text: import numpy as np
  import tensorflow as tf
  
  from tensorflow import keras
  from tensorflow.keras import layers
  from tensorflow.keras.layers import Lambda
  from tensorflow.keras.layers import Embedding
  from tensorflow.keras.layers import LSTM
  from tensorflow.keras.preprocessing.text import Tokenizer
  import tensorflow.keras.preprocessing.sequence as sequence
  from tensorflow.keras.layers import Dense, Dropout, Flatten, Reshape, Concatenate
  from tensorflow.keras.layers import Conv1D, GlobalAveragePooling1D, MaxPooling1D
  
  # This function selects the probability distribution over actions
  from baselines.common.distributions import make_pdtype
  from tensorflow.keras import backend as K
  from baselines.a2c.utils import fc
  
  MM_EMBEDDINGS_DIM = 50
  MM_MAX_WORD_SIZE = 20
  MM_MAX_SENTENCE_SIZE = 200
  MM_FEATURES_SIZE = 20000
  MM_MAX_VOCAB_SIZE = 5000
  
  MM_MAX_SENTENCE_SIZE = 200
  SMALL_VOCAB_SIZE = 50  # limit used for elements, status, weather, terrain, etc
  EXTRA_SMALL_VOCAB_SIZE = 10  # limit used for elements, status, weather, terrain, etc
  SMALL_EMBEDDINGS_DIM = 15
  EXTRA_SMALL_EMBEDDINGS_DIM = 3
  
  GUMBALL_FIELD_REMAINDER = 5
  
  # Fully connected layer
  def fc_layer(inputs, units, activation_fn=tf.nn.relu, gain=1.0):
  	return tf.layers.dense(inputs,
  							units=units,
  							activation=activation_fn,
  							kernel_initializer=tf.orthogonal_initializer(gain))
  
  
  # LSTM Layer
  #def lstm_layer(vocab_size=MM_MAX_VOCAB_SIZE, word_len_limit=MM_MAX_WORD_SIZE, input_length=MM_MAX_SENTENCE_SIZE):
  #	return LSTM(Embedding(vocab_size, word_len_limit, input_length=input_length, mask_zero=True), dropout=0.2, recurrent_dropout=0.2, return_sequences=True)
  def lstm_layer(em_input, vocab_size=MM_MAX_VOCAB_SIZE, word_len_limit=MM_MAX_WORD_SIZE, input_length=MM_MAX_SENTENCE_SIZE):
  	print('em shape', em_input.shape)
  	embedding = Embedding(vocab_size, word_len_limit, input_length=input_length )(em_input)
  	print('emb shape', embedding.shape)
  	return LSTM(units=100, dropout=0.2, recurrent_dropout=0.2, return_sequences=True)(embedding)
  
  # future
  #x_train = sequence.pad_sequences(x_train, maxlen=maxlen)   # pre_padding with 0
  
  """
  This object creates the PPO Network architecture
  """
  
  class PPOPolicy(object):
  	def __init__(self, sess, ob_space, action_space, nbatch, nsteps, reuse=False):
  		# This will use to initialize our kernels
  		gain = np.sqrt(2)
  
  		self.tokenizer = Tokenizer(num_words=5000)
  		# Based on the action space, will select what probability distribution type
  		# we will use to distribute action in our stochastic policy (in our case DiagGaussianPdType
  		# aka Diagonal Gaussian, 3D normal distribution)
  		self.pdtype = make_pdtype(action_space)
  
  		song_text_shape = ( None, 200 )
  
  
  		category_embedding_shape = ( None, 1)
  		embeddings = []
  
  
  
  		girl_1_inputs_ = tf.placeholder(tf.float32, category_embedding_shape, name="girl_1_inputs_")
  		girl_1_inputs_keras = tf.keras.layers.Input(tensor=girl_1_inputs_)
  		embedding_size = EXTRA_SMALL_EMBEDDINGS_DIM
  		embedding = Embedding(EXTRA_SMALL_VOCAB_SIZE, embedding_size, input_length=1 )(girl_1_inputs_keras)
  		embeddings.append(Reshape(target_shape=(embedding_size,))(embedding))
  
  		girl_2_inputs_ = tf.placeholder(tf.float32, category_embedding_shape, name="girl_2_inputs_")
  		girl_2_inputs_keras = tf.keras.layers.Input(tensor=girl_2_inputs_)
  		embedding_size = EXTRA_SMALL_EMBEDDINGS_DIM
  		embedding = Embedding(EXTRA_SMALL_VOCAB_SIZE, embedding_size, input_length=1 )(girl_2_inputs_keras)
  		embeddings.append(Reshape(target_shape=(embedding_size,))(embedding))
  
  		girl_3_inputs_ = tf.placeholder(tf.float32, category_embedding_shape, name="girl_3_inputs_")
  		girl_3_inputs_keras = tf.keras.layers.Input(tensor=girl_3_inputs_)
  		embedding_size = EXTRA_SMALL_EMBEDDINGS_DIM
  		embedding = Embedding(EXTRA_SMALL_VOCAB_SIZE, embedding_size, input_length=1 )(girl_3_inputs_keras)
  		embeddings.append(Reshape(target_shape=(embedding_size,))(embedding))
  
  		girl_4_inputs_ = tf.placeholder(tf.float32, category_embedding_shape, name="girl_4_inputs_")
  		girl_4_inputs_keras = tf.keras.layers.Input(tensor=girl_4_inputs_)
  		embedding_size = EXTRA_SMALL_EMBEDDINGS_DIM
  		embedding = Embedding(EXTRA_SMALL_VOCAB_SIZE, embedding_size, input_length=1 )(girl_4_inputs_keras)
  		embeddings.append(Reshape(target_shape=(embedding_size,))(embedding))
  
  		current_girl_inputs_ = tf.placeholder(tf.float32, category_embedding_shape, name="current_girl_inputs_")
  		current_girl_inputs_keras = tf.keras.layers.Input(tensor=current_girl_inputs_)
  		embedding_size = EXTRA_SMALL_EMBEDDINGS_DIM
  		embedding = Embedding(EXTRA_SMALL_VOCAB_SIZE, embedding_size, input_length=1 )(current_girl_inputs_keras)
  		embeddings.append(Reshape(target_shape=(embedding_size,))(embedding))
  
  		# Create the input placeholder
  		non_category_data_input_ = tf.placeholder(tf.float32, (None, GUMBALL_FIELD_REMAINDER), name="non_category_data_input")
  		combined_inputs_ = tf.placeholder(tf.float32, (None, ob_space.shape[1] + MM_EMBEDDINGS_DIM*2 ), name="combined_input")
  		text_inputs_ = tf.placeholder(tf.float32, song_text_shape, name="text_input")
  
  		available_moves = tf.placeholder(tf.float32, [None, action_space.n], name="availableActions")
  
  		"""
  		Build the model
  		Embedding
  		LSTM
  		3 FC for spatial dependiencies
  		1 common FC
  		1 FC for policy (actor)
  		1 FC for value (critic)
  		"""
  		with tf.variable_scope('model', reuse=reuse):
  			# text reading LSTM
  #			lt_layer = lstm_layer()
  			text_inputs_keras = tf.keras.layers.Input(tensor=text_inputs_)
  
  			text_out = lstm_layer(text_inputs_keras)
  
  			shape = text_out.get_shape().as_list() [1:]       # a list: [None, 9, 2]
  			dim = np.prod(shape)            # dim = prod(9,2) = 18
  			print('text_flatten before reshape',text_out.shape)
  			text_flatten = tf.reshape(text_out, [1, -1])           # -1 means "all"
  
  			print('embeds', len(embeddings))
  			merged = Concatenate(axis=-1)(embeddings)
  
  			# This returns a tensor
  			non_category_data_input_keras = tf.keras.layers.Input(tensor=non_category_data_input_)
  			categorical_dense = tf.keras.layers.Dense(512, activation='relu')(merged)
  			categorical_dense = Reshape(target_shape=(512,))(categorical_dense)
  			non_categorical_dense = tf.keras.layers.Dense(512, activation='relu')(non_category_data_input_keras)
  
  			combined_fields = Concatenate(axis=-1)([non_categorical_dense, categorical_dense])
  			#reshape to add dimension?
  			comb_shape = combined_fields.get_shape()
  			combined_fields = K.expand_dims(combined_fields, 2)
  			print('combined_fields expanded dim', combined_fields.get_shape())
  
  			conv1 = Conv1D(100, 10, activation='relu', batch_input_shape=(None, combined_fields.get_shape()[1]))(combined_fields)
  #			conv1 = Conv1D(100, 10, activation='relu', batch_input_shape=(None, ob_space.shape[1]))(field_inputs_)
  			conv1 = Conv1D(100, 10, activation='relu')(conv1)
  			conv1 = MaxPooling1D(3)(conv1)
  			conv1 = Conv1D(160, 10, activation='relu')(conv1)
  			conv1 = Conv1D(160, 10, activation='relu')(conv1)
  			conv1 = GlobalAveragePooling1D()(conv1)
  			conv1 = Dropout(0.5)(conv1)
  			print('conv1 before reshape',conv1.get_shape())
  			print('text_out before flatten',text_out.get_shape())
  
  			text_out = Flatten()(text_out)
  			print('text_out ater flatten',text_out.get_shape())
  			text_dense = tf.keras.layers.Dense(512, activation='relu')(text_out)
  			field_dense = tf.keras.layers.Dense(512, activation='relu')(conv1)
  			print('text_dense after dense',text_dense.get_shape())
  
  #			scaled_image = tf.keras.layers.Lambda(function=lambda tensors: tensors[0] * tensors[1])([image, scale])
  #			fc_common_dense = Lambda(lambda x:K.concatenate([x[0], x[1]], axis=1))([text_dense, field_dense])
  #			fc_common_dense = tf.keras.layers.Concatenate(axis=-1)(list([text_dense, field_dense]))
  			fc_common_dense = tf.keras.layers.Concatenate(axis=-1)(list([text_dense, field_dense]))
  			fc_common_dense = tf.keras.layers.Dense(512, activation='relu')(fc_common_dense)
  
  			#available_moves takes form [0, 0, -inf, 0, -inf...], 0 if action is available, -inf if not.
  			fc_act = tf.keras.layers.Dense(256, activation='relu')(fc_common_dense)
  #			self.pi = tf.keras.layers.Dense(action_space.n, activation='relu')(fc_act)
  			self.pi = fc(fc_act,'pi', action_space.n, init_scale = 0.01)
  
  			# Calculate the v(s)
  			h3 = tf.keras.layers.Dense(256, activation='relu')(fc_common_dense)
  			fc_vf = tf.keras.layers.Dense(1, activation=None)(h3)[:,0]
  
  #			vf = fc_layer(fc_3, 1, activation_fn=None)[:,0]
  #			vf = fc_layer(fc_common_dense, 1, activation_fn=None)[:,0]
  
  		self.initial_state = None
  
  		"""
  		# Take an action in the action distribution (remember we are in a situation
  		# of stochastic policy so we don't always take the action with the highest probability
  		# for instance if we have 2 actions 0.7 and 0.3 we have 30% channce to take the second)
  		a0 = self.pd.sample()
  		# Calculate the neg log of our probability
  		neglogp0 = self.pd.neglogp(a0)
  		"""
  
  		# perform calculations using available moves lists
  		availPi = tf.add(self.pi, available_moves)
  
  		def sample():
  			u = tf.random_uniform(tf.shape(availPi))
  			return tf.argmax(availPi - tf.log(-tf.log(u)), axis=-1)
  
  		a0 = sample()
  		el0in = tf.exp(availPi - tf.reduce_max(availPi, axis=-1, keep_dims=True))
  		z0in = tf.reduce_sum(el0in, axis=-1, keep_dims = True)
  		p0in = el0in / z0in
  		onehot = tf.one_hot(a0, availPi.get_shape().as_list()[-1])
  		neglogp0 = -tf.log(tf.reduce_sum(tf.multiply(p0in, onehot), axis=-1))
  
  
  		# Function use to take a step returns action to take and V(s)
  		def step(state_in, valid_moves, ob_texts,*_args, **_kwargs):
  			# return a0, vf, neglogp0
  			# padd text
  #			print('ob_text', ob_texts)
  			for ob_text in ob_texts:
  #				print('ob_text', ob_text)
  				self.tokenizer.fit_on_texts([ob_text])
  
  			ob_text_input = []
  			for ob_text in ob_texts:
  #				print('ob_text', ob_text)
  				token = self.tokenizer.texts_to_sequences([ob_text])
  				token = sequence.pad_sequences(token, maxlen=MM_MAX_SENTENCE_SIZE)   # pre_padding with 0
  				ob_text_input.append(token)
  #				print('token', token)
  #				print('token shape', token.shape)
  			orig_ob_text_input = np.array(ob_text_input)
  			shape = orig_ob_text_input.shape
  #			print('ob_text_input shape', shape)
  			ob_text_input = orig_ob_text_input.reshape(shape[0], shape[2])
  
  			# Reshape for conv1
  #			state_in = np.expand_dims(state_in, axis=2)
  			input_dict = dict({text_inputs_:ob_text_input, available_moves:valid_moves})
  			input_dict.update(split_categories_from_state(state_in))
  
  			return sess.run([a0,fc_vf, neglogp0], input_dict)
  
  		# Function that calculates only the V(s)
  		def value(state_in, valid_moves, ob_texts, *_args, **_kwargs):
  			for ob_text in ob_texts:
  #				print('ob_text', ob_text)
  				self.tokenizer.fit_on_texts([ob_text])
  
  			ob_text_input = []
  			for ob_text in ob_texts:
  #				print('ob_text', ob_text)
  				token = self.tokenizer.texts_to_sequences([ob_text])
  				token = sequence.pad_sequences(token, maxlen=MM_MAX_SENTENCE_SIZE)   # pre_padding with 0
  				ob_text_input.append(token)
  #				print('token', token)
  #				print('token shape', token.shape)
  			ob_text_input = np.array(ob_text_input)
  			shape = ob_text_input.shape
  #			print('ob_text_input shape', shape)
  			ob_text_input = ob_text_input.reshape(shape[0], shape[2])
  
  			# Reshape for conv1
  #			state_in = np.expand_dims(state_in, axis=2)
  			input_dict = dict({text_inputs_:ob_text_input, available_moves:valid_moves})
  			input_dict.update(split_categories_from_state(state_in))
  
  			return sess.run(fc_vf, input_dict)
  #			return sess.run(vf, {field_inputs_:state_in, text_inputs_:ob_text_input, available_moves:valid_moves})
  
  		def select_action(state_in, valid_moves, ob_texts, *_args, **_kwargs):
  			for ob_text in ob_texts:
  #				print('ob_text', ob_text)
  				self.tokenizer.fit_on_texts([ob_text])
  
  			ob_text_input = []
  			for ob_text in ob_texts:
  #				print('ob_text', ob_text)
  				token = self.tokenizer.texts_to_sequences([ob_text])
  				token = sequence.pad_sequences(token, maxlen=MM_MAX_SENTENCE_SIZE)   # pre_padding with 0
  				ob_text_input.append(token)
  #				print('token', token)
  #				print('token shape', token.shape)
  			ob_text_input = np.array(ob_text_input)
  			shape = ob_text_input.shape
  #			print('ob_text_input shape', shape)
  			ob_text_input = ob_text_input.reshape(shape[0], shape[2])
  
  			# Reshape for conv1
  #			state_in = np.expand_dims(state_in, axis=2)
  			input_dict = dict({text_inputs_:ob_text_input, available_moves:valid_moves})
  			input_dict.update(split_categories_from_state(state_in))
  
  			return sess.run(fc_vf, input_dict)
  #			return sess.run(vf, {field_inputs_:state_in, text_inputs_:ob_text_input, available_moves:valid_moves})
  
  		def split_categories_from_state(obs_datas):
  			input_mappings = {}
  			# Initialize buckets
  			current_girl = np.empty([0,1], dtype=np.float32)
  			girl_1 = np.empty([0,1], dtype=np.float32)
  			girl_2 = np.empty([0,1], dtype=np.float32)
  			girl_3 = np.empty([0,1], dtype=np.float32)
  			girl_4 = np.empty([0,1], dtype=np.float32)
  			non_category_data = np.empty([0,GUMBALL_FIELD_REMAINDER], dtype=np.float32)
  
  			input_mappings[current_girl_inputs_] = current_girl
  			input_mappings[girl_1_inputs_] = girl_1
  			input_mappings[girl_2_inputs_] = girl_2
  			input_mappings[girl_3_inputs_] = girl_3
  			input_mappings[girl_4_inputs_] = girl_4
  			input_mappings[non_category_data_input_] = non_category_data
  
  			# Everything above only happens once
  			for obs_data in obs_datas:
  
  				input_mappings[current_girl_inputs_] = np.append(input_mappings[current_girl_inputs_], np.array([[obs_data[0]]]), axis=0)
  				input_mappings[girl_1_inputs_] = np.append(input_mappings[girl_1_inputs_], np.array([[obs_data[1]]]), axis=0)
  				input_mappings[girl_2_inputs_] = np.append(input_mappings[girl_2_inputs_], np.array([[obs_data[2]]]), axis=0)
  				input_mappings[girl_3_inputs_] = np.append(input_mappings[girl_3_inputs_], np.array([[obs_data[3]]]), axis=0)
  				input_mappings[girl_4_inputs_] = np.append(input_mappings[girl_4_inputs_], np.array([[obs_data[4]]]), axis=0)
  
  				# rest of data is numeric observation
  				rest_details_index = 5
  				input_mappings[non_category_data_input_] = np.append(input_mappings[non_category_data_input_], np.array([obs_data[rest_details_index:]]), axis=0)
  
  			return input_mappings
  
  
  		self.availPi = availPi
  		self.split_categories_from_state = split_categories_from_state
  		self.text_inputs_ = text_inputs_
  		self.available_moves = available_moves
  		self.vf = fc_vf
  #		self.fc_vf = fc_vf
  		self.step = step
  		self.value = value
  		self.select_action = select_action
  		print('this did finish')
  