import os
import os.path
import copy
import hashlib
import errno
import numpy as np
from numpy.testing import assert_array_almost_equal
import torch
import torch.nn.functional as F 

def check_integrity(fpath, md5):
    if not os.path.isfile(fpath):
        return False
    md5o = hashlib.md5()
    with open(fpath, 'rb') as f:
        # read in 1MB chunks
        for chunk in iter(lambda: f.read(1024 * 1024), b''):
            md5o.update(chunk)
    md5c = md5o.hexdigest()
    if md5c != md5:
        return False
    return True


def download_url(url, root, filename, md5):
    from six.moves import urllib

    root = os.path.expanduser(root)
    fpath = os.path.join(root, filename)

    try:
        os.makedirs(root)
    except OSError as e:
        if e.errno == errno.EEXIST:
            pass
        else:
            raise

    # downloads file
    if os.path.isfile(fpath) and check_integrity(fpath, md5):
        print('Using downloaded and verified file: ' + fpath)
    else:
        try:
            print('Downloading ' + url + ' to ' + fpath)
            urllib.request.urlretrieve(url, fpath)
        except:
            if url[:5] == 'https':
                url = url.replace('https:', 'http:')
                print('Failed download. Trying https -> http instead.'
                      ' Downloading ' + url + ' to ' + fpath)
                urllib.request.urlretrieve(url, fpath)


def list_dir(root, prefix=False):
    """List all directories at a given root

    Args:
        root (str): Path to directory whose folders need to be listed
        prefix (bool, optional): If true, prepends the path to each result, otherwise
            only returns the name of the directories found
    """
    root = os.path.expanduser(root)
    directories = list(
        filter(
            lambda p: os.path.isdir(os.path.join(root, p)),
            os.listdir(root)
        )
    )

    if prefix is True:
        directories = [os.path.join(root, d) for d in directories]

    return directories


def list_files(root, suffix, prefix=False):
    """List all files ending with a suffix at a given root

    Args:
        root (str): Path to directory whose folders need to be listed
        suffix (str or tuple): Suffix of the files to match, e.g. '.png' or ('.jpg', '.png').
            It uses the Python "str.endswith" method and is passed directly
        prefix (bool, optional): If true, prepends the path to each result, otherwise
            only returns the name of the files found
    """
    root = os.path.expanduser(root)
    files = list(
        filter(
            lambda p: os.path.isfile(os.path.join(root, p)) and p.endswith(suffix),
            os.listdir(root)
        )
    )

    if prefix is True:
        files = [os.path.join(root, d) for d in files]

    return files

# basic function#
def multiclass_noisify(y, P, random_state=0):
    """ Flip classes according to transition probability matrix T.
    It expects a number between 0 and the number of classes - 1.
    """
    #print np.max(y), P.shape[0]
    assert P.shape[0] == P.shape[1]
    assert np.max(y) < P.shape[0]

    # row stochastic matrix
    assert_array_almost_equal(P.sum(axis=1), np.ones(P.shape[1]))
    assert (P >= 0.0).all()

    m = y.shape[0]
    #print m
    new_y = y.copy()
    flipper = np.random.RandomState(random_state)
    print(f'flip with random seed {random_state}')

    for idx in np.arange(m):
        i = y[idx]
        # draw a vector with only an 1
        flipped = flipper.multinomial(1, P[i, :], 1)[0]
        new_y[idx] = np.where(flipped == 1)[0]

    return new_y


# noisify_pairflip call the function "multiclass_noisify"
def noisify_pairflip(y_train, noise, random_state=None, nb_classes=10):
    """mistakes:
        flip in the pair
    """
    P = np.eye(nb_classes)
    n = noise

    if n > 0.0:
        # 0 -> 1
        P[0, 0], P[0, 1] = 1. - n, n
        for i in range(1, nb_classes-1):
            P[i, i], P[i, i + 1] = 1. - n, n
        P[nb_classes-1, nb_classes-1], P[nb_classes-1, 0] = 1. - n, n

        y_train_noisy = multiclass_noisify(y_train, P=P,
                                           random_state=random_state)
        actual_noise = (y_train_noisy != y_train).mean()
        assert actual_noise > 0.0
        print('Actual noise %.2f' % actual_noise)
        y_train = y_train_noisy
    #print P

    return y_train, actual_noise

def noisify_multiclass_symmetric(y_train, noise, random_state=None, nb_classes=10):
    """mistakes:
        flip in the symmetric way
    """
    P = np.ones((nb_classes, nb_classes))
    n = noise
    P = (n / (nb_classes - 1)) * P

    if n > 0.0:
        # 0 -> 1
        P[0, 0] = 1. - n
        for i in range(1, nb_classes-1):
            P[i, i] = 1. - n
        P[nb_classes-1, nb_classes-1] = 1. - n

        y_train_noisy = multiclass_noisify(y_train, P=P,
                                           random_state=random_state)
        actual_noise = (y_train_noisy != y_train).mean()
        assert actual_noise > 0.0
        print('Actual noise %.2f' % actual_noise)
        y_train = y_train_noisy
    #print P

    return y_train, actual_noise

def noisify(dataset='mnist', nb_classes=10, train_labels=None, noise_type=None, noise_rate=0, random_state=0):
    if noise_type == 'pairflip':
        train_noisy_labels, actual_noise_rate = noisify_pairflip(train_labels, noise_rate, random_state=0, nb_classes=nb_classes)
    if noise_type == 'symmetric':
        train_noisy_labels, actual_noise_rate = noisify_multiclass_symmetric(train_labels, noise_rate, random_state=0, nb_classes=nb_classes)
    return train_noisy_labels, actual_noise_rate

def noisify_instance(train_data,train_labels,noise_rate):
    if max(train_labels)>10:
        num_class = 100
    else:
        num_class = 10
    np.random.seed(0)

    q_ = np.random.normal(loc=noise_rate,scale=0.1,size=1000000)
    q = []
    for pro in q_:
        if 0 < pro < 1:
            q.append(pro)
        if len(q)==50000:
            break

    w = np.random.normal(loc=0,scale=1,size=(32*32*3,num_class))

    noisy_labels = []
    for i, sample in enumerate(train_data):
        sample = sample.flatten()
        p_all = np.matmul(sample,w)
        p_all[train_labels[i]] = -1000000
        p_all = q[i]* F.softmax(torch.tensor(p_all),dim=0).numpy()
        p_all[train_labels[i]] = 1 - q[i]
        noisy_labels.append(np.random.choice(np.arange(num_class),p=p_all/sum(p_all)))
    over_all_noise_rate = 1 - float(torch.tensor(train_labels).eq(torch.tensor(noisy_labels)).sum())/50000
    return noisy_labels, over_all_noise_rate
'''
def noisify_instance(train_data,train_labels,noise_rate):
    if max(train_labels)>10:
        num_class = 100
    else:
        num_class = 10
    np.random.seed(0)

    p_ = np.random.normal(loc=noise_rate,scale=0.1,size=1000000)
    p = []
    for pro in p_:
        if 0 < pro < 1:
            p.append(pro)
        if len(p)==50000:
            break

    w = []
    for i in range(num_class):
        w.append(np.random.normal(loc=0,scale=1,size=3072))

    noisy_labels = []
    for i, sample in enumerate(train_data):
        sample = sample.flatten()
        w_sample = w[train_labels[i]]
        dot = np.dot(sample,w_sample)
        norm_dot = 1/(1+np.exp(-dot))
        p_flip = p[i]*norm_dot
        p_random = np.random.rand()
        if p_random <= p_flip:
            random_label = np.random.choice(range(num_class),1)
            noisy_labels.append(int(random_label))
        else:
            noisy_labels.append(train_labels[i])
    over_all_noise_rate = 1 - float(torch.tensor(train_labels).eq(torch.tensor(noisy_labels)).sum())/50000
    return noisy_labels, over_all_noise_rate
'''