#!/usr/bin/python
# coding: utf-8

import numpy 
import theano
import time
import theano.tensor as T
from theano.tensor.shared_randomstreams import RandomStreams
import copy

from utils import update_locals

class LogisticRegression(object):
    def __init__(self, input, n_in, n_out):
        # initialize with 0 the weights W as a matrix of shape (n_in, n_out) 
        self.W = theano.shared( value=numpy.zeros((n_in,n_out),
                                            dtype = theano.config.floatX) )
        # initialize the baises b as a vector of n_out 0s
        self.b = theano.shared( value=numpy.zeros((n_out,), 
                                            dtype = theano.config.floatX) )
        # compute vector of class-membership probabilities in symbolic form
        self.p_y_given_x = T.nnet.softmax(T.dot(input, self.W)+self.b)
        
        # compute prediction as class whose probability is maximal in 
        # symbolic form
        self.y_pred=T.argmax(self.p_y_given_x, axis=1)

        # list of parameters for this layer
        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 errors(self, y):
        # check if y has same dimension of y_pred 
        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))

        # check if y is of the correct datatype        
        if y.dtype.startswith('int'):
            # the T.neq operator returns a vector of 0s and 1s, where 1
            # represents a mistake in prediction
            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.nnet.sigmoid(T.dot(input, self.W) + self.b)
        self.params = [self.W, self.b]



class dA(object):
  def __init__(self, n_visible= 784, n_hidden= 500, corruption_level = 0.1,\
               input = None, shared_W = None, shared_b = None):
    self.n_visible = n_visible
    self.n_hidden  = n_hidden
    
    # create a Theano random generator that gives symbolic random values
    theano_rng = RandomStreams()
    
    if shared_W != None and shared_b != None : 
        self.W = shared_W
        self.b = shared_b
    else:
        # initial values for weights and biases
        # note : W' was written as `W_prime` and b' as `b_prime`

        # W is initialized with `initial_W` which is uniformely sampled
        # from -6./sqrt(n_visible+n_hidden) and 6./sqrt(n_hidden+n_visible)
        # the output of uniform if converted using asarray to dtype 
        # theano.config.floatX so that the code is runable on GPU
        initial_W = numpy.asarray( numpy.random.uniform( \
              low = -numpy.sqrt(6./(n_hidden+n_visible)), \
              high = numpy.sqrt(6./(n_hidden+n_visible)), \
              size = (n_visible, n_hidden)), dtype = theano.config.floatX)
        initial_b       = numpy.zeros(n_hidden, dtype = theano.config.floatX)
    
    
        # theano shared variables for weights and biases
        self.W       = theano.shared(value = initial_W,       name = "W")
        self.b       = theano.shared(value = initial_b,       name = "b")
    
 
    initial_b_prime= numpy.zeros(n_visible)
    # tied weights, therefore W_prime is W transpose
    self.W_prime = self.W.T 
    self.b_prime = theano.shared(value = initial_b_prime, name = "b'")

    # if no input is given, generate a variable representing the input
    if input == None : 
        # we use a matrix because we expect a minibatch of several examples,
        # each example being a row
        self.x = T.dmatrix(name = 'input') 
    else:
        self.x = input
    # Equation (1)
    # keep 90% of the inputs the same and zero-out randomly selected subset of 10% of the inputs
    # note : first argument of theano.rng.binomial is the shape(size) of 
    #        random numbers that it should produce
    #        second argument is the number of trials 
    #        third argument is the probability of success of any trial
    #
    #        this will produce an array of 0s and 1s where 1 has a 
    #        probability of 1 - ``corruption_level`` and 0 with
    #        ``corruption_level``
    self.tilde_x  = theano_rng.binomial( self.x.shape,  1,  1 - corruption_level) * self.x
    # Equation (2)
    # note  : y is stored as an attribute of the class so that it can be 
    #         used later when stacking dAs. 
    self.y   = T.nnet.sigmoid(T.dot(self.tilde_x, self.W      ) + self.b)
    # Equation (3)
    self.z   = T.nnet.sigmoid(T.dot(self.y, self.W_prime) + self.b_prime)
    # Equation (4)
    # note : we sum over the size of a datapoint; if we are using minibatches,
    #        L will  be a vector, with one entry per example in minibatch
    self.L = - T.sum( self.x*T.log(self.z) + (1-self.x)*T.log(1-self.z), axis=1 ) 
    # note : L is now a vector, where each element is the cross-entropy cost 
    #        of the reconstruction of the corresponding example of the 
    #        minibatch. We need to compute the average of all these to get 
    #        the cost of the minibatch
    self.cost = T.mean(self.L)

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




class SdA(object):
    def __init__(self, train_set_x, train_set_y, batch_size, n_ins, 
                 hidden_layers_sizes, n_outs, 
                 corruption_levels, rng, pretrain_lr, finetune_lr, input_divider=1.0):
        update_locals(self, locals())      
 
        self.layers             = []
        self.pretrain_functions = []
        self.params             = []
        self.n_layers           = len(hidden_layers_sizes)

        self.input_divider = numpy.asarray(input_divider, dtype=theano.config.floatX)

        if len(hidden_layers_sizes) < 1 :
            raiseException (' You must have at least one hidden layer ')


        # allocate symbolic variables for the data
        index   = T.lscalar()    # index to a [mini]batch 
        self.x  = T.matrix('x')  # the data is presented as rasterized images
        self.y  = T.ivector('y') # the labels are presented as 1D vector of 
                                 # [int] labels

        for i in xrange( self.n_layers ):
            # construct the sigmoidal layer

            # the size of the input is either the number of hidden units of 
            # the layer below or the input size if we are on the first layer
            if i == 0 :
                input_size = n_ins
            else:
                input_size = hidden_layers_sizes[i-1]

            # the input to this layer is either the activation of the hidden
            # layer below or the input of the SdA if you are on the first
            # layer
            if i == 0 : 
                layer_input = self.x
            else:
                layer_input = self.layers[-1].output

            layer = SigmoidalLayer(rng, layer_input, input_size, 
                                   hidden_layers_sizes[i] )
            # add the layer to the 
            self.layers += [layer]
            self.params += layer.params
        
            # Construct a denoising autoencoder that shared weights with this
            # layer
            dA_layer = dA(input_size, hidden_layers_sizes[i], \
                          corruption_level = corruption_levels[0],\
                          input = layer_input, \
                          shared_W = layer.W, shared_b = layer.b)
        
            # Construct a function that trains this dA
            # compute gradients of layer parameters
            gparams = T.grad(dA_layer.cost, dA_layer.params)
            # compute the list of updates
            updates = {}
            for param, gparam in zip(dA_layer.params, gparams):
                updates[param] = param - gparam * pretrain_lr
            
            # create a function that trains the dA
            update_fn = theano.function([index], dA_layer.cost, \
                  updates = updates,
                  givens = { 
                     self.x : train_set_x[index*batch_size:(index+1)*batch_size] / self.input_divider})
            # collect this function into a list
            self.pretrain_functions += [update_fn]

        
        # We now need to add a logistic layer on top of the MLP
        self.logLayer = LogisticRegression(\
                         input = self.layers[-1].output,\
                         n_in = hidden_layers_sizes[-1], n_out = n_outs)

        self.params += self.logLayer.params
        # construct a function that implements one step of finetunining

        # compute the cost, defined as the negative log likelihood 
        cost = self.logLayer.negative_log_likelihood(self.y)
        # compute the gradients with respect to the model parameters
        gparams = T.grad(cost, self.params)
        # compute list of updates
        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.input_divider,
                  self.y : train_set_y[index*batch_size:(index+1)*batch_size]} )

        # symbolic variable that points to the number of errors made on the
        # minibatch given by self.x and self.y

        self.errors = self.logLayer.errors(self.y)

    @classmethod
    def copy_reusing_lower_layers(cls, obj, num_hidden_layers, new_finetuning_lr=None):
        assert(num_hidden_layers <= obj.n_layers)

        if not new_finetuning_lr:
            new_finetuning_lr = obj.finetune_lr

        new_sda = cls(train_set_x= obj.train_set_x, \
                      train_set_y = obj.train_set_y,\
                      batch_size = obj.batch_size, \
                      n_ins= obj.n_ins, \
                      hidden_layers_sizes = obj.hidden_layers_sizes[:num_hidden_layers], \
                      n_outs = obj.n_outs, \
                      corruption_levels = obj.corruption_levels[:num_hidden_layers],\
                      rng = obj.rng,\
                      pretrain_lr = obj.pretrain_lr, \
                      finetune_lr = new_finetuning_lr, \
                      input_divider = obj.input_divider )

        # new_sda.layers contains only the hidden layers actually
        for i, layer in enumerate(new_sda.layers):
            original_layer = obj.layers[i]
            for p1,p2 in zip(layer.params, original_layer.params):
                p1.value = p2.value.copy()

        return new_sda

    def get_params_copy(self):
        return copy.deepcopy(self.params)

    def set_params_from_copy(self, copy):
        # We don't want to replace the var, as the functions have pointers in there
        # We only want to replace values.
        for i, p in enumerate(self.params):
            p.value = copy[i].value

    def get_params_means(self):
        s = []
        for p in self.params:
            s.append(numpy.mean(p.value))
        return s

if __name__ == '__main__':
    import sys
    args = sys.argv[1:]