import argparse
import json
import matplotlib.pyplot as plt
import math
import numpy as np
import os

from scipy import io
from scipy import stats
from tqdm import tqdm 
import math
import PIL.Image as Img
import uuid

def show_img(image):
    image = np.multiply(image, 255)
    image = image.reshape(int(math.sqrt(len(image))), int(math.sqrt(len(image))))
    image = Img.fromarray(image)
    image = image.convert('L')
    image.save('outfile.png')
    
def gen_data_dict(data_dict, raw_data, group_name):
    data_dict[group_name] = []
    for _, value in raw_data.items():
        for img_data, img_label in zip(value['x'], value['y']):
            data_dict[group_name].append({
                'image': np.multiply(img_data, 255),
                'label': img_label
            })

def construct_data_dirs_clientwise(raw_data, group_name, data_dir, data_type='train'):
    data_group_dir = os.path.join(data_dir, 'huggingface', 'clientwise', group_name, data_type)
    if not os.path.exists(data_group_dir):
        os.makedirs(data_group_dir)

    for _, value in raw_data.items():
        for img_data, img_label in zip(value['x'], value['y']):
            sample_dir = os.path.join(data_group_dir, str(img_label))
            if not os.path.exists(sample_dir):
                os.makedirs(sample_dir)
            sampel_path = os.path.join(sample_dir, str(uuid.uuid1())[:8]+'.png')

            image = np.multiply(img_data, 255)
            image = image.reshape(int(math.sqrt(len(image))), int(math.sqrt(len(image))))
            image = Img.fromarray(image)
            image = image.convert('L')
            image.save(sampel_path)

def construct_data_dirs(raw_data, data_dir, data_type='train'):
    data_group_dir = os.path.join(data_dir, 'huggingface', 'centralized', data_type)
    if not os.path.exists(data_group_dir):
        os.makedirs(data_group_dir)

    for _, value in raw_data.items():
        for img_data, img_label in zip(value['x'], value['y']):
            sample_dir = os.path.join(data_group_dir, str(img_label))
            if not os.path.exists(sample_dir):
                os.makedirs(sample_dir)
            sampel_path = os.path.join(sample_dir, str(uuid.uuid1())[:8]+'.png')

            image = np.multiply(img_data, 255)
            image = image.reshape(int(math.sqrt(len(image))), int(math.sqrt(len(image))))
            image = Img.fromarray(image)
            image = image.convert('L')
            image.save(sampel_path)

def load_data(name):
    train_users = []
    train_num_samples = []
    train_data = {}

    test_users = []
    test_num_samples = []
    test_data = {}
    
    parent_path = os.path.dirname(os.path.realpath(__file__))
    data_dir = os.path.join(parent_path, 'dataset', name)
    train_subdir = os.path.join(data_dir, 'train')
    test_subdir = os.path.join(data_dir, 'test')

    # load train
    train_files = os.listdir(train_subdir)
    train_files = [f for f in train_files if f.endswith('.json')]

    for index, f in tqdm(enumerate(train_files), desc='Training Data Generating', total=len(train_files)):
        group_name = 'client_' + str(index)
        file_dir = os.path.join(train_subdir, f)

        with open(file_dir) as inf:
            data = json.load(inf)

        # show image and print label
        # show_img(data['user_data']['f3242_19']['x'][0])
        # print("Label: " + str(data['user_data']['f3242_19']['y'][0]))

        # train_users.extend(data['users'])
        train_num_samples.extend([sum(data['num_samples'])])
        gen_data_dict(train_data, data['user_data'], group_name)
        construct_data_dirs_clientwise(data['user_data'], group_name, data_dir, data_type='train')
        construct_data_dirs(data['user_data'], data_dir, data_type='train')

    # load test
    test_files = os.listdir(test_subdir)
    test_files = [f for f in test_files if f.endswith('.json')]

    for index, f in tqdm(enumerate(test_files), desc='Testing Data Generating', total=len(test_files)):
        group_name = 'client_' + str(index)
        file_dir = os.path.join(test_subdir, f)

        with open(file_dir) as inf:
            data = json.load(inf)

        test_users.extend(data['users'])
        test_num_samples.extend([sum(data['num_samples'])])
        gen_data_dict(test_data, data['user_data'], group_name)
        construct_data_dirs_clientwise(data['user_data'], group_name, data_dir, data_type='test')
        construct_data_dirs(data['user_data'], data_dir, data_type='test')

    return train_num_samples, train_data, test_num_samples, test_data

if __name__ == '__main__':
    name = 'femnist-small'
    train_num_samples, train_data, \
    test_num_samples, test_data = load_data(name)

    print('####################################')
    print('DATASET: %s' % name)
    print('%d train samples (total)' % np.sum(train_num_samples))
    print('%d test samples (total)' % np.sum(test_num_samples))
    print('%.2f train samples per user (mean)' % np.mean(train_num_samples))
    print('%.2f test samples per user (mean)' % np.mean(test_num_samples))