# coding: utf-8
  
  # In[2]:
  
  # Import
  import pandas
  # Alignement
  import nltk
  import codecs
  import gensim
  from scipy import sparse
  import itertools
  from sklearn.feature_extraction.text import CountVectorizer
  import scipy.sparse
  import scipy.io
  from sklearn import preprocessing
  from keras.models import Sequential
  from keras.layers.core import Dense, Dropout, Activation,AutoEncoder
  from keras.optimizers import SGD,Adam
  from keras.layers import containers
  from mlp import *
  import mlp
  import sklearn.metrics
  import shelve
  import pickle
  from utils import *
  import sys
  import json
  # In[4]:
  
  db=shelve.open("{}.shelve".format(sys.argv[2]),writeback=True)
  
  sparse_model=shelve.open("{}.shelve".format(sys.argv[1]))
  #['ASR', 'TRS', 'LABEL']
  # In[6]:
  
  ASR=sparse_model["ASR"]
  TRS=sparse_model["TRS"]
  LABEL=sparse_model["LABEL"]
  
  db["ASR_SPARSE"]=ASR
  db["TRS_SPARSE"]=TRS
  db["LABEL"]=LABEL
  print "todo label"
  def select(elm):
      return int(elm.split("_")[-1])
  #z.apply(select)
  label_bin={}
  lb = preprocessing.LabelBinarizer(neg_label=0)
  lb.fit(LABEL["TRAIN"].apply(select))
  for i in ASR.keys():
      label_bin=lb.transform(LABEL[i].apply(select))
  
  hidden_size=50
  input_activation="tanh"
  out_activation="tanh"
  loss="mse"
  epochs=500
  batch=1
  patience=60
  w1_size=3000
  w2_size=500
  do_do=False
  sgd = Adam(lr=0.0001)#SGD(lr=0.00001,nesterov=False) #'rmsprop'# Adam(lr=0.00001)#SGD(lr=0.001, momentum=0.9, nesterov=True)
  try :
      sgd_repr=sgd.get_config()
  except AttributeError :
      sgd_repr=sgd
  json.dump({ "h1" : hidden_size,
  	"inside_activation" : input_activation,
  	"out_activation" : out_activation,
          "do_dropout": do_do,
  	"loss" : loss,
  	"epochs" : epochs ,
  	"batch_size" : batch,
  	"patience" : patience,
          "sgd" : sgd_repr},
  	open("{}.json".format(sys.argv[2]),"w"),
  	indent=4)
  print "gogo autoencoder ASR"
  autoencode=Sequential()
  autoencode.add(Dense(hidden_size,input_dim=ASR["TRAIN"].shape[1],init='glorot_uniform',activation=input_activation))
  if do_do :
      autoencode.add(Dropout(0.5))
  autoencode.add(Dense(ASR["DEV"].todense().shape[1],input_dim=hidden_size,init="glorot_uniform",activation=out_activation))
  
  #autoencode.compile(optimizer=sgd,loss=loss)
  
  autoencode.compile(optimizer=sgd,loss=loss)
  
  
  # In[ ]:
  
  autoencode.fit(ASR["TRAIN"].todense(),ASR["TRAIN"].todense(),nb_epoch=epochs,batch_size=batch,
                 callbacks=[keras.callbacks.EarlyStopping(monitor='val_loss',
                     patience=patience, verbose=0)],validation_data=(ASR["DEV"].todense(),ASR["DEV"].todense()),verbose=1)
  
  
  # In[ ]:
  
  auto_decoder=Sequential()
  auto_decoder.add(Dense(hidden_size,input_dim=ASR["DEV"].todense().shape[1],init='uniform',activation=input_activation,weights=autoencode.get_weights()[:2]))
  auto_decoder.compile(optimizer=sgd,loss=loss)
  
  
  # In[77]:
  
  #autoencode.predict(ASR["DEV"].todense())
  
  
  # In[ ]:
  
  print "auto encoder et auto decoder asr okay"
  
  ASR_AE_H1={}
  for i in ASR.keys():
      ASR_AE_H1[i]=auto_decoder.predict(ASR[i].todense())
      #TRS[i]=dico.transform(TRS[i][2])
  
  db["ASR_AE_H1"]=ASR_AE_H1
  
  print "auto encoder trs learning"
  # In[68]:/
  autoencode_trs=Sequential()
  autoencode_trs.add(Dense(hidden_size,input_dim=TRS["DEV"].todense().shape[1],init='glorot_uniform',activation=input_activation))
  if do_do:
      autoencode_trs.add(Dropout(0.5))
  autoencode_trs.add(Dense(TRS["DEV"].todense().shape[1],input_dim=hidden_size,init="glorot_uniform",activation=out_activation))
  
  #autoencode_trs.compile(optimizer=sgd_trs,loss=loss)
  
  autoencode_trs.compile(optimizer=sgd,loss=loss)
  
  
  # In[69]:
  
  autoencode_trs.fit(TRS["TRAIN"].todense(),TRS["TRAIN"].todense(),nb_epoch=epochs,batch_size=batch,
                 callbacks=[keras.callbacks.EarlyStopping(monitor='val_loss',
                                                          patience=patience, verbose=0)],
                 validation_data=(TRS["DEV"].todense(),TRS["DEV"].todense()),verbose=1)
  
  
  # In[87]:
  
  
  
  auto_decoder_trs=Sequential()
  auto_decoder_trs.add(Dense(hidden_size,input_dim=ASR["DEV"].todense().shape[1],activation=input_activation,weights=autoencode_trs.get_weights()[:2]))
  auto_decoder_trs.compile(optimizer=sgd,loss=loss)
  
  
  # In[88]:
  print "auto encoder trs okay"
  TRS_AE_H1={}
  
  for i in TRS.keys():
      TRS_AE_H1[i]=auto_decoder_trs.predict(TRS[i].todense())
      #TRS[i]=dico.transform(TRS[i][2])
  
  db["TRS_AE_H1"]=TRS_AE_H1
  
  
  db.sync()
  
  
  
  
  # In[261]:
  
  #pred_dev= model_TRS_AE.predict(TRS_AE["DEV"],batch_size=1)
  
  TRS_AE={}
  ASR_AE={}
  for i in TRS.keys():
      TRS_AE[i]=autoencode_trs.predict(TRS[i].todense())
      ASR_AE[i]=autoencode.predict(ASR[i].todense())
  
  
  db["TRS_AE_OUT"]=TRS_AE
  db["ASR_AE_OUT"]=ASR_AE
  
  db.sync()
  # # Transfert de couche
  # ICI
  # In[138]:
  print "learn transform ae H1({})".format(hidden_size)
  model_TRANS = Sequential()
  model_TRANS.add(Dense( w1_size,input_dim=hidden_size, init='glorot_uniform', activation=input_activation))
  if do_do: 
      model_TRANS.add(Dropout(0.5))
  model_TRANS.add(Dense( hidden_size,input_dim=w1_size, init='glorot_uniform', activation=input_activation))
  sgd_TRANS = SGD(lr=0.01, decay=1e-4, momentum=0.9, nesterov=True)
  #model_TRANS.compile(loss='mse', optimizer=sgd_TRANS)
  
  model_TRANS.compile(loss='mse', optimizer=sgd)
  
  
  # In[146]:
  
  model_TRANS.fit(ASR_AE_H1["TRAIN"],TRS_AE_H1["TRAIN"],nb_epoch=epochs,batch_size=batch,
                 callbacks=[keras.callbacks.EarlyStopping(monitor='val_loss',
                                                          patience=patience, verbose=0)],
                 validation_data=(ASR_AE_H1["DEV"],TRS_AE_H1["DEV"]),verbose=1)
  
  
  # In[140]:
  print "make trans projection H1"
  asr_transformer={}
  for i in ASR_AE.keys():
      asr_transformer[i]=model_TRANS.predict(ASR_AE_H1[i])
  
  db["ASR_H1_TRANFORMED_TRSH1"]=asr_transformer
  # In[ ]:
  
  db.sync()
  
  
  auto_decoder_trans=Sequential()
  auto_decoder_trans.add(Dense(w1_size,input_dim=hidden_size,activation=input_activation,weights=model_TRANS.get_weights()[:2]))
  auto_decoder_trans.compile(optimizer=sgd,loss=loss)
  
  asr_trans_w1={}
  for i in ASR_AE.keys():
      asr_trans_w1[i]=auto_decoder_trans.predict(ASR_AE_H1[i])
  db["ASR_H1_TRANSFORMED_W1"]=asr_trans_w1
  print "shape",ASR_AE["TRAIN"].shape[1]
  
  model_TRANS_H2_OUT = Sequential()
  model_TRANS_H2_OUT.add(Dense(TRS["DEV"].todense().shape[1],input_dim=hidden_size,init="glorot_uniform",activation=out_activation,weights=autoencode_trs.get_weights()[-2:]))
  sgd_out = SGD(lr=0.01, decay=1e-4, momentum=0.9, nesterov=True)
  model_TRANS_H2_OUT.compile(loss='mse', optimizer=sgd)
  
  asr_tranform_out={}
  for i in ASR_AE.keys():
      asr_tranform_out[i]=model_TRANS_H2_OUT.predict(asr_transformer[i])
  
  db["ASR_H2_TRANFORMED_OUT"]=asr_tranform_out
  db.sync()
  
  
  db.sync()
  db.close()