# Copyright 2021 Jij Inc.

# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at

#     http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This module defines the abstract sampler (BaseSampler).
"""

import time

import dimod
from dimod import SPIN, BINARY, Vartype
import numpy as np

from cimod.utils import get_state_and_energy
from dimod.core.sampler import samplemixinmethod

import openjij as oj
import openjij 
import openjij.cxxjij as cxxjij


def measure_time(func):
    """Decorator for measuring calculation time.

    Args:
        func: decorator function
    """

    def wrapper(*args, **kargs):
        start_time = time.perf_counter()

        func(*args, **kargs)

        end_time = time.perf_counter()
        execution_time = end_time - start_time
        return execution_time

    return wrapper


class BaseSampler(dimod.Sampler):
    """Base sampler class of python wrapper for cxxjij simulator"""

    parameters = dict()
    properties = dict()

    def _set_params(self, **kwargs):
        for key, value in kwargs.items():
            if key not in self._default_params:
                raise ValueError("Unknown parameters detected")
            if value is None:
                self._params[key] = self._default_params[key]
            else:
                self._params[key] = value

    def _sampling(self, **kwargs):
        pass

    def _cxxjij_sampling(
        self,
        model,
        init_generator,
        algorithm,
        system,
        reinitialize_state=None,
        seed=None,
        offset=None,
    ):
        """Basic sampling function: for cxxjij sampling

        Args:
            model (openjij.BinaryQuadraticModel): model has a information of instaunce (h, J, Q)
            init_generator (callable): return initial state, must have argument structure
            algorithm (callable): system algorithm of cxxjij
            system (:obj:): [description]
            reinitialize_state (bool, optional): [description]. Defaults to None.
            seed (int, optional): seed for algorithm. Defaults to None.
            offset (float): an offset which is added to the calculated energy

        Returns:
            :class:`openjij.sampler.response.Response`: results
        """

        if offset is None:
            offset = 0

        # set algorithm function and set random seed ----
        if seed is None:

            def sampling_algorithm(system):
                return algorithm(system, self._params["schedule"])

        else:

            def sampling_algorithm(system):
                return algorithm(system, seed, self._params["schedule"])

        # ---- set algorithm function and set random seed

        # setting of response class
        execution_time = []

        # define sampling execution function ---------------
        states, energies = [], []
        system_info = {"system": []}

        @measure_time
        def exec_sampling():
            for _ in range(self._params["num_reads"]):
                # Re-initialize at each sampling
                # In reverse annealing,
                # user can use previous result (if re-initilize is set to False)
                if reinitialize_state:
                    system.reset_spins(init_generator())
                # Run sampling algorithm
                # and measure execution time
                _exec_time = measure_time(sampling_algorithm)(system)
                execution_time.append(_exec_time)

                # get Ising result (state and system information)
                # ex. _sys_info save trotterized quantum state.
                result_state, _sys_info = self._get_result(system, model)

                # convert result_state to cimod style
                result_state, energy = get_state_and_energy(model, result_state, offset)

                # store result (state and energy)
                states.append(result_state)
                energies.append(energy)

                if _sys_info:
                    system_info["system"].append(_sys_info)

        # --------------- define sampling execution function

        # Execute sampling function
        sampling_time = exec_sampling()

        # construct response instance
        response = oj.sampler.response.Response.from_samples(
            states, model.vartype, energies, info=system_info
        )

        # save execution time
        response.info["sampling_time"] = sampling_time * 10**6  # micro sec
        response.info["execution_time"] = np.mean(execution_time) * 10**6  # micro sec
        response.info["list_exec_times"] = (
            np.array(execution_time) * 10**6
        )  # micro sec

        return response

    def _get_result(self, system, model):
        result = cxxjij.result.get_solution(system)
        sys_info = {}
        return result, sys_info

    @samplemixinmethod
    def sample(self, bqm, **parameters):
        """Sample from a binary quadratic model.
        Args:
            bqm (openjij.BinaryQuadraticModel):
                Binary Qudratic Model
            **parameters:
                See the implemented sampling for additional keyword definitions.

        Returns:
            :class:`openjij.sampler.response.Response`: results
        """
        if bqm.vartype == SPIN:
            if not getattr(self.sample_ising, "__issamplemixin__", False):
                # sample_ising is implemented
                h, J, offset = bqm.to_ising()
                sampleset = self.sample_ising(h, J, **parameters)
                sampleset.record.energy += offset
                return sampleset
            else:
                Q, offset = bqm.to_qubo()
                sampleset = self.sample_qubo(Q, **parameters)
                sampleset.change_vartype(dimod.SPIN, energy_offset=offset)
                return sampleset
        elif bqm.vartype == BINARY:
            if not getattr(self.sample_qubo, "__issamplemixin__", False):
                # sample_qubo is implemented
                Q, offset = bqm.to_qubo()
                sampleset = self.sample_qubo(Q, **parameters)
                sampleset.record.energy += offset
                return sampleset
            else:
                h, J, offset = bqm.to_ising()
                sampleset = self.sample_ising(h, J, **parameters)
                sampleset.change_vartype(dimod.BINARY, energy_offset=offset)
                return sampleset
        else:
            raise RuntimeError("binary quadratic model has an unknown vartype")

    @samplemixinmethod
    def sample_ising(self, h, J, **parameters):
        """Sample from an Ising model using the implemented sample method.

        Args:
            h (dict): Linear biases
            J (dict): Quadratic biases

        Returns:
            :class:`openjij.sampler.response.Response`: results
        """
        bqm = oj.model.model.BinaryQuadraticModel.from_ising(
            h, J, sparse=parameters.get("sparse", False)
        )
        return self.sample(bqm, **parameters)

    @samplemixinmethod
    def sample_qubo(self, Q, **parameters):
        """Sample from a QUBO model using the implemented sample method.

        Args:
            Q (dict or numpy.ndarray): Coefficients of a quadratic unconstrained binary optimization

        Returns:
            :class:`openjij.sampler.response.Response`: results
        """
        if isinstance(Q, dict):
            bqm = oj.model.model.BinaryQuadraticModel.from_qubo(
                Q, sparse=parameters.get("sparse", False)
            )
            return self.sample(bqm, **parameters)
        elif isinstance(Q, np.ndarray):
            bqm = oj.model.model.BinaryQuadraticModel.from_numpy_matrix(Q, vartype="BINARY")
            return self.sample(bqm, **parameters)
        else:
            raise TypeError("Q must be either dict or np.ndarray")
