# Path: util/data.py
# def whiten(X_train, X_valid):
# def load_cifar10():
#     def _progress(count, block_size, total_size):
#   def load_CIFAR_batch(filename):
# def load_mnist():
#   def download(filename, source='http://yann.lecun.com/exdb/mnist/'):
#   def load_mnist_images(filename):
#   def load_mnist_labels(filename):
# def load_svhn():
#   def download(filename, source="https://github.com/smlaine2/tempens/raw/master/data/svhn/"):
#   def load_svhn_files(filenames):
# def split_semisup(X, y, n_lbl):
# def load_digits():
# def load_noise(n=100,d=5):
# def load_h5(h5_path):
# def nudge_dataset(X, Y):
# def prepare_dataset(X_train, y_train, X_test, y_test, aug_translation=0, zca=False):
#   def whiten_norm(x):
#     def __init__(self, regularization=1e-5, x=None):
#     def fit(self, x):
#     def apply(self, x):
#     def invert(self, x):
#   DATA_URL = 'http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'
#       X = datadict['data']
#       Y = datadict['labels']
#       X = X.reshape(10000, 3, 32, 32).astype("float32")
#       Y = np.array(Y, dtype=np.uint8)
#     X, Y = load_CIFAR_batch(f)
#     X = np.asarray(digits.data, 'float32')
#     X, Y = nudge_dataset(X, digits.target)
#     X = (X - np.min(X, 0)) / (np.max(X, 0) + 0.0001)  # 0-1 scaling
#     X = X.reshape((n,1,d,d))
#     Y = np.array(Y, dtype=np.uint8)
#   X = np.random.randint(2,size=(n,1,d,d)).astype('float32')
#   Y = np.random.randint(2,size=(n,)).astype(np.uint8)
#     X = np.array(hf.get('data'))
#     Y = np.array(hf.get('label'))
#     X = np.concatenate([X] +
#                        [np.apply_along_axis(shift, 1, X, vector)
#                         for vector in direction_vectors])
#     Y = np.concatenate([Y for _ in range(5)], axis=0)
#         U, S, V = np.linalg.svd(sigma)
# class ZCA(object):
# 
# Path: util/fig.py
# def parselog(fname, xi=0, yi=2):
# def plot_many(fname, curves, names=None, double=[]):
# def plot_one_vs_many(fname, main_curve, curves, double_main=False):
# def plot_many_vs_many(fname, curves1, curves2, double1=False, double2=False):
# def _flip_and_stretch(curves, idx=[]):
# 
# Path: util/launch.py
# def print_grid(args):


import argparse
  import models
  import numpy as np
from util import data, fig, launch
    model = models.ADGM(n_dim=n_dim, n_out=n_out, n_chan=n_channels, n_batch=args.n_batch,
                          n_superbatch=args.n_superbatch, opt_alg=args.alg, opt_params=p,
                          model='bernoulli' if args.dataset in ('digits', 'mnist') 
                                            else 'gaussian')
  elif args.model == 'hdgm':
    model = models.HDGM(n_dim=n_dim, n_out=n_out, n_chan=n_channels, n_batch=args.n_batch,
                          n_superbatch=args.n_superbatch, opt_alg=args.alg, opt_params=p)        
  elif args.model == 'dadgm':
    model = models.DADGM(n_dim=n_dim, n_out=n_out, n_chan=n_channels,
                          n_superbatch=args.n_superbatch, opt_alg=args.alg, opt_params=p) 
  elif args.model == 'dcgan':
    model = models.DCGAN(n_dim=n_dim, n_out=n_out, n_chan=n_channels,
                          n_superbatch=args.n_superbatch, opt_alg=args.alg, opt_params=p)   
  elif args.model == 'ssadgm':
    X_train_lbl, y_train_lbl, X_train_unl, y_train_unl \
      = data.split_semisup(X_train, y_train, n_lbl=args.n_labeled)
    model = models.SSADGM(X_labeled=X_train_lbl, y_labeled=y_train_lbl, n_out=n_out,
                          n_superbatch=args.n_superbatch, opt_alg=args.alg, opt_params=p)
    X_train, y_train = X_train_unl, y_train_unl
  else:
    raise ValueError('Invalid model')
  
  # train model
  model.fit(X_train, y_train, X_val, y_val, 
            n_epoch=args.epochs, n_batch=args.n_batch,
            logname=args.logname)

def plot(args):
  curves = []
  for f in args.logfiles:

# Path: util/data.py
# def whiten(X_train, X_valid):
# def load_cifar10():
#     def _progress(count, block_size, total_size):
#   def load_CIFAR_batch(filename):
# def load_mnist():
#   def download(filename, source='http://yann.lecun.com/exdb/mnist/'):
#   def load_mnist_images(filename):
#   def load_mnist_labels(filename):
# def load_svhn():
#   def download(filename, source="https://github.com/smlaine2/tempens/raw/master/data/svhn/"):
#   def load_svhn_files(filenames):
# def split_semisup(X, y, n_lbl):
# def load_digits():
# def load_noise(n=100,d=5):
# def load_h5(h5_path):
# def nudge_dataset(X, Y):
# def prepare_dataset(X_train, y_train, X_test, y_test, aug_translation=0, zca=False):
#   def whiten_norm(x):
#     def __init__(self, regularization=1e-5, x=None):
#     def fit(self, x):
#     def apply(self, x):
#     def invert(self, x):
#   DATA_URL = 'http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'
#       X = datadict['data']
#       Y = datadict['labels']
#       X = X.reshape(10000, 3, 32, 32).astype("float32")
#       Y = np.array(Y, dtype=np.uint8)
#     X, Y = load_CIFAR_batch(f)
#     X = np.asarray(digits.data, 'float32')
#     X, Y = nudge_dataset(X, digits.target)
#     X = (X - np.min(X, 0)) / (np.max(X, 0) + 0.0001)  # 0-1 scaling
#     X = X.reshape((n,1,d,d))
#     Y = np.array(Y, dtype=np.uint8)
#   X = np.random.randint(2,size=(n,1,d,d)).astype('float32')
#   Y = np.random.randint(2,size=(n,)).astype(np.uint8)
#     X = np.array(hf.get('data'))
#     Y = np.array(hf.get('label'))
#     X = np.concatenate([X] +
#                        [np.apply_along_axis(shift, 1, X, vector)
#                         for vector in direction_vectors])
#     Y = np.concatenate([Y for _ in range(5)], axis=0)
#         U, S, V = np.linalg.svd(sigma)
# class ZCA(object):
# 
# Path: util/fig.py
# def parselog(fname, xi=0, yi=2):
# def plot_many(fname, curves, names=None, double=[]):
# def plot_one_vs_many(fname, main_curve, curves, double_main=False):
# def plot_many_vs_many(fname, curves1, curves2, double1=False, double2=False):
# def _flip_and_stretch(curves, idx=[]):
# 
# Path: util/launch.py
# def print_grid(args):


# Path: run.py
import argparse
  import models
  import numpy as np
from util import data, fig, launch

    model = models.ADGM(n_dim=n_dim, n_out=n_out, n_chan=n_channels, n_batch=args.n_batch,
                          n_superbatch=args.n_superbatch, opt_alg=args.alg, opt_params=p,
                          model='bernoulli' if args.dataset in ('digits', 'mnist') 
                                            else 'gaussian')
  elif args.model == 'hdgm':
    model = models.HDGM(n_dim=n_dim, n_out=n_out, n_chan=n_channels, n_batch=args.n_batch,
                          n_superbatch=args.n_superbatch, opt_alg=args.alg, opt_params=p)        
  elif args.model == 'dadgm':
    model = models.DADGM(n_dim=n_dim, n_out=n_out, n_chan=n_channels,
                          n_superbatch=args.n_superbatch, opt_alg=args.alg, opt_params=p) 
  elif args.model == 'dcgan':
    model = models.DCGAN(n_dim=n_dim, n_out=n_out, n_chan=n_channels,
                          n_superbatch=args.n_superbatch, opt_alg=args.alg, opt_params=p)   
  elif args.model == 'ssadgm':
    X_train_lbl, y_train_lbl, X_train_unl, y_train_unl \
      = data.split_semisup(X_train, y_train, n_lbl=args.n_labeled)
    model = models.SSADGM(X_labeled=X_train_lbl, y_labeled=y_train_lbl, n_out=n_out,
                          n_superbatch=args.n_superbatch, opt_alg=args.alg, opt_params=p)
    X_train, y_train = X_train_unl, y_train_unl
  else:
    raise ValueError('Invalid model')
  
  # train model
  model.fit(X_train, y_train, X_val, y_val, 
            n_epoch=args.epochs, n_batch=args.n_batch,
            logname=args.logname)

def plot(args):
  curves = []
  for f in args.logfiles:

    x, y = fig.parselog(f, yi=args.col)