from __future__ import print_function
from PIL import Image
import os
import os.path
import numpy as np
import sys
from .util import noisify
import pickle
from .torchvisiondataset import VisionDataset
from .torchvisiondatasetsutils import check_integrity, download_and_extract_archive
import torch


__all__ =["CIFAR10_noise","CIFAR100_noise"]


class CIFAR10_noise(VisionDataset):
    """`CIFAR10 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset.

    Args:
        root (string): Root directory of dataset where directory
            ``cifar-10-batches-py`` exists or will be saved to if download is set to True.
        train (bool, optional): If True, creates dataset from training set, otherwise
            creates from test set.
        transform (callable, optional): A function/transform that takes in an PIL image
            and returns a transformed version. E.g, ``transforms.RandomCrop``
        target_transform (callable, optional): A function/transform that takes in the
            target and transforms it.
        download (bool, optional): If true, downloads the dataset from the internet and
            puts it in root directory. If dataset is already downloaded, it is not
            downloaded again.

    """
    base_folder = 'cifar-10-batches-py'
    url = "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
    filename = "cifar-10-python.tar.gz"
    tgz_md5 = 'c58f30108f718f92721af3b95e74349a'
    train_list = [
        ['data_batch_1', 'c99cafc152244af753f735de768cd75f'],
        ['data_batch_2', 'd4bba439e000b95fd0a9bffe97cbabec'],
        ['data_batch_3', '54ebc095f3ab1f0389bbae665268c751'],
        ['data_batch_4', '634d18415352ddfa80567beed471001a'],
        ['data_batch_5', '482c414d41f54cd18b22e5b47cb7c3cb'],
    ]

    test_list = [
        ['test_batch', '40351d587109b95175f43aff81a1287e'],
    ]
    meta = {
        'filename': 'batches.meta',
        'key': 'label_names',
        'md5': '5ff9c542aee3614f3951f8cda6e48888',
    }

    def __init__(
        self, 
        root, 
        train=True, 
        transform=None, 
        transform_eval = None,
        target_transform = None, 
        transform_strong = None, 
        add_noise= True, 
        flip_rate_fixed = None, 
        noise_type = '', 
        random_state = 1, 
        download=True):

        super(CIFAR10_noise, self).__init__(root, transform=transform,
                                      target_transform=target_transform)

        self.use_train = train  # training set or test set
        self.t_matrix = None
        self.apply_transform_eval = False
        self.transform_eval = transform_eval     
        self.transform_strong = transform_strong
        if download:
            self.download()

        if not self._check_integrity():
            raise RuntimeError('Dataset not found or corrupted.' +
                               ' You can use download=True to download it')

        if self.use_train:
            downloaded_list = self.train_list
        else:
            downloaded_list = self.test_list

        self.data = []
        self.targets = []
        self.clean_targets = []

        # now load the picked numpy arrays
        for file_name, checksum in downloaded_list:
            file_path = os.path.join(self.root, self.base_folder, file_name)
            with open(file_path, 'rb') as f:
                entry = pickle.load(f, encoding='latin1')
                self.data.append(entry['data'])
                if 'labels' in entry:
                    self.targets.extend(entry['labels'])
                else:
                    self.targets.extend(entry['fine_labels'])

        # copy clean targets    
        self.data = np.array(self.data)
        self.data = self.data.reshape(-1,3072)
        self.targets = np.array(self.targets)
        self.clean_targets =self.targets.copy()
        self.is_confident = np.zeros(len(self.clean_targets))

        if add_noise:
            noisy_targets, self.actual_noise_rate, self.t_matrix = noisify(
                dataset=zip(torch.from_numpy(self.data).float(),  torch.from_numpy(self.targets)),
                train_labels=self.targets, 
                noise_type=noise_type, 
                noise_rate=flip_rate_fixed, 
                random_state=random_state,
                nb_classes=self._get_num_classes(),
                feature_size=32*32*3
            )
            noisy_targets = noisy_targets.squeeze()
 
            self._set_targets(noisy_targets)
        self.hat_clean_targets = self.targets.copy()

        self.data = np.vstack(self.data).reshape(-1, 3, 32, 32)
        self.data = self.data.transpose((0, 2, 3, 1))  # convert to HWC

        self._load_meta()

    def _load_meta(self):
        path = os.path.join(self.root, self.base_folder, self.meta['filename'])
        if not check_integrity(path, self.meta['md5']):
            raise RuntimeError('Dataset metadata file not found or corrupted.' +
                               ' You can use download=True to download it')
        with open(path, 'rb') as infile:
            data = pickle.load(infile, encoding='latin1')
            self.classes = data[self.meta['key']]
        self.class_to_idx = {_class: i for i, _class in enumerate(self.classes)}



    def __getitem__(self, index):
        """
        Args:
            index (int): Index

        Returns:
            tuple: (image, target) where target is index of the target class.
        """
        img, target, clean_target, hat_clean_target, confidenice = self.data[index], int(self.targets[index]), int(self.clean_targets[index]), int(self.hat_clean_targets[index]), int(self.is_confident[index])

        # doing this so that it is consistent with all other datasets
        # to return a PIL Image
        img = Image.fromarray(img)

        if self.apply_transform_eval:
            transform = self.transform_eval
        else:
            transform = self.transform 

        if self.transform is not None:
            if self.transform_strong is not None:
                strongimg = self.transform_strong(img)
            else:
                strongimg = transform(img)
            img = transform(img)

   
            
        if self.target_transform is not None:
            target = self.target_transform(target)
            clean_target = self.target_transform(clean_target)
            hat_clean_target = self.target_transform(hat_clean_target)
            # confidenice = self.target_transform(confidenice)

        return img, target, clean_target, hat_clean_target, strongimg

    def __len__(self):
        return len(self.data)

    def _check_integrity(self):
        root = self.root
        for fentry in (self.train_list + self.test_list):
            filename, md5 = fentry[0], fentry[1]
            fpath = os.path.join(root, self.base_folder, filename)
            if not check_integrity(fpath, md5):
                return False
        return True

    def download(self):
        if self._check_integrity():
            print('Files already downloaded and verified')
            return
        download_and_extract_archive(self.url, self.root, filename=self.filename, md5=self.tgz_md5)

    def _set_targets(self,n_targets):
        self.targets = n_targets
 

    def _get_num_classes(self):
        return len(set(self.targets))

    def _get_targets(self):
        return self.targets.data.tolist()


    def train(self):
        self.apply_transform_eval = False


    def eval(self):
        self.apply_transform_eval = True

    def get_clean_ratio(self):
        correct = 0
        t_number = 0
        for (c_label, h_c_label, confidence) in zip(self.clean_targets, self.hat_clean_targets,  self.is_confident):
            if confidence == 1:
                if c_label == h_c_label:
                    correct +=1
                t_number +=1
        return correct/(t_number+1e-10)

    def extra_repr(self):
        return "Split: {}".format("Train" if self.use_train is True else "Test")

class CIFAR100_noise(CIFAR10_noise):
    """`CIFAR100 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset.

    This is a subclass of the `CIFAR10` Dataset.
    """
    base_folder = 'cifar-100-python'
    url = "https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz"
    filename = "cifar-100-python.tar.gz"
    tgz_md5 = 'eb9058c3a382ffc7106e4002c42a8d85'
    train_list = [
        ['train', '16019d7e3df5f24257cddd939b257f8d'],
    ]

    test_list = [
        ['test', 'f0ef6b0ae62326f3e7ffdfab6717acfc'],
    ]
    meta = {
        'filename': 'meta',
        'key': 'fine_label_names',
        'md5': '7973b15100ade9c7d40fb424638fde48',
    }
