# Path: mozi/utils/progbar.py
# class Progbar(object):
#     def __init__(self, target, width=30, verbose=1):
#         '''
#             @param target: total number of steps expected
#         '''
#         self.width = width
#         self.target = target
#         self.sum_values = {}
#         self.unique_values = []
#         self.start = time.time()
#         self.total_width = 0
#         self.seen_so_far = 0
#         self.verbose = verbose
# 
#     def update(self, current, values=[]):
#         '''
#             @param current: index of current step
#             @param values: list of tuples (name, value_for_last_step).
#             The progress bar will display averages for these values.
#         '''
#         for k, v in values:
#             if k not in self.sum_values:
#                 self.sum_values[k] = [v * (current-self.seen_so_far), current-self.seen_so_far]
#                 self.unique_values.append(k)
#             else:
#                 self.sum_values[k][0] += v * (current-self.seen_so_far)
#                 self.sum_values[k][1] += (current-self.seen_so_far)
#         self.seen_so_far = current
# 
#         now = time.time()
#         if self.verbose == 1:
#             prev_total_width = self.total_width
#             sys.stdout.write("\b" * prev_total_width)
#             sys.stdout.write("\r")
# 
#             numdigits = int(np.floor(np.log10(self.target))) + 1
#             barstr = '%%%dd/%%%dd [' % (numdigits, numdigits)
#             bar = barstr % (current, self.target)
#             prog = float(current)/self.target
#             if current > self.target:
#                 prog = 1
#             prog_width = int(self.width*prog)
#             if prog_width > 0:
#                 bar += ('='*(prog_width-1))
#                 if current < self.target:
#                     bar += '>'
#             bar += ('.'*(self.width-prog_width))
#             bar += ']'
#             sys.stdout.write(bar)
#             self.total_width = len(bar)
# 
#             if current:
#                 time_per_unit = (now - self.start) / current
#             else:
#                 time_per_unit = 0
#             eta = time_per_unit*(self.target - current)
#             info = ''
#             if current < self.target:
#                 info += ' - ETA: %ds' % eta
#             else:
#                 info += ' - %ds' % (now - self.start)
#             for k in self.unique_values:
#                 info += ' - %s: %.4f' % (k, self.sum_values[k][0]/ max(1, self.sum_values[k][1]))
# 
#             self.total_width += len(info)
#             if prev_total_width > self.total_width:
#                 info += ((prev_total_width-self.total_width) * " ")
# 
#             sys.stdout.write(info)
#             sys.stdout.flush()
# 
#         if self.verbose == 2:
#             if current >= self.target:
#                 info = '%ds' % (now - self.start)
#                 for k in self.unique_values:
#                     info += ' - %s: %.4f' % (k, self.sum_values[k][0]/ max(1, self.sum_values[k][1]))
#                 sys.stdout.write(info + "\n")
# 
# 
#     def add(self, n, values=[]):
#         self.update(self.seen_so_far+n, values)
# 
# Path: mozi/utils/train_object_utils.py
# def is_shared_var(var):
#     return var.__class__.__name__ == 'TensorSharedVariable' or \
#             var.__class__.__name__ == 'CudaNdarraySharedVariable'


import matplotlib
import theano
import theano.tensor as T
import numpy as np
import matplotlib.pyplot as plt
import tarfile, inspect, os
from theano.compile.ops import as_op
from mozi.utils.progbar import Progbar
from mozi.utils.train_object_utils import is_shared_var
from six.moves.urllib.request import urlretrieve
def make_one_hot(X, onehot_size):
    """
    DESCRIPTION:
        Make a one-hot version of X
    PARAM:
        X: 1d numpy with each value in X representing the class of X
        onehot_size: length of the one hot vector
    RETURN:
        2d numpy tensor, with each row been the onehot vector
    """

    rX = np.zeros((len(X), onehot_size), dtype=theano.config.floatX)
    for i in xrange(len(X)):
        rX[i, X[i]] = 1

    return rX

def get_file(fpath, origin, untar=False):
    datadir = os.path.dirname(fpath)
    if not os.path.exists(datadir):
        os.makedirs(datadir)

    if not os.path.exists(fpath):
        print('Downloading data from',  origin)

        global progbar
        progbar = None
        def dl_progress(count, block_size, total_size):
            global progbar
            if progbar is None:

# Path: mozi/utils/progbar.py
# class Progbar(object):
#     def __init__(self, target, width=30, verbose=1):
#         '''
#             @param target: total number of steps expected
#         '''
#         self.width = width
#         self.target = target
#         self.sum_values = {}
#         self.unique_values = []
#         self.start = time.time()
#         self.total_width = 0
#         self.seen_so_far = 0
#         self.verbose = verbose
# 
#     def update(self, current, values=[]):
#         '''
#             @param current: index of current step
#             @param values: list of tuples (name, value_for_last_step).
#             The progress bar will display averages for these values.
#         '''
#         for k, v in values:
#             if k not in self.sum_values:
#                 self.sum_values[k] = [v * (current-self.seen_so_far), current-self.seen_so_far]
#                 self.unique_values.append(k)
#             else:
#                 self.sum_values[k][0] += v * (current-self.seen_so_far)
#                 self.sum_values[k][1] += (current-self.seen_so_far)
#         self.seen_so_far = current
# 
#         now = time.time()
#         if self.verbose == 1:
#             prev_total_width = self.total_width
#             sys.stdout.write("\b" * prev_total_width)
#             sys.stdout.write("\r")
# 
#             numdigits = int(np.floor(np.log10(self.target))) + 1
#             barstr = '%%%dd/%%%dd [' % (numdigits, numdigits)
#             bar = barstr % (current, self.target)
#             prog = float(current)/self.target
#             if current > self.target:
#                 prog = 1
#             prog_width = int(self.width*prog)
#             if prog_width > 0:
#                 bar += ('='*(prog_width-1))
#                 if current < self.target:
#                     bar += '>'
#             bar += ('.'*(self.width-prog_width))
#             bar += ']'
#             sys.stdout.write(bar)
#             self.total_width = len(bar)
# 
#             if current:
#                 time_per_unit = (now - self.start) / current
#             else:
#                 time_per_unit = 0
#             eta = time_per_unit*(self.target - current)
#             info = ''
#             if current < self.target:
#                 info += ' - ETA: %ds' % eta
#             else:
#                 info += ' - %ds' % (now - self.start)
#             for k in self.unique_values:
#                 info += ' - %s: %.4f' % (k, self.sum_values[k][0]/ max(1, self.sum_values[k][1]))
# 
#             self.total_width += len(info)
#             if prev_total_width > self.total_width:
#                 info += ((prev_total_width-self.total_width) * " ")
# 
#             sys.stdout.write(info)
#             sys.stdout.flush()
# 
#         if self.verbose == 2:
#             if current >= self.target:
#                 info = '%ds' % (now - self.start)
#                 for k in self.unique_values:
#                     info += ' - %s: %.4f' % (k, self.sum_values[k][0]/ max(1, self.sum_values[k][1]))
#                 sys.stdout.write(info + "\n")
# 
# 
#     def add(self, n, values=[]):
#         self.update(self.seen_so_far+n, values)
# 
# Path: mozi/utils/train_object_utils.py
# def is_shared_var(var):
#     return var.__class__.__name__ == 'TensorSharedVariable' or \
#             var.__class__.__name__ == 'CudaNdarraySharedVariable'


# Path: mozi/utils/utils.py
import matplotlib
import theano
import theano.tensor as T
import numpy as np
import matplotlib.pyplot as plt
import tarfile, inspect, os
from theano.compile.ops import as_op
from mozi.utils.progbar import Progbar
from mozi.utils.train_object_utils import is_shared_var
from six.moves.urllib.request import urlretrieve

def make_one_hot(X, onehot_size):
    """
    DESCRIPTION:
        Make a one-hot version of X
    PARAM:
        X: 1d numpy with each value in X representing the class of X
        onehot_size: length of the one hot vector
    RETURN:
        2d numpy tensor, with each row been the onehot vector
    """

    rX = np.zeros((len(X), onehot_size), dtype=theano.config.floatX)
    for i in xrange(len(X)):
        rX[i, X[i]] = 1

    return rX

def get_file(fpath, origin, untar=False):
    datadir = os.path.dirname(fpath)
    if not os.path.exists(datadir):
        os.makedirs(datadir)

    if not os.path.exists(fpath):
        print('Downloading data from',  origin)

        global progbar
        progbar = None
        def dl_progress(count, block_size, total_size):
            global progbar
            if progbar is None:

                progbar = Progbar(total_size)