text: # This code is part of Qiskit.
  #
  # (C) Copyright IBM 2020.
  #
  # This code is licensed under the Apache License, Version 2.0. You may
  # obtain a copy of this license in the LICENSE.txt file in the root directory
  # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
  #
  # Any modifications or derivative works of this code must retain this
  # copyright notice, and modified files need to carry a notice indicating
  # that they have been altered from the originals.
  
  """An abstract class for optimization algorithms in Qiskit's optimization module."""
  
  from abc import ABC, abstractmethod
  from enum import Enum
  from typing import List, Union, Any, Optional, Dict, Type
  
  import numpy as np
  
  from .. import QiskitOptimizationError
  from ..converters.quadratic_program_to_qubo import (QuadraticProgramToQubo,
                                                      QuadraticProgramConverter)
  from ..problems.quadratic_program import QuadraticProgram, Variable
  
  
  class OptimizationResultStatus(Enum):
      """Termination status of an optimization algorithm."""
  
      SUCCESS = 0
      """the optimization algorithm succeeded to find an optimal solution."""
  
      FAILURE = 1
      """the optimization algorithm ended in a failure."""
  
      INFEASIBLE = 2
      """the optimization algorithm obtained an infeasible solution."""
  
  
  class OptimizationResult:
      """A base class for optimization results.
  
      The optimization algorithms return an object of the type ``OptimizationResult``
      with the information about the solution obtained.
  
      ``OptimizationResult`` allows users to get the value of a variable by specifying an index or
      a name as follows.
  
      Examples:
          >>> from qiskit.optimization import QuadraticProgram
          >>> from qiskit.optimization.algorithms import CplexOptimizer
          >>> problem = QuadraticProgram()
          >>> _ = problem.binary_var('x1')
          >>> _ = problem.binary_var('x2')
          >>> _ = problem.binary_var('x3')
          >>> problem.minimize(linear={'x1': 1, 'x2': -2, 'x3': 3})
          >>> print([var.name for var in problem.variables])
          ['x1', 'x2', 'x3']
          >>> optimizer = CplexOptimizer()
          >>> result = optimizer.solve(problem)
          >>> print(result.variable_names)
          ['x1', 'x2', 'x3']
          >>> print(result.x)
          [0. 1. 0.]
          >>> print(result[1])
          1.0
          >>> print(result['x1'])
          0.0
          >>> print(result.fval)
          -2.0
          >>> print(result.variables_dict)
          {'x1': 0.0, 'x2': 1.0, 'x3': 0.0}
  
      Note:
          The order of variables should be equal to that of the problem solved by
          optimization algorithms. Optimization algorithms and converters of ``QuadraticProgram``
          should maintain the order when generating a new ``OptimizationResult`` object.
      """
  
      def __init__(self, x: Optional[Union[List[float], np.ndarray]], fval: float,
                   variables: List[Variable],
                   status: OptimizationResultStatus,
                   raw_results: Optional[Any] = None) -> None:
          """
          Args:
              x: the optimal value found in the optimization, or possibly None in case of FAILURE.
              fval: the optimal function value.
              variables: the list of variables of the optimization problem.
              raw_results: the original results object from the optimization algorithm.
              status: the termination status of the optimization algorithm.
  
          Raises:
              QiskitOptimizationError: if sizes of ``x`` and ``variables`` do not match.
          """
          self._variables = variables
          self._variable_names = [var.name for var in self._variables]
          if x is None:
              # if no state is given, it is set to None
              self._x = None  # pylint: disable=invalid-name
              self._variables_dict = None
          else:
              if len(x) != len(variables):
                  raise QiskitOptimizationError(
                      'Inconsistent size of optimal value and variables. x: size {} {}, '
                      'variables: size {} {}'.format(len(x), x, len(variables),
                                                     [v.name for v in variables]))
              self._x = x if isinstance(x, np.ndarray) else np.array(
                  x)  # pylint: disable=invalid-name
              self._variables_dict = dict(zip(self._variable_names, self._x))
  
          self._fval = fval
          self._raw_results = raw_results
          self._status = status
  
      def __repr__(self) -> str:
          return 'optimal function value: {}\n' \
                 'optimal value: {}\n' \
                 'status: {}'.format(self._fval, self._x, self._status.name)
  
      def __getitem__(self, key: Union[int, str]) -> float:
          """Returns the value of the variable whose index or name is equal to ``key``.
  
          The key can be an integer or a string.
          If the key is an integer, this methods returns the value of the variable
          whose index is equal to ``key``.
          If the key is a string, this methods return the value of the variable
          whose name is equal to ``key``.
  
          Args:
              key: an integer or a string.
  
          Returns:
              The value of a variable whose index or name is equal to ``key``.
  
          Raises:
              IndexError: if ``key`` is an integer and is out of range of the variables.
              KeyError: if ``key`` is a string and none of the variables has ``key`` as name.
              TypeError: if ``key`` is neither an integer nor a string.
          """
          if isinstance(key, int):
              return self._x[key]
          if isinstance(key, str):
              return self._variables_dict[key]
          raise TypeError(
              "Integer or string key required,"
              "instead {}({}) provided.".format(type(key), key))
  
      @property
      def x(self) -> Optional[np.ndarray]:
          """Returns the optimal value found in the optimization or None in case of FAILURE.
  
          Returns:
              The optimal value found in the optimization.
          """
          return self._x
  
      @property
      def fval(self) -> float:
          """Returns the optimal function value.
  
          Returns:
              The function value corresponding to the optimal value found in the optimization.
          """
          return self._fval
  
      @property
      def raw_results(self) -> Any:
          """Return the original results object from the optimization algorithm.
  
          Currently a dump for any leftovers.
  
          Returns:
              Additional result information of the optimization algorithm.
          """
          return self._raw_results
  
      @property
      def status(self) -> OptimizationResultStatus:
          """Returns the termination status of the optimization algorithm.
  
          Returns:
              The termination status of the algorithm.
          """
          return self._status
  
      @property
      def variables(self) -> List[Variable]:
          """Returns the list of variables of the optimization problem.
  
          Returns:
              The list of variables.
          """
          return self._variables
  
      @property
      def variables_dict(self) -> Dict[str, float]:
          """Returns the optimal value as a dictionary of the variable name and corresponding value.
  
          Returns:
              The optimal value as a dictionary of the variable name and corresponding value.
          """
          return self._variables_dict
  
      @property
      def variable_names(self) -> List[str]:
          """Returns the list of variable names of the optimization problem.
  
          Returns:
              The list of variable names of the optimization problem.
          """
          return self._variable_names
  
  
  class OptimizationAlgorithm(ABC):
      """An abstract class for optimization algorithms in Qiskit's optimization module."""
  
      @abstractmethod
      def get_compatibility_msg(self, problem: QuadraticProgram) -> str:
          """Checks whether a given problem can be solved with the optimizer implementing this method.
  
          Args:
              problem: The optimization problem to check compatibility.
  
          Returns:
              Returns the incompatibility message. If the message is empty no issues were found.
          """
  
      def is_compatible(self, problem: QuadraticProgram) -> bool:
          """Checks whether a given problem can be solved with the optimizer implementing this method.
  
          Args:
              problem: The optimization problem to check compatibility.
  
          Returns:
              Returns True if the problem is compatible, False otherwise.
          """
          return len(self.get_compatibility_msg(problem)) == 0
  
      @abstractmethod
      def solve(self, problem: QuadraticProgram) -> 'OptimizationResult':
          """Tries to solves the given problem using the optimizer.
  
          Runs the optimizer to try to solve the optimization problem.
  
          Args:
              problem: The problem to be solved.
  
          Returns:
              The result of the optimizer applied to the problem.
  
          Raises:
              QiskitOptimizationError: If the problem is incompatible with the optimizer.
          """
          raise NotImplementedError
  
      def _verify_compatibility(self, problem: QuadraticProgram) -> None:
          """Verifies that the problem is suitable for this optimizer. If the problem is not
          compatible then an exception is raised. This method is for convenience for concrete
          optimizers and is not intended to be used by end user.
  
          Args:
              problem: Problem to verify.
  
          Returns:
              None
  
          Raises:
              QiskitOptimizationError: If the problem is incompatible with the optimizer.
  
          """
          # check compatibility and raise exception if incompatible
          msg = self.get_compatibility_msg(problem)
          if msg:
              raise QiskitOptimizationError('Incompatible problem: {}'.format(msg))
  
      @staticmethod
      def _get_feasibility_status(problem: QuadraticProgram,
                                  x: Union[List[float], np.ndarray]) -> OptimizationResultStatus:
          """Returns whether the input result is feasible or not for the given problem.
  
          Args:
              problem: Problem to verify.
              x: the input result list.
  
          Returns:
              The status of the result.
          """
          is_feasible = problem.is_feasible(x)
  
          return OptimizationResultStatus.SUCCESS if is_feasible \
              else OptimizationResultStatus.INFEASIBLE
  
      @staticmethod
      def _prepare_converters(converters: Optional[Union[QuadraticProgramConverter,
                                                         List[QuadraticProgramConverter]]],
                              penalty: Optional[float] = None) -> List[QuadraticProgramConverter]:
          """Prepare a list of converters from the input.
  
          Args:
              converters: The converters to use for converting a problem into a different form.
                  By default, when None is specified, an internally created instance of
                  :class:`~qiskit.optimization.converters.QuadraticProgramToQubo` will be used.
              penalty: The penalty factor used in the default
                  :class:`~qiskit.optimization.converters.QuadraticProgramToQubo` converter
  
          Returns:
              The list of converters.
  
          Raises:
              TypeError: When the converters include those that are not
              :class:`~qiskit.optimization.converters.QuadraticProgramConverter type.
          """
          if converters is None:
              return [QuadraticProgramToQubo(penalty=penalty)]
          elif isinstance(converters, QuadraticProgramConverter):
              return [converters]
          elif isinstance(converters, list) and \
                  all(isinstance(converter, QuadraticProgramConverter) for converter in converters):
              return converters
          else:
              raise TypeError('`converters` must all be of the QuadraticProgramConverter type')
  
      @staticmethod
      def _convert(problem: QuadraticProgram,
                   converters: Union[QuadraticProgramConverter,
                                     List[QuadraticProgramConverter]]) -> QuadraticProgram:
          """Convert the problem with the converters
  
          Args:
              problem: The problem to be solved
              converters: The converters to use for converting a problem into a different form.
  
          Returns:
              The problem converted by the converters.
          """
          problem_ = problem
  
          if not isinstance(converters, list):
              converters = [converters]
  
          for converter in converters:
              problem_ = converter.convert(problem_)
  
          return problem_
  
      @classmethod
      def _interpret(cls, x: np.ndarray,
                     problem: QuadraticProgram,
                     converters: Optional[Union[QuadraticProgramConverter,
                                                List[QuadraticProgramConverter]]] = None,
                     result_class: Type[OptimizationResult] = OptimizationResult,
                     **kwargs) -> OptimizationResult:
          """Convert back the result of the converted problem to the result of the original problem.
  
          Args:
              x: The result of the converted problem.
              converters: The converters to use for converting back the result of the problem
                  to the result of the original problem.
              problem: The original problem for which `x` is interpreted.
              result_class: The class of the result object.
              kwargs: parameters of the constructor of result_class
  
          Returns:
              The result of the original problem.
  
          Raises:
              QiskitOptimizationError: if result_class is not a sub-class of OptimizationResult.
              TypeError: if converters are not QuadraticProgramConverter or a list of
                  QuadraticProgramConverter.
          """
          if not issubclass(result_class, OptimizationResult):
              raise QiskitOptimizationError(
                  'Invalid result class, not derived from OptimizationResult: '
                  '{}'.format(result_class))
          if converters is None:
              converters = []
          if not isinstance(converters, list):
              converters = [converters]
          if not all(isinstance(conv, QuadraticProgramConverter) for conv in converters):
              raise TypeError('Invalid object of converters: {}'.format(converters))
  
          for converter in converters[::-1]:
              x = converter.interpret(x)
          return result_class(x=x, fval=problem.objective.evaluate(x),
                              variables=problem.variables,
                              status=cls._get_feasibility_status(problem, x),
                              **kwargs)
  