.",
    ))
    CONFIG.declare("options", ConfigBlock(implicit=True))
    CONFIG.declare("intermediate_callback", ConfigValue(
        default=None,
        description="Set the function that will be called each"
                    " iteration."
    ))

    def __init__(self, **kwds):
        """Create an instance of the CyIpoptSolver. You must
        provide a problem_interface that corresponds to
        the abstract class CyIpoptProblemInterface

        options can be provided as a dictionary of key value
        pairs
        """
        self.config = self.CONFIG(kwds)

    def _set_model(self, model):
        self._model = model

    def available(self, exception_flag=False):
        return numpy_available and cyipopt_available

    def license_is_valid(self):
        return True

    def version(self):
        return tuple(int(_) for _ in cyipopt.__version__.split('.'))

    def solve(self, model, **kwds):
        config = self.config(kwds, preserve_implicit=True)

        if not isinstance(model, Block):
            raise ValueError("PyomoCyIpoptSolver.solve(model): model "
                             "must be a Pyomo Block")

        # If this is a Pyomo model / block, then we need to create
        # the appropriate PyomoNLP, then wrap it in a CyIpoptNLP
        grey_box_blocks = list(model.component_data_objects(
            egb.ExternalGreyBoxBlock, active=True))
        if grey_box_blocks:
            # nlp = pyomo_nlp.PyomoGreyBoxNLP(model)
            nlp = pyomo_grey_box.PyomoNLPWithGreyBoxBlocks(model)
        else:
            nlp = pyomo_nlp.PyomoNLP(model)

        problem = CyIpoptNLP(nlp, intermediate_callback=config.intermediate_callback)

        xl = problem.x_lb()
        xu = problem.x_ub()
        gl = problem.g_lb()
        gu = problem.g_ub()

        nx = len(xl)
        ng = len(gl)

        cyipopt_solver = cyipopt.Problem(
            n=nx,
            m=ng,
            problem_obj=problem,
            lb=xl,
            ub=xu,
            cl=gl,
            cu=gu
        )

        # check if we need scaling
        obj_scaling, x_scaling, g_scaling = problem.scaling_factors()
        if any(_ is not None for _ in (obj_scaling, x_scaling, g_scaling)):
            # need to set scaling factors
            if obj_scaling is None:
                obj_scaling = 1.0
            if x_scaling is None:
                x_scaling = np.ones(nx)
            if g_scaling is None:
                g_scaling = np.ones(ng)
            try:
                set_scaling = cyipopt_solver.set_problem_scaling
            except AttributeError:
                # Fall back to pre-1.0.0 API
                set_scaling = cyipopt_solver.setProblemScaling
            set_scaling(obj_scaling, x_scaling, g_scaling)

        # add options
        try:
            add_option = cyipopt_solver.add_option
        except AttributeError:
            # Fall back to pre-1.0.0 API
            add_option = cyipopt_solver.addOption
        for k, v in config.options.items():
            add_option(k, v)

        timer = TicTocTimer()
        try:
            # We preemptively set up the TeeStream, even if we aren't
            # going to use it: the implementation is such that the
            # context manager does nothing (i.e., doesn't start up any
            # processing threads) until afer a client accesses
            # STDOUT/STDERR
            with TeeStream(sys.stdout) as _teeStream:
                if config.tee:
                    try:
                        fd = sys.stdout.fileno()
                    except (io.UnsupportedOperation, AttributeError):
                        # If sys,stdout doesn't have a valid fileno,
                        # then create one using the TeeStream
                        fd = _teeStream.STDOUT.fileno()
                else:
                    fd = None
                with redirect_fd(fd=1, output=fd, synchronize=False):
                    x, info = cyipopt_solver.solve(problem.x_init())
            solverStatus = SolverStatus.ok
        except:
            msg = "Exception encountered during cyipopt solve:"
            logger.error(msg, exc_info=sys.exc_info())
            solverStatus = SolverStatus.unknown
            raise

        wall_time = timer.toc(None)

        results = SolverResults()

        if config.load_solutions:
            nlp.set_primals(x)
            nlp.set_duals(info['mult_g'])
            nlp.load_state_into_pyomo(
                bound_multipliers=(info['mult_x_L'], info['mult_x_U']))
        else:
            soln = results.solution.add()
            soln.variable.update(
                (i, {'Value':j, 'ipopt_zL_out': zl, 'ipopt_zU_out': zu})
                for i,j,zl,zu in zip( nlp.variable_names(),
                                      x,
                                      info['mult_x_L'],
                                      info['mult_x_U'] )
            )
            soln.constraint.update(
                (i, {'Dual':j}) for i,j in zip(
                    nlp.constraint_names(), info['mult_g']))


        results.problem.name = model.name
        obj = next(model.component_data_objects(Objective, active=True))
        if obj.sense == minimize:
            results.problem.sense = ProblemSense.minimize
            results.problem.upper_bound = info['obj_val']
        else:
            results.problem.sense = ProblemSense.maximize
            results.problem.lower_bound = info['obj_val']
        results.problem.number_of_objectives = 1
        results.problem.number_of_constraints = ng
        results.problem.number_of_variables = nx
        results.problem.number_of_binary_variables = 0
        results.problem.number_of_integer_variables = 0
        results.problem.number_of_continuous_variables = nx
        # TODO: results.problem.number_of_nonzeros

        results.solver.name = 'cyipopt'
        results.solver.return_code = info['status']
        results.solver.message = info['status_msg']
        results.solver.wallclock_time = wall_time
        status_enum = _cyipopt_status_enum[info['status_msg']]
        results.solver.termination_condition = _ipopt_term_cond[status_enum]
        results.solver.status = TerminationCondition.to_solver_status(
            results.solver.termination_condition)

        if config.return_nlp:
            return results, nlp

        return results

    #
    # Support "with" statements.
    #
    def __enter__(self):
        return self

    def __exit__(self, t, v, traceback):
        pass
