import numpy
import theano
import time
import theano.tensor as T
from theano.tensor.shared_randomstreams import RandomStreams
import theano.sandbox.softsign

from theano.tensor.signal import downsample
from theano.tensor.nnet import conv 
import gzip
import cPickle
 
 
class LogisticRegression(object):
 
    def __init__(self, input, n_in, n_out):
 
        self.W = theano.shared( value=numpy.zeros((n_in,n_out),
                                            dtype = theano.config.floatX) )

        self.b = theano.shared( value=numpy.zeros((n_out,),
                                            dtype = theano.config.floatX) )

        self.p_y_given_x = T.nnet.softmax(T.dot(input, self.W)+self.b)
        

        self.y_pred=T.argmax(self.p_y_given_x, axis=1)
 
        self.params = [self.W, self.b]
 
    def negative_log_likelihood(self, y):
        return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]),y])
 
    def MSE(self, y):
        return -T.mean(abs((self.p_y_given_x)[T.arange(y.shape[0]),y]-y)**2)

    def errors(self, y):
        if y.ndim != self.y_pred.ndim:
            raise TypeError('y should have the same shape as self.y_pred',
                ('y', target.type, 'y_pred', self.y_pred.type))
 

        if y.dtype.startswith('int'):
            return T.mean(T.neq(self.y_pred, y))
        else:
            raise NotImplementedError()
 
 
class SigmoidalLayer(object):
    def __init__(self, rng, input, n_in, n_out):

        self.input = input
 
        W_values = numpy.asarray( rng.uniform( \
              low = -numpy.sqrt(6./(n_in+n_out)), \
              high = numpy.sqrt(6./(n_in+n_out)), \
              size = (n_in, n_out)), dtype = theano.config.floatX)
        self.W = theano.shared(value = W_values)
 
        b_values = numpy.zeros((n_out,), dtype= theano.config.floatX)
        self.b = theano.shared(value= b_values)
 
        self.output = T.tanh(T.dot(input, self.W) + self.b)
        self.params = [self.W, self.b]
 
class dA_conv(object):
 
  def __init__(self, corruption_level = 0.1, input = None, shared_W = None,\
                   shared_b = None, filter_shape = None, image_shape = None, poolsize = (2,2)):

    theano_rng = RandomStreams()
    
    fan_in = numpy.prod(filter_shape[1:])
    fan_out = filter_shape[0] * numpy.prod(filter_shape[2:])

    center = theano.shared(value = 1, name="center")
    scale = theano.shared(value = 2, name="scale")

    if shared_W != None and shared_b != None :
        self.W = shared_W
        self.b = shared_b
    else:
        initial_W = numpy.asarray( numpy.random.uniform( \
              low = -numpy.sqrt(6./(fan_in+fan_out)), \
              high = numpy.sqrt(6./(fan_in+fan_out)), \
              size = filter_shape), dtype = theano.config.floatX)
        initial_b = numpy.zeros((filter_shape[0],), dtype= theano.config.floatX)
    
    
        self.W = theano.shared(value = initial_W, name = "W")
        self.b = theano.shared(value = initial_b, name = "b")
    
 
    initial_b_prime= numpy.zeros((filter_shape[1],))
        
    self.W_prime=T.dtensor4('W_prime')

    self.b_prime = theano.shared(value = initial_b_prime, name = "b_prime")
 
    self.x = input

    self.tilde_x = theano_rng.binomial( self.x.shape, 1, 1 - corruption_level) * self.x

    conv1_out = conv.conv2d(self.tilde_x, self.W, \
                             filter_shape=filter_shape, \
                                image_shape=image_shape, border_mode='valid')

    
    self.y = T.tanh(conv1_out + self.b.dimshuffle('x', 0, 'x', 'x'))

    
    da_filter_shape = [ filter_shape[1], filter_shape[0], filter_shape[2],\
                       filter_shape[3] ]
    da_image_shape = [ image_shape[0],filter_shape[0],image_shape[2]-filter_shape[2]+1, \
                         image_shape[3]-filter_shape[3]+1 ]
    initial_W_prime =  numpy.asarray( numpy.random.uniform( \
              low = -numpy.sqrt(6./(fan_in+fan_out)), \
              high = numpy.sqrt(6./(fan_in+fan_out)), \
              size = da_filter_shape), dtype = theano.config.floatX)
    self.W_prime = theano.shared(value = initial_W_prime, name = "W_prime")

    #import pdb;pdb.set_trace()

    conv2_out = conv.conv2d(self.y, self.W_prime, \
                               filter_shape = da_filter_shape, image_shape = da_image_shape ,\
                                border_mode='full')

    self.z =  (T.tanh(conv2_out + self.b_prime.dimshuffle('x', 0, 'x', 'x'))+center) / scale

    scaled_x = (self.x + center) / scale

    self.L = - T.sum( scaled_x*T.log(self.z) + (1-scaled_x)*T.log(1-self.z), axis=1 )

    self.cost = T.mean(self.L)

    self.params = [ self.W, self.b, self.b_prime ] 
 
 

class LeNetConvPoolLayer(object):
    def __init__(self, rng, input, filter_shape, image_shape, poolsize=(2,2)):
        assert image_shape[1]==filter_shape[1]
        self.input = input
  
        W_values = numpy.zeros(filter_shape, dtype=theano.config.floatX)
        self.W = theano.shared(value = W_values)
 
        b_values = numpy.zeros((filter_shape[0],), dtype= theano.config.floatX)
        self.b = theano.shared(value= b_values)
 
        conv_out = conv.conv2d(input, self.W,
                filter_shape=filter_shape, image_shape=image_shape)
 

        fan_in = numpy.prod(filter_shape[1:])
        fan_out = filter_shape[0] * numpy.prod(filter_shape[2:]) / numpy.prod(poolsize)

        W_bound = numpy.sqrt(6./(fan_in + fan_out))
        self.W.value = numpy.asarray(
                rng.uniform(low=-W_bound, high=W_bound, size=filter_shape),
                dtype = theano.config.floatX)
  

        pooled_out = downsample.max_pool2D(conv_out, poolsize, ignore_border=True)
 
        self.output = T.tanh(pooled_out + self.b.dimshuffle('x', 0, 'x', 'x'))
        self.params = [self.W, self.b]
 

class SdA():
    def __init__(self, input, n_ins_conv, n_ins_mlp, train_set_x, train_set_y, batch_size, \
                     conv_hidden_layers_sizes, mlp_hidden_layers_sizes, corruption_levels, \
                     rng, n_out, pretrain_lr, finetune_lr):

        self.layers = []
        self.pretrain_functions = []
        self.params = []
        self.conv_n_layers = len(conv_hidden_layers_sizes)
        self.mlp_n_layers = len(mlp_hidden_layers_sizes)
         
        index = T.lscalar() # index to a [mini]batch
        self.x = T.dmatrix('x') # the data is presented as rasterized images
        self.y = T.ivector('y') # the labels are presented as 1D vector of
        
 
        
        for i in xrange( self.conv_n_layers ):

            filter_shape=conv_hidden_layers_sizes[i][0]
            image_shape=conv_hidden_layers_sizes[i][1]
            max_poolsize=conv_hidden_layers_sizes[i][2]
                
            if i == 0 :
                layer_input=self.x.reshape((batch_size,1,28,28))
            else:
                layer_input=self.layers[-1].output

            layer = LeNetConvPoolLayer(rng, input=layer_input, \
                                image_shape=image_shape, \
                                filter_shape=filter_shape,poolsize=max_poolsize)
            print 'Convolutional layer '+str(i+1)+' created'
                
            self.layers += [layer]
            self.params += layer.params
                
            da_layer = dA_conv(corruption_level = corruption_levels[0],\
                                  input = layer_input, \
                                  shared_W = layer.W, shared_b = layer.b,\
                                  filter_shape = filter_shape , image_shape = image_shape )
                
                
            gparams = T.grad(da_layer.cost, da_layer.params)
                
            updates = {}
            for param, gparam in zip(da_layer.params, gparams):
                    updates[param] = param - gparam * pretrain_lr
                    
                
            update_fn = theano.function([index], da_layer.cost, \
                                        updates = updates,
                                        givens = {
                    self.x : train_set_x[index*batch_size:(index+1)*batch_size]} )
             
            self.pretrain_functions += [update_fn]

        for i in xrange( self.mlp_n_layers ): 
            if i == 0 :
                input_size = n_ins_mlp
            else:
                input_size = mlp_hidden_layers_sizes[i-1]

            if i == 0 :
                if len( self.layers ) == 0 :
                    layer_input=self.x
                else :
                    layer_input = self.layers[-1].output.flatten(2)
            else:
                layer_input = self.layers[-1].output
     
            layer = SigmoidalLayer(rng, layer_input, input_size,
                                        mlp_hidden_layers_sizes[i] )
              
            self.layers += [layer]
            self.params += layer.params
            

            print 'MLP layer '+str(i+1)+' created'
            
        self.logLayer = LogisticRegression(input=self.layers[-1].output, \
                                                     n_in=mlp_hidden_layers_sizes[-1], n_out=n_out)
        self.params += self.logLayer.params

        cost = self.logLayer.negative_log_likelihood(self.y)

        gparams = T.grad(cost, self.params)
        updates = {}

        for param,gparam in zip(self.params, gparams):
            updates[param] = param - gparam*finetune_lr
            
        self.finetune = theano.function([index], cost,
                updates = updates,
                givens = {
                  self.x : train_set_x[index*batch_size:(index+1)*batch_size],
                  self.y : train_set_y[index*batch_size:(index+1)*batch_size]} )
 

        self.errors = self.logLayer.errors(self.y)
 
 
 
def sgd_optimization_mnist( learning_rate=0.1, pretraining_epochs = 2, \
                            pretrain_lr = 0.01, training_epochs = 1000, \
                            dataset='mnist.pkl.gz'):

    f = gzip.open(dataset,'rb')
    train_set, valid_set, test_set = cPickle.load(f)
    f.close()
 
 
    def shared_dataset(data_xy):
        data_x, data_y = data_xy
        shared_x = theano.shared(numpy.asarray(data_x, dtype=theano.config.floatX))
        shared_y = theano.shared(numpy.asarray(data_y, dtype=theano.config.floatX))
        return shared_x, T.cast(shared_y, 'int32')
 

    test_set_x, test_set_y = shared_dataset(test_set)
    valid_set_x, valid_set_y = shared_dataset(valid_set)
    train_set_x, train_set_y = shared_dataset(train_set)
 
    batch_size = 500 # size of the minibatch
 

    n_train_batches = train_set_x.value.shape[0] / batch_size
    n_valid_batches = valid_set_x.value.shape[0] / batch_size
    n_test_batches = test_set_x.value.shape[0] / batch_size
 
    # allocate symbolic variables for the data
    index = T.lscalar() # index to a [mini]batch
    x = T.matrix('x') # the data is presented as rasterized images
    y = T.ivector('y') # the labels are presented as 1d vector of
                           # [int] labels
    layer0_input = x.reshape((batch_size,1,28,28))
    

    # Setup the convolutional layers with their DAs(add as many as you want)
    corruption_levels = [ 0.2, 0.2, 0.2]
    rng = numpy.random.RandomState(1234)
    ker1=2
    ker2=2
    conv_layers=[]
    conv_layers.append([[ker1,1,5,5], [batch_size,1,28,28], [2,2] ])
    conv_layers.append([[ker2,ker1,5,5], [batch_size,ker1,12,12], [2,2] ])

    # Setup the MLP layers of the network
    mlp_layers=[500]
  
    network = SdA(input = layer0_input, n_ins_conv = 28*28, n_ins_mlp = ker2*4*4, \
                      train_set_x = train_set_x, train_set_y = train_set_y, batch_size = batch_size,
                      conv_hidden_layers_sizes = conv_layers,  \
                      mlp_hidden_layers_sizes = mlp_layers, \
                      corruption_levels = corruption_levels , n_out = 10, \
                      rng = rng , pretrain_lr = pretrain_lr , finetune_lr = learning_rate )

    test_model = theano.function([index], network.errors,
             givens = {
                network.x: test_set_x[index*batch_size:(index+1)*batch_size],
                network.y: test_set_y[index*batch_size:(index+1)*batch_size]})
 
    validate_model = theano.function([index], network.errors,
           givens = {
                network.x: valid_set_x[index*batch_size:(index+1)*batch_size],
                network.y: valid_set_y[index*batch_size:(index+1)*batch_size]})



    start_time = time.clock()
    for i in xrange(len(network.layers)-len(mlp_layers)):
        for epoch in xrange(pretraining_epochs):
            for batch_index in xrange(n_train_batches):
                c = network.pretrain_functions[i](batch_index)
            print 'pre-training convolution layer %i, epoch %d, cost '%(i,epoch),c

    patience = 10000 # look as this many examples regardless
    patience_increase = 2. # WAIT THIS MUCH LONGER WHEN A NEW BEST IS
                                  # FOUND
    improvement_threshold = 0.995 # a relative improvement of this much is

    validation_frequency = min(n_train_batches, patience/2)
 
 
    best_params = None
    best_validation_loss = float('inf')
    test_score = 0.
    start_time = time.clock()
 
    done_looping = False
    epoch = 0
 
    while (epoch < training_epochs) and (not done_looping):
      epoch = epoch + 1
      for minibatch_index in xrange(n_train_batches):
 
        cost_ij = network.finetune(minibatch_index)
        iter = epoch * n_train_batches + minibatch_index
 
        if (iter+1) % validation_frequency == 0:
            
            validation_losses = [validate_model(i) for i in xrange(n_valid_batches)]
            this_validation_loss = numpy.mean(validation_losses)
            print('epoch %i, minibatch %i/%i, validation error %f %%' % \
                   (epoch, minibatch_index+1, n_train_batches, \
                    this_validation_loss*100.))
 
 
            # if we got the best validation score until now
            if this_validation_loss < best_validation_loss:
 
                #improve patience if loss improvement is good enough
                if this_validation_loss < best_validation_loss * \
                       improvement_threshold :
                    patience = max(patience, iter * patience_increase)
 
                # save best validation score and iteration number
                best_validation_loss = this_validation_loss
                best_iter = iter
 
                # test it on the test set
                test_losses = [test_model(i) for i in xrange(n_test_batches)]
                test_score = numpy.mean(test_losses)
                print((' epoch %i, minibatch %i/%i, test error of best '
                      'model %f %%') %
                             (epoch, minibatch_index+1, n_train_batches,
                              test_score*100.))
 
 
        if patience <= iter :
                done_looping = True
                break
 
    end_time = time.clock()
    print(('Optimization complete with best validation score of %f %%,'
           'with test performance %f %%') %
                 (best_validation_loss * 100., test_score*100.))
    print ('The code ran for %f minutes' % ((end_time-start_time)/60.))
 
 
 
 
 
 
if __name__ == '__main__':
    sgd_optimization_mnist()