from flcore.clients.clientpac import clientPAC
from flcore.servers.serverbase import Server
from utils.data_utils import read_client_data
from threading import Thread
from collections import defaultdict
import time
import numpy as np
import random
import torch
import cvxpy as cvx
import copy


class FedPAC(Server):
    def __init__(self, args, times):
        super().__init__(args, times)

        # select slow clients
        self.set_slow_clients()
        self.set_clients(clientPAC)

        print(f"\nJoin ratio / total clients: {self.join_ratio} / {self.num_clients}")
        print("Finished creating server and clients.")

        # self.load_model()
        self.Budget = []
        self.num_classes = args.num_classes
        self.global_protos = [None for _ in range(args.num_classes)]

        self.Vars = []
        self.Hs = []
        self.uploaded_heads = []


    def train(self):
        for i in range(self.global_rounds+1):
            s_t = time.time()
            self.selected_clients = self.select_clients()
            self.send_models()

            self.Vars = []
            self.Hs = []
            for client in self.selected_clients:
                self.Vars.append(client.V)
                self.Hs.append(client.h)

            if i%self.eval_gap == 0:
                print(f"\n-------------Round number: {i}-------------")
                print("\nEvaluate personalized models")
                self.evaluate()

            for client in self.selected_clients:
                client.train()

            # threads = [Thread(target=client.train)
            #            for client in self.selected_clients]
            # [t.start() for t in threads]
            # [t.join() for t in threads]

            self.receive_protos()
            self.global_protos = proto_aggregation(self.uploaded_protos)
            self.send_protos()

            self.receive_models()
            self.aggregate_parameters()

            self.aggregate_and_send_heads()

            self.Budget.append(time.time() - s_t)
            print('-'*50, self.Budget[-1])

            if self.auto_break and self.check_done(acc_lss=[self.rs_test_acc], top_cnt=self.top_cnt):
                break

        print("\nBest accuracy.")
        # self.print_(max(self.rs_test_acc), max(
        #     self.rs_train_acc), min(self.rs_train_loss))
        print(max(self.rs_test_acc))
        print(sum(self.Budget[1:])/len(self.Budget[1:]))

        self.save_results()
        

    def send_protos(self):
        assert (len(self.clients) > 0)

        for client in self.clients:
            start_time = time.time()

            client.set_protos(self.global_protos)

            client.send_time_cost['num_rounds'] += 1
            client.send_time_cost['total_cost'] += 2 * (time.time() - start_time)

    def receive_protos(self):
        assert (len(self.selected_clients) > 0)

        self.uploaded_ids = []
        self.uploaded_protos = []
        for client in self.selected_clients:
            self.uploaded_ids.append(client.id)
            self.uploaded_protos.append(client.protos)

    def evaluate(self, acc=None, loss=None):
        stats = self.test_metrics()
        # stats_train = self.train_metrics()

        test_acc = sum(stats[2])*1.0 / sum(stats[1])
        # train_loss = sum(stats_train[2])*1.0 / sum(stats_train[1])
        accs = [a / n for a, n in zip(stats[2], stats[1])]
        
        if acc == None:
            self.rs_test_acc.append(test_acc)
        else:
            acc.append(test_acc)
        
        # if loss == None:
        #     self.rs_train_loss.append(train_loss)
        # else:
        #     loss.append(train_loss)

        # print("Averaged Train Loss: {:.4f}".format(train_loss))
        print("Averaged Test Accurancy: {:.4f}".format(test_acc))
        # self.print_(test_acc, train_acc, train_loss)
        print("Std Test Accurancy: {:.4f}".format(np.std(accs)))

    def receive_models(self):
        assert (len(self.selected_clients) > 0)

        active_clients = random.sample(
            self.selected_clients, int((1-self.client_drop_rate) * self.current_num_join_clients))

        self.uploaded_ids = []
        self.uploaded_weights = []
        self.uploaded_models = []
        self.uploaded_heads = []
        tot_samples = 0
        for client in active_clients:
            try:
                client_time_cost = client.train_time_cost['total_cost'] / client.train_time_cost['num_rounds'] + \
                        client.send_time_cost['total_cost'] / client.send_time_cost['num_rounds']
            except ZeroDivisionError:
                client_time_cost = 0
            if client_time_cost <= self.time_threthold:
                tot_samples += client.train_samples
                self.uploaded_ids.append(client.id)
                self.uploaded_weights.append(client.train_samples)
                self.uploaded_models.append(client.model.base)
                self.uploaded_heads.append(client.model.head)
        for i, w in enumerate(self.uploaded_weights):
            self.uploaded_weights[i] = w / tot_samples

    def aggregate_and_send_heads(self):
        head_weights = solve_quadratic(len(self.uploaded_ids), self.Vars, self.Hs)

        for idx, cid in enumerate(self.uploaded_ids):
            print('(Client {}) Weights of Classifier Head'.format(cid))
            print(head_weights[idx],'\n')

            if head_weights[idx] is not None:
                new_head = self.add_heads(head_weights[idx])
            else:
                new_head = self.uploaded_heads[cid]

            self.clients[cid].set_head(new_head)

    def add_heads(self, weights):
        new_head = copy.deepcopy(self.uploaded_heads[0])
        for param in new_head.parameters():
            param.data.zero_()
                    
        for w, head in zip(weights, self.uploaded_heads):
            for server_param, client_param in zip(new_head.parameters(), head.parameters()):
                server_param.data += client_param.data.clone() * w
        return new_head


# https://github.com/yuetan031/fedproto/blob/main/lib/utils.py#L221
def proto_aggregation(local_protos_list):
    agg_protos_label = defaultdict(list)
    for local_protos in local_protos_list:
        for label in local_protos.keys():
            agg_protos_label[label].append(local_protos[label])

    for [label, proto_list] in agg_protos_label.items():
        if len(proto_list) > 1:
            proto = 0 * proto_list[0].data
            for i in proto_list:
                proto += i.data
            agg_protos_label[label] = proto / len(proto_list)
        else:
            agg_protos_label[label] = proto_list[0].data

    return agg_protos_label


# https://github.com/JianXu95/FedPAC/blob/main/tools.py#L94
def solve_quadratic(num_users, Vars, Hs):
    device = Hs[0][0].device
    num_cls = Hs[0].shape[0] # number of classes
    d = Hs[0].shape[1] # dimension of feature representation
    avg_weight = []
    for i in range(num_users):
        # ---------------------------------------------------------------------------
        # variance ter
        v = torch.tensor(Vars, device=device)
        # ---------------------------------------------------------------------------
        # bias term
        h_ref = Hs[i]
        dist = torch.zeros((num_users, num_users), device=device)
        for j1, j2 in pairwise(tuple(range(num_users))):
            h_j1 = Hs[j1]
            h_j2 = Hs[j2]
            h = torch.zeros((d, d), device=device)
            for k in range(num_cls):
                h += torch.mm((h_ref[k]-h_j1[k]).reshape(d,1), (h_ref[k]-h_j2[k]).reshape(1,d))
            dj12 = torch.trace(h)
            dist[j1][j2] = dj12
            dist[j2][j1] = dj12

        # QP solver
        p_matrix = torch.diag(v) + dist
        p_matrix = p_matrix.cpu().numpy()  # coefficient for QP problem
        evals, evecs = torch.linalg.eig(torch.tensor(p_matrix))
        
        # for numerical stablity
        p_matrix_new = 0
        p_matrix_new = 0
        for ii in range(num_users):
            if evals[ii].real >= 0.01:
                p_matrix_new += evals[ii].real*torch.mm(evecs[:,ii].reshape(num_users,1), evecs[:,ii].reshape(1, num_users))
        p_matrix = p_matrix_new.numpy() if not np.all(np.linalg.eigvals(p_matrix)>=0.0) else p_matrix
        
        # solve QP
        alpha = 0
        eps = 1e-3
        if np.all(np.linalg.eigvals(p_matrix)>=0):
            alphav = cvx.Variable(num_users)
            obj = cvx.Minimize(cvx.quad_form(alphav, p_matrix))
            prob = cvx.Problem(obj, [cvx.sum(alphav) == 1.0, alphav >= 0])
            prob.solve()
            alpha = alphav.value
            alpha = [(i)*(i>eps) for i in alpha] # zero-out small weights (<eps)
        else:
            alpha = None # if no solution for the optimization problem, use local classifier only
        
        avg_weight.append(alpha)

    return avg_weight

# https://github.com/JianXu95/FedPAC/blob/main/tools.py#L10
def pairwise(data):
    n = len(data)
    for i in range(n):
        for j in range(i, n):
            yield (data[i], data[j])