text: """Define a base class for all Drivers in OpenMDAO."""
  from collections import OrderedDict
  import pprint
  import sys
  import os
  import weakref
  
  import numpy as np
  
  from openmdao.core.total_jac import _TotalJacInfo
  from openmdao.core.constants import INT_DTYPE
  from openmdao.recorders.recording_manager import RecordingManager
  from openmdao.recorders.recording_iteration_stack import Recording
  from openmdao.utils.record_util import create_local_meta, check_path
  from openmdao.utils.general_utils import simple_warning, warn_deprecation, prom2ivc_src_dict
  from openmdao.utils.mpi import MPI
  from openmdao.utils.options_dictionary import OptionsDictionary
  import openmdao.utils.coloring as coloring_mod
  from openmdao.utils.array_utils import sizes2offsets, convert_neg
  from openmdao.vectors.vector import _full_slice
  
  
  def _check_debug_print_opts_valid(name, opts):
      """
      Check validity of debug_print option for Driver.
  
      Parameters
      ----------
      name : str
          The name of the option.
      opts : list
          The value of the debug_print option set by the user.
      """
      if not isinstance(opts, list):
          raise ValueError("Option '%s' with value %s is not a list." % (name, opts))
  
      _valid_opts = ['desvars', 'nl_cons', 'ln_cons', 'objs', 'totals']
      for opt in opts:
          if opt not in _valid_opts:
              raise ValueError("Option '%s' contains value '%s' which is not one of %s." %
                               (name, opt, _valid_opts))
  
  
  class Driver(object):
      """
      Top-level container for the systems and drivers.
  
      Attributes
      ----------
      fail : bool
          Reports whether the driver ran successfully.
      iter_count : int
          Keep track of iterations for case recording.
      options : <OptionsDictionary>
          Dictionary with general pyoptsparse options.
      recording_options : <OptionsDictionary>
          Dictionary with driver recording options.
      cite : str
          Listing of relevant citations that should be referenced when
          publishing work that uses this class.
      _problem : weakref to <Problem>
          Pointer to the containing problem.
      supports : <OptionsDictionary>
          Provides a consistent way for drivers to declare what features they support.
      _designvars : dict
          Contains all design variable info.
      _designvars_discrete : list
          List of design variables that are discrete.
      _dist_driver_vars : dict
          Dict of constraints that are distributed outputs. Key is rank, values are
          (local indices, local sizes).
      _cons : dict
          Contains all constraint info.
      _objs : dict
          Contains all objective info.
      _responses : dict
          Contains all response info.
      _remote_dvs : dict
          Dict of design variables that are remote on at least one proc. Values are
          (owning rank, size).
      _remote_cons : dict
          Dict of constraints that are remote on at least one proc. Values are
          (owning rank, size).
      _remote_objs : dict
          Dict of objectives that are remote on at least one proc. Values are
          (owning rank, size).
      _rec_mgr : <RecordingManager>
          Object that manages all recorders added to this driver.
      _coloring_info : dict
          Metadata pertaining to total coloring.
      _total_jac_sparsity : dict, str, or None
          Specifies sparsity of sub-jacobians of the total jacobian. Only used by pyOptSparseDriver.
      _res_jacs : dict
          Dict of sparse subjacobians for use with certain optimizers, e.g. pyOptSparseDriver.
      _total_jac : _TotalJacInfo or None
          Cached total jacobian handling object.
      """
  
      def __init__(self, **kwargs):
          """
          Initialize the driver.
  
          Parameters
          ----------
          **kwargs : dict of keyword arguments
              Keyword arguments that will be mapped into the Driver options.
          """
          self._rec_mgr = RecordingManager()
  
          self._problem = None
          self._designvars = None
          self._designvars_discrete = []
          self._cons = None
          self._objs = None
          self._responses = None
  
          # Driver options
          self.options = OptionsDictionary(parent_name=type(self).__name__)
  
          self.options.declare('debug_print', types=list, check_valid=_check_debug_print_opts_valid,
                               desc="List of what type of Driver variables to print at each "
                                    "iteration. Valid items in list are 'desvars', 'ln_cons', "
                                    "'nl_cons', 'objs', 'totals'",
                               default=[])
  
          # Case recording options
          self.recording_options = OptionsDictionary(parent_name=type(self).__name__)
  
          self.recording_options.declare('record_model_metadata', types=bool, default=True,
                                         desc='Deprecated. Recording of model metadata will always '
                                         'be done',
                                         deprecation="The recording option, record_model_metadata, "
                                         "on Driver is "
                                         "deprecated. Recording of model metadata will always "
                                         "be done",
                                         )
          self.recording_options.declare('record_desvars', types=bool, default=True,
                                         desc='Set to True to record design variables at the '
                                              'driver level')
          self.recording_options.declare('record_responses', types=bool, default=False,
                                         desc='Set True to record constraints and objectives at the '
                                              'driver level')
  
          self.recording_options.declare('record_objectives', types=bool, default=True,
                                         desc='Set to True to record objectives at the driver level')
          self.recording_options.declare('record_constraints', types=bool, default=True,
                                         desc='Set to True to record constraints at the '
                                              'driver level')
          self.recording_options.declare('includes', types=list, default=[],
                                         desc='Patterns for variables to include in recording. '
                                         'Uses fnmatch wildcards')
          self.recording_options.declare('excludes', types=list, default=[],
                                         desc='Patterns for vars to exclude in recording '
                                              '(processed post-includes). Uses fnmatch wildcards')
          self.recording_options.declare('record_derivatives', types=bool, default=False,
                                         desc='Set to True to record derivatives at the driver '
                                              'level')
          self.recording_options.declare('record_inputs', types=bool, default=True,
                                         desc='Set to True to record inputs at the driver level')
          self.recording_options.declare('record_outputs', types=bool, default=True,
                                         desc='Set True to record outputs at the '
                                              'driver level.')
          self.recording_options.declare('record_residuals', types=bool, default=False,
                                         desc='Set True to record residuals at the '
                                              'driver level.')
  
          # What the driver supports.
          self.supports = OptionsDictionary(parent_name=type(self).__name__)
          self.supports.declare('inequality_constraints', types=bool, default=False)
          self.supports.declare('equality_constraints', types=bool, default=False)
          self.supports.declare('linear_constraints', types=bool, default=False)
          self.supports.declare('two_sided_constraints', types=bool, default=False)
          self.supports.declare('multiple_objectives', types=bool, default=False)
          self.supports.declare('integer_design_vars', types=bool, default=True)
          self.supports.declare('gradients', types=bool, default=False)
          self.supports.declare('active_set', types=bool, default=False)
          self.supports.declare('simultaneous_derivatives', types=bool, default=False)
          self.supports.declare('total_jac_sparsity', types=bool, default=False)
          self.supports.declare('distributed_design_vars', types=bool, default=True)
  
          self.iter_count = 0
          self.cite = ""
  
          self._coloring_info = coloring_mod._get_coloring_meta()
  
          self._total_jac_sparsity = None
          self._res_jacs = {}
          self._total_jac = None
  
          self.fail = False
  
          self._declare_options()
          self.options.update(kwargs)
  
      @property
      def msginfo(self):
          """
          Return info to prepend to messages.
  
          Returns
          -------
          str
              Info to prepend to messages.
          """
          return type(self).__name__
  
      def add_recorder(self, recorder):
          """
          Add a recorder to the driver.
  
          Parameters
          ----------
          recorder : CaseRecorder
             A recorder instance.
          """
          self._rec_mgr.append(recorder)
  
      def cleanup(self):
          """
          Clean up resources prior to exit.
          """
          # shut down all recorders
          self._rec_mgr.shutdown()
  
      def _declare_options(self):
          """
          Declare options before kwargs are processed in the init method.
  
          This is optionally implemented by subclasses of Driver.
          """
          pass
  
      def _setup_comm(self, comm):
          """
          Perform any driver-specific setup of communicators for the model.
  
          Parameters
          ----------
          comm : MPI.Comm or <FakeComm> or None
              The communicator for the Problem.
  
          Returns
          -------
          MPI.Comm or <FakeComm> or None
              The communicator for the Problem model.
          """
          return comm
  
      def _setup_driver(self, problem):
          """
          Prepare the driver for execution.
  
          This is the final thing to run during setup.
  
          Parameters
          ----------
          problem : <Problem>
              Pointer to the containing problem.
          """
          self._problem = weakref.ref(problem)
          model = problem.model
  
          self._total_jac = None
  
          self._has_scaling = (
              np.any([r['total_scaler'] is not None for r in self._responses.values()]) or
              np.any([dv['total_scaler'] is not None for dv in self._designvars.values()])
          )
  
          # Determine if any design variables are discrete.
          self._designvars_discrete = [name for name, meta in self._designvars.items()
                                       if meta['ivc_source'] in model._discrete_outputs]
          if not self.supports['integer_design_vars'] and len(self._designvars_discrete) > 0:
              msg = "Discrete design variables are not supported by this driver: "
              msg += '.'.join(self._designvars_discrete)
              raise RuntimeError(msg)
  
          con_set = set()
          obj_set = set()
          dv_set = set()
  
          self._remote_dvs = remote_dv_dict = {}
          self._remote_cons = remote_con_dict = {}
          self._dist_driver_vars = dist_dict = {}
          self._remote_objs = remote_obj_dict = {}
  
          src_design_vars = prom2ivc_src_dict(self._designvars)
          src_cons = prom2ivc_src_dict(self._cons)
          src_objs = prom2ivc_src_dict(self._objs)
          responses = prom2ivc_src_dict(self._responses)
  
          # Only allow distributed design variables on drivers that support it.
          if self.supports['distributed_design_vars'] is False:
              dist_vars = []
              abs2meta_in = model._var_allprocs_abs2meta['input']
              discrete_in = model._var_allprocs_discrete['input']
              for dv, meta in self._designvars.items():
  
                  # For Auto-ivcs, we need to check the distributed metadata on the target instead.
                  if meta['ivc_source'].startswith('_auto_ivc.'):
                      for abs_name in model._var_allprocs_prom2abs_list['input'][dv]:
                          if abs_name in discrete_in:
                              # Discrete vars aren't distributed.
                              break
  
                          if abs2meta_in[abs_name]['distributed']:
                              dist_vars.append(dv)
                              break
                  elif meta['distributed']:
                      dist_vars.append(dv)
  
              if dist_vars:
                  dstr = ', '.join(dist_vars)
                  msg = "Distributed design variables are not supported by this driver, but the "
                  msg += f"following variables are distributed: [{dstr}]"
                  raise RuntimeError(msg)
  
          # Now determine if later we'll need to allgather cons, objs, or desvars.
          if model.comm.size > 1 and model._subsystems_allprocs:
              local_out_vars = set(model._outputs._abs_iter())
              remote_dvs = set(src_design_vars) - local_out_vars
              remote_cons = set(src_cons) - local_out_vars
              remote_objs = set(src_objs) - local_out_vars
  
              all_remote_vois = model.comm.allgather(
                  (remote_dvs, remote_cons, remote_objs))
              for rem_dvs, rem_cons, rem_objs in all_remote_vois:
                  con_set.update(rem_cons)
                  obj_set.update(rem_objs)
                  dv_set.update(rem_dvs)
  
              # If we have remote VOIs, pick an owning rank for each and use that
              # to bcast to others later
              owning_ranks = model._owning_rank
              sizes = model._var_sizes['nonlinear']['output']
              rank = model.comm.rank
              nprocs = model.comm.size
              for i, (vname, meta) in enumerate(model._var_allprocs_abs2meta['output'].items()):
                  if vname in responses:
                      indices = responses[vname].get('indices')
                  elif vname in src_design_vars:
                      indices = src_design_vars[vname].get('indices')
                  else:
                      continue
  
                  if meta['distributed']:
  
                      idx = model._var_allprocs_abs2idx['nonlinear'][vname]
                      dist_sizes = model._var_sizes['nonlinear']['output'][:, idx]
                      total_dist_size = np.sum(dist_sizes)
  
                      # Determine which indices are on our proc.
                      offsets = sizes2offsets(dist_sizes)
  
                      if indices is not None:
                          indices = convert_neg(indices, total_dist_size)
                          true_sizes = np.zeros(nprocs, dtype=INT_DTYPE)
                          for irank in range(nprocs):
                              dist_inds = indices[np.logical_and(indices >= offsets[irank],
                                                                 indices < (offsets[irank] +
                                                                            dist_sizes[irank]))]
                              if irank == rank:
                                  local_indices = dist_inds - offsets[rank]
                                  distrib_indices = dist_inds
  
                              true_sizes[irank] = dist_inds.size
                          dist_dict[vname] = (local_indices, true_sizes, distrib_indices)
                      else:
                          dist_dict[vname] = (_full_slice, dist_sizes,
                                              slice(offsets[rank], offsets[rank] + dist_sizes[rank]))
  
                  else:
                      owner = owning_ranks[vname]
                      sz = sizes[owner, i]
  
                      if vname in dv_set:
                          remote_dv_dict[vname] = (owner, sz)
                      if vname in con_set:
                          remote_con_dict[vname] = (owner, sz)
                      if vname in obj_set:
                          remote_obj_dict[vname] = (owner, sz)
  
          self._remote_responses = self._remote_cons.copy()
          self._remote_responses.update(self._remote_objs)
  
          # set up simultaneous deriv coloring
          if coloring_mod._use_total_sparsity:
              # reset the coloring
              if self._coloring_info['dynamic'] or self._coloring_info['static'] is not None:
                  self._coloring_info['coloring'] = None
  
              coloring = self._get_static_coloring()
              if coloring is not None and self.supports['simultaneous_derivatives']:
                  if model._owns_approx_jac:
                      coloring._check_config_partial(model)
                  else:
                      coloring._check_config_total(self)
                  self._setup_simul_coloring()
  
      def _check_for_missing_objective(self):
          """
          Check for missing objective and raise error if no objectives found.
          """
          if len(self._objs) == 0:
              msg = "Driver requires objective to be declared"
              raise RuntimeError(msg)
  
      def _get_vars_to_record(self, recording_options):
          """
          Get variables to record based on recording options.
  
          Parameters
          ----------
          recording_options : <OptionsDictionary>
              Dictionary with recording options.
  
          Returns
          -------
          dict
             Dictionary containing lists of variables to record.
          """
          problem = self._problem()
          model = problem.model
  
          incl = recording_options['includes']
          excl = recording_options['excludes']
  
          # includes and excludes for outputs are specified using promoted names
          abs2prom = model._var_allprocs_abs2prom['output']
  
          # 1. If record_outputs is True, get the set of outputs
          # 2. Filter those using includes and excludes to get the baseline set of variables to record
          # 3. Add or remove from that set any desvars, objs, and cons based on the recording
          #    options of those
  
          # includes and excludes for outputs are specified using _promoted_ names
          # vectors are keyed on absolute name, discretes on relative/promoted name
          myinputs = myoutputs = myresiduals = []
  
          if recording_options['record_outputs']:
              myoutputs = sorted([n for n, prom in abs2prom.items() if check_path(prom, incl, excl)])
  
              model_outs = model._outputs
  
              if model._var_discrete['output']:
                  # if we have discrete outputs then residual name set doesn't match output one
                  if recording_options['record_residuals']:
                      myresiduals = [n for n in myoutputs if model_outs._contains_abs(n)]
              elif recording_options['record_residuals']:
                  myresiduals = myoutputs
  
          elif recording_options['record_residuals']:
              myresiduals = [n for n in model._residuals._abs_iter()
                             if check_path(abs2prom[n], incl, excl)]
  
          myoutputs = set(myoutputs)
          if recording_options['record_desvars']:
              myoutputs.update(self._designvars)
          if recording_options['record_objectives'] or recording_options['record_responses']:
              myoutputs.update(self._objs)
          if recording_options['record_constraints'] or recording_options['record_responses']:
              myoutputs.update(self._cons)
  
          # inputs (if in options). inputs use _absolute_ names for includes/excludes
          if 'record_inputs' in recording_options:
              if recording_options['record_inputs']:
                  # sort the results since _var_allprocs_abs2prom isn't ordered
                  myinputs = sorted([n for n in model._var_allprocs_abs2prom['input']
                                    if check_path(n, incl, excl)])
  
          vars2record = {
              'input': myinputs,
              'output': list(myoutputs),
              'residual': myresiduals
          }
  
          return vars2record
  
      def _setup_recording(self):
          """
          Set up case recording.
          """
          self._filtered_vars_to_record = self._get_vars_to_record(self.recording_options)
  
          self._rec_mgr.startup(self)
  
      def _get_voi_val(self, name, meta, remote_vois, driver_scaling=True,
                       get_remote=True, rank=None):
          """
          Get the value of a variable of interest (objective, constraint, or design var).
  
          This will retrieve the value if the VOI is remote.
  
          Parameters
          ----------
          name : str
              Name of the variable of interest.
          meta : dict
              Metadata for the variable of interest.
          remote_vois : dict
              Dict containing (owning_rank, size) for all remote vois of a particular
              type (design var, constraint, or objective).
          driver_scaling : bool
              When True, return values that are scaled according to either the adder and scaler or
              the ref and ref0 values that were specified when add_design_var, add_objective, and
              add_constraint were called on the model. Default is True.
          get_remote : bool or None
              If True, retrieve the value even if it is on a remote process.  Note that if the
              variable is remote on ANY process, this function must be called on EVERY process
              in the Problem's MPI communicator.
              If False, only retrieve the value if it is on the current process, or only the part
              of the value that's on the current process for a distributed variable.
          rank : int or None
              If not None, gather value to this rank only.
  
          Returns
          -------
          float or ndarray
              The value of the named variable of interest.
          """
          model = self._problem().model
          comm = model.comm
          get = model._outputs._abs_get_val
          distributed_vars = self._dist_driver_vars
          indices = meta['indices']
  
          if meta.get('ivc_source') is not None:
              src_name = meta['ivc_source']
          else:
              src_name = name
  
          if MPI:
              distributed = comm.size > 0 and src_name in distributed_vars
          else:
              distributed = False
  
          if src_name in remote_vois:
              owner, size = remote_vois[src_name]
              # if var is distributed or only gathering to one rank
              # TODO - support distributed var under a parallel group.
              if owner is None or rank is not None:
                  val = model.get_val(src_name, get_remote=get_remote, rank=rank, flat=True)
                  if indices is not None:
                      val = val[indices]
              else:
                  if owner == comm.rank:
                      if indices is None:
                          val = get(name).copy()
                      else:
                          val = get(name)[indices]
                  else:
                      if indices is not None:
                          size = len(indices)
                      val = np.empty(size)
  
                  if get_remote:
                      comm.Bcast(val, root=owner)
  
          elif distributed:
              local_val = model.get_val(src_name, get_remote=False, flat=True)
              local_indices, sizes, _ = distributed_vars[src_name]
              if local_indices is not None:
                  local_val = local_val[local_indices]
  
              if get_remote:
                  offsets = np.zeros(sizes.size, dtype=INT_DTYPE)
                  offsets[1:] = np.cumsum(sizes[:-1])
                  val = np.zeros(np.sum(sizes))
                  comm.Allgatherv(local_val, [val, sizes, offsets, MPI.DOUBLE])
              else:
                  val = local_val
  
          else:
              if name in self._designvars_discrete:
                  val = model._discrete_outputs[src_name]
  
                  # At present, only integers are supported by OpenMDAO drivers.
                  # We check the values here.
                  msg = "Only integer scalars or ndarrays are supported as values for " + \
                        "discrete variables when used as a design variable. "
                  if np.isscalar(val) and not isinstance(val, (int, np.integer)):
                      msg += "A value of type '{}' was specified.".format(val.__class__.__name__)
                      raise ValueError(msg)
                  elif isinstance(val, np.ndarray) and not np.issubdtype(val[0], np.integer):
                      msg += "An array of type '{}' was specified.".format(val[0].__class__.__name__)
                      raise ValueError(msg)
  
              elif indices is None:
                  val = get(src_name).copy()
              else:
                  val = get(src_name)[indices]
  
          if self._has_scaling and driver_scaling:
              # Scale design variable values
              adder = meta['total_adder']
              if adder is not None:
                  val += adder
  
              scaler = meta['total_scaler']
              if scaler is not None:
                  val *= scaler
  
          return val
  
      def get_design_var_values(self, get_remote=True):
          """
          Return the design variable values.
  
          Parameters
          ----------
          get_remote : bool or None
              If True, retrieve the value even if it is on a remote process.  Note that if the
              variable is remote on ANY process, this function must be called on EVERY process
              in the Problem's MPI communicator.
              If False, only retrieve the value if it is on the current process, or only the part
              of the value that's on the current process for a distributed variable.
  
          Returns
          -------
          dict
             Dictionary containing values of each design variable.
          """
          return {n: self._get_voi_val(n, dv, self._remote_dvs, get_remote=get_remote)
                  for n, dv in self._designvars.items()}
  
      def set_design_var(self, name, value, set_remote=True):
          """
          Set the value of a design variable.
  
          Parameters
          ----------
          name : str
              Global pathname of the design variable.
          value : float or ndarray
              Value for the design variable.
          set_remote : bool
              If True, set the global value of the variable (value must be of the global size).
              If False, set the local value of the variable (value must be of the local size).
          """
          problem = self._problem()
          meta = self._designvars[name]
  
          src_name = meta['ivc_source']
  
          # if the value is not local, don't set the value
          if (src_name in self._remote_dvs and
                  problem.model._owning_rank[src_name] != problem.comm.rank):
              return
  
          indices = meta['indices']
          if indices is None:
              indices = _full_slice
  
          if name in self._designvars_discrete:
  
              # Note, drivers set values here and generally should know it is setting an integer.
              # However, the DOEdriver may pull a non-integer value from its generator, so we
              # convert it.
              if isinstance(value, float):
                  value = int(value)
              elif isinstance(value, np.ndarray):
                  if isinstance(problem.model._discrete_outputs[src_name], int):
                      # Setting an integer value with a 1D array - don't want to convert to array.
                      value = int(value)
                  else:
                      value = value.astype(np.int)
  
              problem.model._discrete_outputs[src_name] = value
  
          elif problem.model._outputs._contains_abs(src_name):
              desvar = problem.model._outputs._abs_get_val(src_name)
              if src_name in self._dist_driver_vars:
                  loc_idxs, _, dist_idxs = self._dist_driver_vars[src_name]
              else:
                  loc_idxs = indices
                  dist_idxs = _full_slice
  
              if set_remote:
                  # provided value is the global value, use indices for this proc
                  desvar[loc_idxs] = np.atleast_1d(value)[dist_idxs]
              else:
                  # provided value is the local value
                  desvar[loc_idxs] = np.atleast_1d(value)
  
              # Undo driver scaling when setting design var values into model.
              if self._has_scaling:
                  scaler = meta['total_scaler']
                  if scaler is not None:
                      desvar[loc_idxs] *= 1.0 / scaler
  
                  adder = meta['total_adder']
                  if adder is not None:
                      desvar[loc_idxs] -= adder
  
      def get_objective_values(self, driver_scaling=True):
          """
          Return objective values.
  
          Parameters
          ----------
          driver_scaling : bool
              When True, return values that are scaled according to either the adder and scaler or
              the ref and ref0 values that were specified when add_design_var, add_objective, and
              add_constraint were called on the model. Default is True.
  
          Returns
          -------
          dict
             Dictionary containing values of each objective.
          """
          return {n: self._get_voi_val(n, obj, self._remote_objs,
                                       driver_scaling=driver_scaling)
                  for n, obj in self._objs.items()}
  
      def get_constraint_values(self, ctype='all', lintype='all', driver_scaling=True):
          """
          Return constraint values.
  
          Parameters
          ----------
          ctype : string
              Default is 'all'. Optionally return just the inequality constraints
              with 'ineq' or the equality constraints with 'eq'.
          lintype : string
              Default is 'all'. Optionally return just the linear constraints
              with 'linear' or the nonlinear constraints with 'nonlinear'.
          driver_scaling : bool
              When True, return values that are scaled according to either the adder and scaler or
              the ref and ref0 values that were specified when add_design_var, add_objective, and
              add_constraint were called on the model. Default is True.
  
          Returns
          -------
          dict
             Dictionary containing values of each constraint.
          """
          con_dict = {}
          for name, meta in self._cons.items():
              if lintype == 'linear' and not meta['linear']:
                  continue
  
              if lintype == 'nonlinear' and meta['linear']:
                  continue
  
              if ctype == 'eq' and meta['equals'] is None:
                  continue
  
              if ctype == 'ineq' and meta['equals'] is not None:
                  continue
  
              con_dict[name] = self._get_voi_val(name, meta, self._remote_cons,
                                                 driver_scaling=driver_scaling)
  
          return con_dict
  
      def _get_ordered_nl_responses(self):
          """
          Return the names of nonlinear responses in the order used by the driver.
  
          Default order is objectives followed by nonlinear constraints.  This is used for
          simultaneous derivative coloring and sparsity determination.
  
          Returns
          -------
          list of str
              The nonlinear response names in order.
          """
          order = list(self._objs)
          order.extend(n for n, meta in self._cons.items()
                       if not ('linear' in meta and meta['linear']))
          return order
  
      def _update_voi_meta(self, model):
          """
          Collect response and design var metadata from the model and size desvars and responses.
  
          Parameters
          ----------
          model : System
              The System that represents the entire model.
  
          Returns
          -------
          int
              Total size of responses, with linear constraints excluded.
          int
              Total size of design vars.
          """
          self._objs = objs = OrderedDict()
          self._cons = cons = OrderedDict()
  
          model._setup_driver_units()
  
          self._responses = resps = model.get_responses(recurse=True, use_prom_ivc=True)
          for name, data in resps.items():
              if data['type'] == 'con':
                  cons[name] = data
              else:
                  objs[name] = data
  
          response_size = sum(resps[n]['global_size'] for n in self._get_ordered_nl_responses())
  
          # Gather up the information for design vars.
          self._designvars = designvars = model.get_design_vars(recurse=True, use_prom_ivc=True)
          desvar_size = sum(data['global_size'] for data in designvars.values())
  
          return response_size, desvar_size
  
      def run(self):
          """
          Execute this driver.
  
          The base `Driver` just runs the model. All other drivers overload
          this method.
  
          Returns
          -------
          boolean
              Failure flag; True if failed to converge, False is successful.
          """
          with RecordingDebugging(self._get_name(), self.iter_count, self):
              self._problem().model.run_solve_nonlinear()
  
          self.iter_count += 1
          return False
  
      @property
      def _recording_iter(self):
          return self._problem()._metadata['recording_iter']
  
      def _compute_totals(self, of=None, wrt=None, return_format='flat_dict', global_names=None,
                          use_abs_names=True):
          """
          Compute derivatives of desired quantities with respect to desired inputs.
  
          All derivatives are returned using driver scaling.
  
          Parameters
          ----------
          of : list of variable name strings or None
              Variables whose derivatives will be computed. Default is None, which
              uses the driver's objectives and constraints.
          wrt : list of variable name strings or None
              Variables with respect to which the derivatives will be computed.
              Default is None, which uses the driver's desvars.
          return_format : string
              Format to return the derivatives. Default is a 'flat_dict', which
              returns them in a dictionary whose keys are tuples of form (of, wrt). For
              the scipy optimizer, 'array' is also supported.
          global_names : bool
              Deprecated.  Use 'use_abs_names' instead.
          use_abs_names : bool
              Set to True when passing in absolute names to skip some translation steps.
  
          Returns
          -------
          derivs : object
              Derivatives in form requested by 'return_format'.
          """
          problem = self._problem()
          total_jac = self._total_jac
          debug_print = 'totals' in self.options['debug_print'] and (not MPI or
                                                                     problem.comm.rank == 0)
  
          if debug_print:
              header = 'Driver total derivatives for iteration: ' + str(self.iter_count)
              print(header)
              print(len(header) * '-' + '\n')
  
          if global_names is not None:
              warn_deprecation("'global_names' is deprecated in calls to _compute_totals. "
                               "Use 'use_abs_names' instead.")
              use_abs_names = global_names
  
          if problem.model._owns_approx_jac:
              self._recording_iter.push(('_compute_totals_approx', 0))
  
              try:
                  if total_jac is None:
                      total_jac = _TotalJacInfo(problem, of, wrt, use_abs_names,
                                                return_format, approx=True, debug_print=debug_print)
  
                      # Don't cache linear constraint jacobian
                      if not total_jac.has_lin_cons:
                          self._total_jac = total_jac
  
                      totals = total_jac.compute_totals_approx(initialize=True)
                  else:
                      totals = total_jac.compute_totals_approx()
              finally:
                  self._recording_iter.pop()
  
          else:
              if total_jac is None:
                  total_jac = _TotalJacInfo(problem, of, wrt, use_abs_names, return_format,
                                            debug_print=debug_print)
  
                  # don't cache linear constraint jacobian
                  if not total_jac.has_lin_cons:
                      self._total_jac = total_jac
  
              self._recording_iter.push(('_compute_totals', 0))
  
              try:
                  totals = total_jac.compute_totals()
              finally:
                  self._recording_iter.pop()
  
          if self._rec_mgr._recorders and self.recording_options['record_derivatives']:
              metadata = create_local_meta(self._get_name())
              total_jac.record_derivatives(self, metadata)
  
          return totals
  
      def record_iteration(self):
          """
          Record an iteration of the current Driver.
          """
          record_iteration(self, self._problem(), self._get_name())
  
      def _get_recorder_metadata(self, case_name):
          """
          Return metadata from the latest iteration for use in the recorder.
  
          Parameters
          ----------
          case_name : str
              Name of current case.
  
          Returns
          -------
          dict
              Metadata dictionary for the recorder.
          """
          return create_local_meta(case_name)
  
      def _get_name(self):
          """
          Get name of current Driver.
  
          Returns
          -------
          str
              Name of current Driver.
          """
          return "Driver"
  
      def declare_coloring(self, num_full_jacs=coloring_mod._DEF_COMP_SPARSITY_ARGS['num_full_jacs'],
                           tol=coloring_mod._DEF_COMP_SPARSITY_ARGS['tol'],
                           orders=coloring_mod._DEF_COMP_SPARSITY_ARGS['orders'],
                           perturb_size=coloring_mod._DEF_COMP_SPARSITY_ARGS['perturb_size'],
                           min_improve_pct=coloring_mod._DEF_COMP_SPARSITY_ARGS['min_improve_pct'],
                           show_summary=coloring_mod._DEF_COMP_SPARSITY_ARGS['show_summary'],
                           show_sparsity=coloring_mod._DEF_COMP_SPARSITY_ARGS['show_sparsity']):
          """
          Set options for total deriv coloring.
  
          Parameters
          ----------
          num_full_jacs : int
              Number of times to repeat partial jacobian computation when computing sparsity.
          tol : float
              Tolerance used to determine if an array entry is nonzero during sparsity determination.
          orders : int
              Number of orders above and below the tolerance to check during the tolerance sweep.
          perturb_size : float
              Size of input/output perturbation during generation of sparsity.
          min_improve_pct : float
              If coloring does not improve (decrease) the number of solves more than the given
              percentage, coloring will not be used.
          show_summary : bool
              If True, display summary information after generating coloring.
          show_sparsity : bool
              If True, display sparsity with coloring info after generating coloring.
          """
          self._coloring_info['num_full_jacs'] = num_full_jacs
          self._coloring_info['tol'] = tol
          self._coloring_info['orders'] = orders
          self._coloring_info['perturb_size'] = perturb_size
          self._coloring_info['min_improve_pct'] = min_improve_pct
          if self._coloring_info['static'] is None:
              self._coloring_info['dynamic'] = True
          else:
              self._coloring_info['dynamic'] = False
          self._coloring_info['coloring'] = None
          self._coloring_info['show_summary'] = show_summary
          self._coloring_info['show_sparsity'] = show_sparsity
  
      def use_fixed_coloring(self, coloring=coloring_mod._STD_COLORING_FNAME):
          """
          Tell the driver to use a precomputed coloring.
  
          Parameters
          ----------
          coloring : str
              A coloring filename.  If no arg is passed, filename will be determined
              automatically.
  
          """
          if self.supports['simultaneous_derivatives']:
              if coloring_mod._force_dyn_coloring and coloring is coloring_mod._STD_COLORING_FNAME:
                  # force the generation of a dynamic coloring this time
                  self._coloring_info['dynamic'] = True
                  self._coloring_info['static'] = None
              else:
                  self._coloring_info['static'] = coloring
                  self._coloring_info['dynamic'] = False
  
              self._coloring_info['coloring'] = None
          else:
              raise RuntimeError("Driver '%s' does not support simultaneous derivatives." %
                                 self._get_name())
  
      def _setup_tot_jac_sparsity(self, coloring=None):
          """
          Set up total jacobian subjac sparsity.
  
          Drivers that can use subjac sparsity should override this.
  
          Parameters
          ----------
          coloring : Coloring or None
              Current coloring.
          """
          pass
  
      def _get_static_coloring(self):
          """
          Get the Coloring for this driver.
  
          If necessary, load the Coloring from a file.
  
          Returns
          -------
          Coloring or None
              The pre-existing or loaded Coloring, or None
          """
          info = self._coloring_info
          static = info['static']
  
          if isinstance(static, coloring_mod.Coloring):
              coloring = static
              info['coloring'] = coloring
          else:
              coloring = info['coloring']
  
          if coloring is not None:
              return coloring
  
          if static is coloring_mod._STD_COLORING_FNAME or isinstance(static, str):
              if static is coloring_mod._STD_COLORING_FNAME:
                  fname = self._get_total_coloring_fname()
              else:
                  fname = static
              print("loading total coloring from file %s" % fname)
              coloring = info['coloring'] = coloring_mod.Coloring.load(fname)
              info.update(coloring._meta)
              return coloring
  
      def _get_total_coloring_fname(self):
          return os.path.join(self._problem().options['coloring_dir'], 'total_coloring.pkl')
  
      def _setup_simul_coloring(self):
          """
          Set up metadata for coloring of total derivative solution.
  
          If set_coloring was called with a filename, load the coloring file.
          """
          # command line simul_coloring uses this env var to turn pre-existing coloring off
          if not coloring_mod._use_total_sparsity:
              return
  
          problem = self._problem()
          if not problem.model._use_derivatives:
              simple_warning("Derivatives are turned off.  Skipping simul deriv coloring.")
              return
  
          total_coloring = self._get_static_coloring()
  
          if total_coloring._rev and problem._orig_mode not in ('rev', 'auto'):
              revcol = total_coloring._rev[0][0]
              if revcol:
                  raise RuntimeError("Simultaneous coloring does reverse solves but mode has "
                                     "been set to '%s'" % problem._orig_mode)
          if total_coloring._fwd and problem._orig_mode not in ('fwd', 'auto'):
              fwdcol = total_coloring._fwd[0][0]
              if fwdcol:
                  raise RuntimeError("Simultaneous coloring does forward solves but mode has "
                                     "been set to '%s'" % problem._orig_mode)
  
      def _pre_run_model_debug_print(self):
          """
          Optionally print some debugging information before the model runs.
          """
          debug_opt = self.options['debug_print']
          rank = self._problem().comm.rank
          if not debug_opt or debug_opt == ['totals']:
              return
  
          if not MPI or rank == 0:
              header = 'Driver debug print for iter coord: {}'.format(
                  self._recording_iter.get_formatted_iteration_coordinate())
              print(header)
              print(len(header) * '-')
  
          if 'desvars' in debug_opt:
              model = self._problem().model
              desvar_vals = {n: model.get_val(n, get_remote=True, rank=0) for n in self._designvars}
              if not MPI or rank == 0:
                  print("Design Vars")
                  if desvar_vals:
                      pprint.pprint(desvar_vals)
                  else:
                      print("None")
                  print()
  
          sys.stdout.flush()
  
      def _post_run_model_debug_print(self):
          """
          Optionally print some debugging information after the model runs.
          """
          rank = self._problem().comm.rank
  
          if 'nl_cons' in self.options['debug_print']:
              cons = self.get_constraint_values(lintype='nonlinear', driver_scaling=False)
              if not MPI or rank == 0:
                  print("Nonlinear constraints")
                  if cons:
                      pprint.pprint(cons)
                  else:
                      print("None")
                  print()
  
          if 'ln_cons' in self.options['debug_print']:
              cons = self.get_constraint_values(lintype='linear', driver_scaling=False)
              if not MPI or rank == 0:
                  print("Linear constraints")
                  if cons:
                      pprint.pprint(cons)
                  else:
                      print("None")
                  print()
  
          if 'objs' in self.options['debug_print']:
              objs = self.get_objective_values(driver_scaling=False)
              if not MPI or rank == 0:
                  print("Objectives")
                  if objs:
                      pprint.pprint(objs)
                  else:
                      print("None")
                  print()
  
          sys.stdout.flush()
  
  
  class RecordingDebugging(Recording):
      """
      A class that acts as a context manager.
  
      Handles doing the case recording and also the Driver
      debugging printing.
      """
  
      def __enter__(self):
          """
          Do things before the code inside the 'with RecordingDebugging' block.
  
          Returns
          -------
          self : object
              self
          """
          super().__enter__()
          self.recording_requester()._pre_run_model_debug_print()
          return self
  
      def __exit__(self, *args):
          """
          Do things after the code inside the 'with RecordingDebugging' block.
  
          Parameters
          ----------
          *args : array
              Solver recording requires extra args.
          """
          self.recording_requester()._post_run_model_debug_print()
          super().__exit__()
  
  
  def record_iteration(requester, prob, case_name):
      """
      Record an iteration of the current Problem or Driver.
  
      Parameters
      ----------
      requester : Problem or Driver
          The recording requester.
      prob : Problem
          The Problem.
      case_name : str
          The name of this case.
      """
      rec_mgr = requester._rec_mgr
      if not rec_mgr._recorders:
          return
  
      # Get the data to record (collective calls that get across all ranks)
      model = prob.model
      parallel = rec_mgr._check_parallel() if model.comm.size > 1 else False
  
      inputs, outputs, residuals = model.get_nonlinear_vectors()
      discrete_inputs = model._discrete_inputs
      discrete_outputs = model._discrete_outputs
  
      opts = requester.recording_options
      data = {'input': {}, 'output': {}, 'residual': {}}
      filt = requester._filtered_vars_to_record
  
      if opts['record_inputs'] and (inputs._names or len(discrete_inputs) > 0):
          data['input'] = model._retrieve_data_of_kind(filt, 'input', 'nonlinear', parallel)
  
      if opts['record_outputs'] and (outputs._names or len(discrete_outputs) > 0):
          data['output'] = model._retrieve_data_of_kind(filt, 'output', 'nonlinear', parallel)
  
      if opts['record_residuals'] and residuals._names:
          data['residual'] = model._retrieve_data_of_kind(filt, 'residual', 'nonlinear', parallel)
  
      from openmdao.core.problem import Problem
      if isinstance(requester, Problem):
          # Record total derivatives
          if opts['record_derivatives'] and prob.driver._designvars and prob.driver._responses:
              data['totals'] = requester.compute_totals(return_format='flat_dict_structured_key')
  
          # Record solver info
          if opts['record_abs_error'] or opts['record_rel_error']:
              norm = residuals.get_norm()
          if opts['record_abs_error']:
              data['abs'] = norm
          if opts['record_rel_error']:
              solver = model.nonlinear_solver
              norm0 = solver._norm0 if solver._norm0 != 0.0 else 1.0  # runonce never sets _norm0
              data['rel'] = norm / norm0
  
      rec_mgr.record_iteration(requester, data, requester._get_recorder_metadata(case_name))
  