import os
import clip
import torch.nn as nn
from datasets import Action_DATASETS
from torch.utils.data import DataLoader
from tqdm import tqdm
import wandb
import argparse
import shutil
from pathlib import Path
import yaml
from dotmap import DotMap
import pprint
import numpy
from modules.Visual_Prompt import visual_prompt
from utils.Augmentation import get_augmentation
import torch
from utils.Text_Prompt import *
import pdb
from sklearn.metrics import f1_score
from sklearn.metrics import balanced_accuracy_score
import pandas as pd
import numpy as np
import logging


class TextCLIP(nn.Module):
    def __init__(self, model):
        super(TextCLIP, self).__init__()
        self.model = model

    def forward(self, text):
        return self.model.encode_text(text)


class ImageCLIP(nn.Module):
    def __init__(self, model):
        super(ImageCLIP, self).__init__()
        self.model = model

    def forward(self, image):
        return self.model.encode_image(image)


def val_metrics(pred, logger):
    # pdb.set_trace()
    test_num_each = [5464, 5373, 27014, 4239, 3936, 6258, 10474, 6273,
                     10512, 6667, 22131, 4661, 8855, 14047, 28896, 4209]
    test_num_snippet = [43, 42, 212, 34, 31, 49, 82, 50, 83, 53, 173, 37, 70, 110, 226, 33]
    # test_num_rem = [88, 125, 6, 15, 96, 114, 106, 1, 16, 11, 115, 53, 23, 95, 96, 113]

    mean_weighted_f1 = 0.0
    mean_unweighted_f1 = 0.0
    mean_global_f1 = 0.0
    mean_balanced_acc = 0.0
    each_wf1 = []
    each_unf1 = []
    each_gf1 = []
    each_bacc = []

    test_labels_pth = ''

    for i in range(16):
        predi = pred[sum(test_num_snippet[:i]): sum(test_num_snippet[:i+1])]
        predi = [p for p in predi for _ in range(128)]
        predi = predi[:test_num_each[i]]

        tl_pth = test_labels_pth + '/test_video_' + str(i).zfill(4) + '.csv'
        ls = np.array(pd.read_csv(tl_pth, usecols=['frame_label']))

        label = []
        predict = []
        for idx, l in enumerate(ls):
            if not np.isnan(l):
                label.append(int(l))
                predict.append(predi[idx])

        # pdb.set_trace()
        mean_weighted_f1 += f1_score(label, predict, average='weighted')/16.0
        mean_unweighted_f1 += f1_score(label, predict, average='macro') / 16.0
        mean_global_f1 += f1_score(label, predict, average='micro') / 16.0
        mean_balanced_acc += balanced_accuracy_score(label, predict) / 16.0

        each_wf1.append(f1_score(label, predict, average='weighted'))
        each_unf1.append(f1_score(label, predict, average='macro'))
        each_gf1.append(f1_score(label, predict, average='micro'))
        each_bacc.append(balanced_accuracy_score(label, predict))
        # print('video: ', i, 'label: ', label, 'predict: ', predict)

    logger.info('wf1: {}'.format(each_wf1))
    logger.info('unf1:{}'.format(each_unf1))
    logger.info('gf1:{}'.format(each_gf1))
    logger.info('bacc:{}'.format(each_bacc))


    return mean_weighted_f1, mean_unweighted_f1, mean_global_f1, mean_balanced_acc


def validate_val(epoch, val_loader, classes, device, model, fusion_model, config, num_text_aug):
    model.eval()
    fusion_model.eval()
    num = 0
    corr_1 = 0
    corr_5 = 0

    predict_list = []
    label_list = []
    label2 = []
    pred2 = []

    with torch.no_grad():
        text_inputs = classes.to(device)
        text_features = model.encode_text(text_inputs)  # (bs*num_classes, 512)
        for iii, (image, class_id) in enumerate(tqdm(val_loader)):
            # image: (bs, 24, 224, 224)
            image = image.view((-1, config.data.num_segments, 3) + image.size()[-2:])
            # image: (16, 8, 3, 224, 224)
            b, t, c, h, w = image.size()
            class_id = class_id.to(device)
            image_input = image.to(device).view(-1, c, h, w)
            image_features = model.encode_image(image_input).view(b, t, -1)
            image_features = fusion_model(image_features)  # (bs, 512)
            image_features /= image_features.norm(dim=-1, keepdim=True)
            text_features /= text_features.norm(dim=-1, keepdim=True)
            similarity = (100.0 * image_features @ text_features.T)
            similarity = similarity.view(b, num_text_aug, -1)
            # pdb.set_trace()

            similarity = similarity.softmax(dim=-1)
            similarity = similarity.mean(dim=1, keepdim=False)
            values_1, indices_1 = similarity.topk(1, dim=-1)
            # values_5, indices_5 = similarity.topk(5, dim=-1)
            num += b

            # print(indices_1)
            # print(class_id)
            # pdb.set_trace()

            for i in range(b):
                if values_1[i] < 0.5:
                    indices_1[i] = -1

                # pdb.set_trace()

                label_list.append(int(class_id[i].cpu().numpy()))
                predict_list.append(indices_1[i].cpu().numpy()[0])

                # if indices_1[i] == class_id[i]:
                #     corr_1 += 1
                # if class_id[i] in indices_5[i]:
                #     corr_5 += 1
            # pdb.set_trace()

    # f1score = f1_score(label2, pred2, average='weighted')
    # acc = accuracy_score(label2, pred2)
    # pdb.set_trace()
    bacc = balanced_accuracy_score(label_list, predict_list)
    print('Epoch: [{}/{}]: bacc:{}'.format(epoch, config.solver.epochs, bacc))
    return bacc


def validate(epoch, val_loader, classes, device, model, fusion_model, config, num_text_aug, logger):
    model.eval()
    fusion_model.eval()
    num = 0
    corr_1 = 0
    corr_5 = 0

    predict_list = []
    label_list = []
    label2 = []
    pred2 = []




    with torch.no_grad():
        text_inputs = classes.to(device)
        text_features = model.encode_text(text_inputs)  # (bs*num_classes, 512)
        for iii, (image, class_id) in enumerate(tqdm(val_loader)):
            # image: (bs, 24, 224, 224)
            image = image.view((-1, config.data.num_segments, 3) + image.size()[-2:])
            # image: (16, 8, 3, 224, 224)
            b, t, c, h, w = image.size()
            class_id = class_id.to(device)
            image_input = image.to(device).view(-1, c, h, w)
            image_features = model.encode_image(image_input).view(b, t, -1)
            image_features = fusion_model(image_features)  # (bs, 512)
            image_features /= image_features.norm(dim=-1, keepdim=True)
            text_features /= text_features.norm(dim=-1, keepdim=True)
            similarity = (100.0 * image_features @ text_features.T)
            similarity = similarity.view(b, num_text_aug, -1)
            # pdb.set_trace()

            similarity = similarity.softmax(dim=-1)
            similarity = similarity.mean(dim=1, keepdim=False)
            values_1, indices_1 = similarity.topk(1, dim=-1)
            # values_5, indices_5 = similarity.topk(5, dim=-1)
            num += b

            # print(indices_1)
            # print(class_id)
            # pdb.set_trace()

            for i in range(b):
                # if values_1[i] < 0.5:
                #     indices_1[i] = -1

                # pdb.set_trace()

                # label_list.append(int(class_id[i].cpu().numpy()))
                predict_list.append(indices_1[i].cpu().numpy()[0])

                # if indices_1[i] == class_id[i]:
                #     corr_1 += 1
                # if class_id[i] in indices_5[i]:
                #     corr_5 += 1
    # pdb.set_trace()

    # f1score = f1_score(label2, pred2, average='weighted')
    # acc = accuracy_score(label2, pred2)

    wf1, unf1, gf1, bacc  = val_metrics(predict_list, logger)
    # top1 = f1score
    # top5 = float(corr_5) / num * 100
    # wandb.log({"top1": top1})
    # wandb.log({"top5": top5})
    # print('Epoch: [{}/{}]: Top1: {}, Top5: {}'.format(epoch, config.solver.epochs, top1, top5))
    logger.info('Epoch: [{}/{}]: wf1:{:.3f} unf1:{:.3f} gf1:{:.3f} bacc:{:.3f}'.format(epoch, config.solver.epochs, wf1, unf1, gf1, bacc))
    return wf1


def main():
    global args, best_prec1
    global global_step
    parser = argparse.ArgumentParser()
    parser.add_argument('--config', '-cfg', default='')
    parser.add_argument('--log_time', default='')
    args = parser.parse_args()

    with open(args.config, 'r') as f:
        config = yaml.load(f)
    working_dir = os.path.join('./exp', config['network']['type'], config['network']['arch'], config['data']['dataset'],
                               args.log_time)
    wandb.init(project=config['network']['type'],
               name='{}_{}_{}_{}'.format(args.log_time, config['network']['type'], config['network']['arch'],
                                         config['data']['dataset']))
    print('-' * 80)
    print(' ' * 20, "working dir: {}".format(working_dir))
    print('-' * 80)

    print('-' * 80)
    print(' ' * 30, "Config")
    pp = pprint.PrettyPrinter(indent=4)
    pp.pprint(config)
    print('-' * 80)

    config = DotMap(config)

    Path(working_dir).mkdir(parents=True, exist_ok=True)
    shutil.copy(args.config, working_dir)
    shutil.copy('test.py', working_dir)

    device = "cuda" if torch.cuda.is_available() else "cpu"  # If using GPU then use mixed precision training.

    model, clip_state_dict = clip.load(config.network.arch, device=device, jit=False, tsm=config.network.tsm,
                                                   T=config.data.num_segments, dropout=config.network.drop_out,
                                                   emb_dropout=config.network.emb_dropout)  # Must set jit=False for training  ViT-B/32

    transform_val = get_augmentation(False, config)

    fusion_model = visual_prompt(config.network.sim_header, clip_state_dict, config.data.num_segments)

    model_text = TextCLIP(model)
    model_image = ImageCLIP(model)

    model_text = torch.nn.DataParallel(model_text).cuda()
    model_image = torch.nn.DataParallel(model_image).cuda()
    fusion_model = torch.nn.DataParallel(fusion_model).cuda()
    wandb.watch(model)
    wandb.watch(fusion_model)

    val_data = Action_DATASETS(config.data.val_list, config.data.label_list, num_segments=config.data.num_segments,
                        image_tmpl=config.data.image_tmpl,
                        transform=transform_val, random_shift=config.random_shift)
    val_loader = DataLoader(val_data, batch_size=config.data.batch_size, num_workers=config.data.workers, shuffle=False,
                            pin_memory=True, drop_last=True)

    if device == "cpu":
        model_text.float()
        model_image.float()
    else:
        clip.model.convert_weights(
            model_text)  # Actually this line is unnecessary since clip by default already on float16
        clip.model.convert_weights(model_image)

    start_epoch = config.solver.start_epoch

    if config.pretrain:
        if os.path.isfile(config.pretrain):
            print(("=> loading checkpoint '{}'".format(config.pretrain)))
            checkpoint = torch.load(config.pretrain)
            model.load_state_dict(checkpoint['model_state_dict'])
            fusion_model.load_state_dict(checkpoint['fusion_model_state_dict'])
            del checkpoint
        else:
            print(("=> no checkpoint found at '{}'".format(config.pretrain)))

    classes, num_text_aug, text_dict = text_prompt(val_data)

    best_prec1 = 0.0
    prec1 = validate(start_epoch, val_loader, classes, device, model, fusion_model, config, num_text_aug)

if __name__ == '__main__':
    main()
