import cPickle as pickle
import numpy as np
import os
from scipy.misc import imread

def load_CIFAR_batch(filename):
  """ load single batch of cifar """
  with open(filename, 'rb') as f:
    datadict = pickle.load(f)
    X = datadict['data']
    Y = datadict['labels']
    X = X.reshape(10000, 3, 32, 32).transpose(0,2,3,1).astype("float")
    Y = np.array(Y)
    return X, Y

def load_CIFAR10(ROOT):
  



def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000):
    """
    Load the CIFAR-10 dataset from disk and perform preprocessing to prepare
    it for classifiers. These are the same steps as we used for the SVM, but
    condensed to a single function.
    """
    # Load the raw CIFAR-10 data
    cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'
    X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)
        
    # Subsample the data
    mask = range(num_training, num_training + num_validation)
    X_val = X_train[mask]
    y_val = y_train[mask]
    mask = range(num_training)
    X_train = X_train[mask]
    y_train = y_train[mask]
    mask = range(num_test)
    X_test = X_test[mask]
    y_test = y_test[mask]

    # Normalize the data: subtract the mean image
    mean_image = np.mean(X_train, axis=0)
    X_train -= mean_image
    X_val -= mean_image
    X_test -= mean_image

    pickle.dump(mean_image, open("mean_img", "wb"), 1)
    
    # Transpose so that channels come first
    X_train = X_train.transpose(0, 3, 1, 2).copy()
    X_val = X_val.transpose(0, 3, 1, 2).copy()
    X_test = X_test.transpose(0, 3, 1, 2).copy()

    # Package data into a dictionary
    return {
      'X_train': X_train, 'y_train': y_train,
      'X_val': X_val, 'y_val': y_val,
      'X_test': X_test, 'y_test': y_test,
    }
    

def load_tiny_imagenet(path, dtype=np.float32):
  """
  Load TinyImageNet. Each of TinyImageNet-100-A, TinyImageNet-100-B, and
  TinyImageNet-200 have the same directory structure, so this can be used
  to load any of them.

  Inputs:
  - path: String giving path to the directory to load.
  - dtype: numpy datatype used to load the data.

  Returns: A tuple of
  - class_names: A list where class_names[i] is a list of strings giving the
    WordNet names for class i in the loaded dataset.
  - X_train: (N_tr, 3, 64, 64) array of training images
  - y_train: (N_tr,) array of training labels
  - X_val: (N_val, 3, 64, 64) array of validation images
  - y_val: (N_val,) array of validation labels
  - X_test: (N_test, 3, 64, 64) array of testing images.
  - y_test: (N_test,) array of test labels; if test labels are not available
    (such as in student code) then y_test will be None.
  """
  # First load wnids
  with open(os.path.join(path, 'wnids.txt'), 'r') as f:
    wnids = [x.strip() for x in f]

  # Map wnids to integer labels
  wnid_to_label = {wnid: i for i, wnid in enumerate(wnids)}

  # Use words.txt to get names for each class
  with open(os.path.join(path, 'words.txt'), 'r') as f:
    wnid_to_words = dict(line.split('\t') for line in f)
    for wnid, words in wnid_to_words.iteritems():
      wnid_to_words[wnid] = [w.strip() for w in words.split(',')]
  class_names = [wnid_to_words[wnid] for wnid in wnids]

  # Next load training data.
  X_train = []
  y_train = []
  for i, wnid in enumerate(wnids):
    if (i + 1) % 20 == 0:
      print 'loading training data for synset %d / %d' % (i + 1, len(wnids))
    # To figure out the filenames we need to open the boxes file
    boxes_file = os.path.join(path, 'train', wnid, '%s_boxes.txt' % wnid)
    with open(boxes_file, 'r') as f:
      filenames = [x.split('\t')[0] for x in f]
    num_images = len(filenames)
    
    X_train_block = np.zeros((num_images, 3, 64, 64), dtype=dtype)
    y_train_block = wnid_to_label[wnid] * np.ones(num_images, dtype=np.int64)
    for j, img_file in enumerate(filenames):
      img_file = os.path.join(path, 'train', wnid, 'images', img_file)
      img = imread(img_file)
      if img.ndim == 2:
        ## grayscale file
        img.shape = (64, 64, 1)
      X_train_block[j] = img.transpose(2, 0, 1)
    X_train.append(X_train_block)
    y_train.append(y_train_block)
      
  # We need to concatenate all training data
  X_train = np.concatenate(X_train, axis=0)
  y_train = np.concatenate(y_train, axis=0)
  
  # Next load validation data
  with open(os.path.join(path, 'val', 'val_annotations.txt'), 'r') as f:
    img_files = []
    val_wnids = []
    for line in f:
      img_file, wnid = line.split('\t')[:2]
      img_files.append(img_file)
      val_wnids.append(wnid)
    num_val = len(img_files)
    y_val = np.array([wnid_to_label[wnid] for wnid in val_wnids])
    X_val = np.zeros((num_val, 3, 64, 64), dtype=dtype)
    for i, img_file in enumerate(img_files):
      img_file = os.path.join(path, 'val', 'images', img_file)
      img = imread(img_file)
      if img.ndim == 2:
        img.shape = (64, 64, 1)
      X_val[i] = img.transpose(2, 0, 1)

  # Next load test images
  # Students won't have test labels, so we need to iterate over files in the
  # images directory.
  img_files = os.listdir(os.path.join(path, 'test', 'images'))
  X_test = np.zeros((len(img_files), 3, 64, 64), dtype=dtype)
  for i, img_file in enumerate(img_files):
    img_file = os.path.join(path, 'test', 'images', img_file)
    img = imread(img_file)
    if img.ndim == 2:
      img.shape = (64, 64, 1)
    X_test[i] = img.transpose(2, 0, 1)

  y_test = None
  y_test_file = os.path.join(path, 'test', 'test_annotations.txt')
  if os.path.isfile(y_test_file):
    with open(y_test_file, 'r') as f:
      img_file_to_wnid = {}
      for line in f:
        line = line.split('\t')
        img_file_to_wnid[line[0]] = line[1]
    y_test = [wnid_to_label[img_file_to_wnid[img_file]] for img_file in img_files]
    y_test = np.array(y_test)
  
  return class_names, X_train, y_train, X_val, y_val, X_test, y_test


def load_models(models_dir):
  """
  Load saved models from disk. This will attempt to unpickle all files in a
  directory; any files that give errors on unpickling (such as README.txt) will
  be skipped.

  Inputs:
  - models_dir: String giving the path to a directory containing model files.
    Each model file is a pickled dictionary with a 'model' field.

  Returns:
  A dictionary mapping model file names to models.
  """
  models = {}
  for model_file in os.listdir(models_dir):
    with open(os.path.join(models_dir, model_file), 'rb') as f:
      try:
        models[model_file] = pickle.load(f)['model']
      except pickle.UnpicklingError:
        continue
  return models

""" load all of cifar """
  xs = []
  ys = []
  for b in range(1,6):
    f = os.path.join(ROOT, 'data_batch_%d' % (b, ))
    X, Y = load_CIFAR_batch(f)
    xs.append(X)
    ys.append(Y)    
  Xtr = np.concatenate(xs)
  Ytr = np.concatenate(ys)
  del X, Y
  Xte, Yte = load_CIFAR_batch(os.path.join(ROOT, 'test_batch'))
  return Xtr, Ytr, Xte, Yte