text: '''
  Skip-thought vectors
  '''
  import os
  
  import theano
  import theano.tensor as tensor
  
  import cPickle as pkl
  import numpy
  import copy
  import nltk
  import os.path
  from collections import OrderedDict, defaultdict
  from scipy.linalg import norm
  from nltk.tokenize import word_tokenize
  
  profile = False
  
  #-----------------------------------------------------------------------------#
  # Specify model and table locations here
  #-----------------------------------------------------------------------------#
  # /Users/xtarx/Documents/TUM/3rd/Roboy/roboy_dialog/IntentGrouping/code/skip-thoughts/code/uni_skip.npz.pkl'
  # models_path = os.getcwd() + "/models/"
  # path_to_tables = os.getcwd() + "/models/"
  #-----------------------------------------------------------------------------#
  
  
  
  
  def load_model(args):
      """
      Load the model with saved tables
      """
      path_to_umodel = args.models_path + 'uni_skip.npz'
      path_to_bmodel = args.models_path + 'bi_skip.npz'
      # Load model options
      print 'Loading model parameters...'
      with open('%s.pkl'%path_to_umodel, 'rb') as f:
          uoptions = pkl.load(f)
      with open('%s.pkl'%path_to_bmodel, 'rb') as f:
          boptions = pkl.load(f)
  
      # Load parameters
      uparams = init_params(uoptions)
      uparams = load_params(path_to_umodel, uparams)
      utparams = init_tparams(uparams)
      bparams = init_params_bi(boptions)
      bparams = load_params(path_to_bmodel, bparams)
      btparams = init_tparams(bparams)
  
      # Extractor functions
      print 'Compiling encoders...'
      embedding, x_mask, ctxw2v = build_encoder(utparams, uoptions)
      f_w2v = theano.function([embedding, x_mask], ctxw2v, name='f_w2v')
      embedding, x_mask, ctxw2v = build_encoder_bi(btparams, boptions)
      f_w2v2 = theano.function([embedding, x_mask], ctxw2v, name='f_w2v2')
  
      # Tables
      print 'Loading tables...'
      utable, btable = load_tables(args)
  
      # Store everything we need in a dictionary
      print 'Packing up...'
      model = {}
      model['uoptions'] = uoptions
      model['boptions'] = boptions
      model['utable'] = utable
      model['btable'] = btable
      model['f_w2v'] = f_w2v
      model['f_w2v2'] = f_w2v2
  
      return model
  
  
  def load_tables(args):
      """
      Load the tables
      """
      words = []
      utable = numpy.load(args.models_path + 'utable.npy')
      btable = numpy.load(args.models_path + 'btable.npy')
      f = open(args.models_path  + 'dictionary.txt', 'rb')
      for line in f:
          words.append(line.decode('utf-8').strip())
      f.close()
      utable = OrderedDict(zip(words, utable))
      btable = OrderedDict(zip(words, btable))
      return utable, btable
  
  
  class Encoder(object):
      """
      Sentence encoder.
      """
  
      def __init__(self, model):
        self._model = model
  
      def encode(self, X, use_norm=True, verbose=True, batch_size=128, use_eos=False):
        """
        Encode sentences in the list X. Each entry will return a vector
        """
        return encode(self._model, X, use_norm, verbose, batch_size, use_eos)
  
  
  def encode(model, X, use_norm=True, verbose=True, batch_size=128, use_eos=False):
      """
      Encode sentences in the list X. Each entry will return a vector
      """
      # first, do preprocessing
      X = preprocess(X)
  
      # word dictionary and init
      d = defaultdict(lambda : 0)
      for w in model['utable'].keys():
          d[w] = 1
      ufeatures = numpy.zeros((len(X), model['uoptions']['dim']), dtype='float32')
      bfeatures = numpy.zeros((len(X), 2 * model['boptions']['dim']), dtype='float32')
  
      # length dictionary
      ds = defaultdict(list)
      captions = [s.split() for s in X]
      for i,s in enumerate(captions):
          ds[len(s)].append(i)
  
      # Get features. This encodes by length, in order to avoid wasting computation
      for k in ds.keys():
          if verbose:
              print k
          numbatches = len(ds[k]) / batch_size + 1
          for minibatch in range(numbatches):
              caps = ds[k][minibatch::numbatches]
  
              if use_eos:
                  uembedding = numpy.zeros((k+1, len(caps), model['uoptions']['dim_word']), dtype='float32')
                  bembedding = numpy.zeros((k+1, len(caps), model['boptions']['dim_word']), dtype='float32')
              else:
                  uembedding = numpy.zeros((k, len(caps), model['uoptions']['dim_word']), dtype='float32')
                  bembedding = numpy.zeros((k, len(caps), model['boptions']['dim_word']), dtype='float32')
              for ind, c in enumerate(caps):
                  caption = captions[c]
                  for j in range(len(caption)):
                      if d[caption[j]] > 0:
                          uembedding[j,ind] = model['utable'][caption[j]]
                          bembedding[j,ind] = model['btable'][caption[j]]
                      else:
                          uembedding[j,ind] = model['utable']['UNK']
                          bembedding[j,ind] = model['btable']['UNK']
                  if use_eos:
                      uembedding[-1,ind] = model['utable']['<eos>']
                      bembedding[-1,ind] = model['btable']['<eos>']
              if use_eos:
                  uff = model['f_w2v'](uembedding, numpy.ones((len(caption)+1,len(caps)), dtype='float32'))
                  bff = model['f_w2v2'](bembedding, numpy.ones((len(caption)+1,len(caps)), dtype='float32'))
              else:
                  uff = model['f_w2v'](uembedding, numpy.ones((len(caption),len(caps)), dtype='float32'))
                  bff = model['f_w2v2'](bembedding, numpy.ones((len(caption),len(caps)), dtype='float32'))
              if use_norm:
                  for j in range(len(uff)):
                      uff[j] /= norm(uff[j])
                      bff[j] /= norm(bff[j])
              for ind, c in enumerate(caps):
                  ufeatures[c] = uff[ind]
                  bfeatures[c] = bff[ind]
  
      features = numpy.c_[ufeatures, bfeatures]
      return features
  
  
  def preprocess(text):
      """
      Preprocess text for encoder
      """
      X = []
      sent_detector = nltk.data.load('tokenizers/punkt/english.pickle')
      for t in text:
          sents = sent_detector.tokenize(t)
          result = ''
          for s in sents:
              tokens = word_tokenize(s)
              result += ' ' + ' '.join(tokens)
          X.append(result)
      return X
  
  
  def nn(model, text, vectors, query, k=5):
      """
      Return the nearest neighbour sentences to query
      text: list of sentences
      vectors: the corresponding representations for text
      query: a string to search
      """
      qf = encode(model, [query])
      qf /= norm(qf)
      scores = numpy.dot(qf, vectors.T).flatten()
      sorted_args = numpy.argsort(scores)[::-1]
      sentences = [text[a] for a in sorted_args[:k]]
      print 'QUERY: ' + query
      print 'NEAREST: '
      for i, s in enumerate(sentences):
          print s, sorted_args[i]
  
  
  def word_features(table):
      """
      Extract word features into a normalized matrix
      """
      features = numpy.zeros((len(table), 620), dtype='float32')
      keys = table.keys()
      for i in range(len(table)):
          f = table[keys[i]]
          features[i] = f / norm(f)
      return features
  
  
  def nn_words(table, wordvecs, query, k=10):
      """
      Get the nearest neighbour words
      """
      keys = table.keys()
      qf = table[query]
      scores = numpy.dot(qf, wordvecs.T).flatten()
      sorted_args = numpy.argsort(scores)[::-1]
      words = [keys[a] for a in sorted_args[:k]]
      print 'QUERY: ' + query
      print 'NEAREST: '
      for i, w in enumerate(words):
          print w
  
  
  def _p(pp, name):
      """
      make prefix-appended name
      """
      return '%s_%s'%(pp, name)
  
  
  def init_tparams(params):
      """
      initialize Theano shared variables according to the initial parameters
      """
      tparams = OrderedDict()
      for kk, pp in params.iteritems():
          tparams[kk] = theano.shared(params[kk], name=kk)
      return tparams
  
  
  def load_params(path, params):
      """
      load parameters
      """
      pp = numpy.load(path)
      for kk, vv in params.iteritems():
          if kk not in pp:
              warnings.warn('%s is not in the archive'%kk)
              continue
          params[kk] = pp[kk]
      return params
  
  
  # layers: 'name': ('parameter initializer', 'feedforward')
  layers = {'gru': ('param_init_gru', 'gru_layer')}
  
  def get_layer(name):
      fns = layers[name]
      return (eval(fns[0]), eval(fns[1]))
  
  
  def init_params(options):
      """
      initialize all parameters needed for the encoder
      """
      params = OrderedDict()
  
      # embedding
      params['Wemb'] = norm_weight(options['n_words_src'], options['dim_word'])
  
      # encoder: GRU
      params = get_layer(options['encoder'])[0](options, params, prefix='encoder',
                                                nin=options['dim_word'], dim=options['dim'])
      return params
  
  
  def init_params_bi(options):
      """
      initialize all paramters needed for bidirectional encoder
      """
      params = OrderedDict()
  
      # embedding
      params['Wemb'] = norm_weight(options['n_words_src'], options['dim_word'])
  
      # encoder: GRU
      params = get_layer(options['encoder'])[0](options, params, prefix='encoder',
                                                nin=options['dim_word'], dim=options['dim'])
      params = get_layer(options['encoder'])[0](options, params, prefix='encoder_r',
                                                nin=options['dim_word'], dim=options['dim'])
      return params
  
  
  def build_encoder(tparams, options):
      """
      build an encoder, given pre-computed word embeddings
      """
      # word embedding (source)
      embedding = tensor.tensor3('embedding', dtype='float32')
      x_mask = tensor.matrix('x_mask', dtype='float32')
  
      # encoder
      proj = get_layer(options['encoder'])[1](tparams, embedding, options,
                                              prefix='encoder',
                                              mask=x_mask)
      ctx = proj[0][-1]
  
      return embedding, x_mask, ctx
  
  
  def build_encoder_bi(tparams, options):
      """
      build bidirectional encoder, given pre-computed word embeddings
      """
      # word embedding (source)
      embedding = tensor.tensor3('embedding', dtype='float32')
      embeddingr = embedding[::-1]
      x_mask = tensor.matrix('x_mask', dtype='float32')
      xr_mask = x_mask[::-1]
  
      # encoder
      proj = get_layer(options['encoder'])[1](tparams, embedding, options,
                                              prefix='encoder',
                                              mask=x_mask)
      projr = get_layer(options['encoder'])[1](tparams, embeddingr, options,
                                               prefix='encoder_r',
                                               mask=xr_mask)
  
      ctx = tensor.concatenate([proj[0][-1], projr[0][-1]], axis=1)
  
      return embedding, x_mask, ctx
  
  
  # some utilities
  def ortho_weight(ndim):
      W = numpy.random.randn(ndim, ndim)
      u, s, v = numpy.linalg.svd(W)
      return u.astype('float32')
  
  
  def norm_weight(nin,nout=None, scale=0.1, ortho=True):
      if nout == None:
          nout = nin
      if nout == nin and ortho:
          W = ortho_weight(nin)
      else:
          W = numpy.random.uniform(low=-scale, high=scale, size=(nin, nout))
      return W.astype('float32')
  
  
  def param_init_gru(options, params, prefix='gru', nin=None, dim=None):
      """
      parameter init for GRU
      """
      if nin == None:
          nin = options['dim_proj']
      if dim == None:
          dim = options['dim_proj']
      W = numpy.concatenate([norm_weight(nin,dim),
                             norm_weight(nin,dim)], axis=1)
      params[_p(prefix,'W')] = W
      params[_p(prefix,'b')] = numpy.zeros((2 * dim,)).astype('float32')
      U = numpy.concatenate([ortho_weight(dim),
                             ortho_weight(dim)], axis=1)
      params[_p(prefix,'U')] = U
  
      Wx = norm_weight(nin, dim)
      params[_p(prefix,'Wx')] = Wx
      Ux = ortho_weight(dim)
      params[_p(prefix,'Ux')] = Ux
      params[_p(prefix,'bx')] = numpy.zeros((dim,)).astype('float32')
  
      return params
  
  
  def gru_layer(tparams, state_below, options, prefix='gru', mask=None, **kwargs):
      """
      Forward pass through GRU layer
      """
      nsteps = state_below.shape[0]
      if state_below.ndim == 3:
          n_samples = state_below.shape[1]
      else:
          n_samples = 1
  
      dim = tparams[_p(prefix,'Ux')].shape[1]
  
      if mask == None:
          mask = tensor.alloc(1., state_below.shape[0], 1)
  
      def _slice(_x, n, dim):
          if _x.ndim == 3:
              return _x[:, :, n*dim:(n+1)*dim]
          return _x[:, n*dim:(n+1)*dim]
  
      state_below_ = tensor.dot(state_below, tparams[_p(prefix, 'W')]) + tparams[_p(prefix, 'b')]
      state_belowx = tensor.dot(state_below, tparams[_p(prefix, 'Wx')]) + tparams[_p(prefix, 'bx')]
      U = tparams[_p(prefix, 'U')]
      Ux = tparams[_p(prefix, 'Ux')]
  
      def _step_slice(m_, x_, xx_, h_, U, Ux):
          preact = tensor.dot(h_, U)
          preact += x_
  
          r = tensor.nnet.sigmoid(_slice(preact, 0, dim))
          u = tensor.nnet.sigmoid(_slice(preact, 1, dim))
  
          preactx = tensor.dot(h_, Ux)
          preactx = preactx * r
          preactx = preactx + xx_
  
          h = tensor.tanh(preactx)
  
          h = u * h_ + (1. - u) * h
          h = m_[:,None] * h + (1. - m_)[:,None] * h_
  
          return h
  
      seqs = [mask, state_below_, state_belowx]
      _step = _step_slice
  
      rval, updates = theano.scan(_step,
                                  sequences=seqs,
                                  outputs_info = [tensor.alloc(0., n_samples, dim)],
                                  non_sequences = [tparams[_p(prefix, 'U')],
                                                   tparams[_p(prefix, 'Ux')]],
                                  name=_p(prefix, '_layers'),
                                  n_steps=nsteps,
                                  profile=profile,
                                  strict=True)
      rval = [rval]
      return rval
  
  
  