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if BUCKET_NAME:
  !gsutil -m cp -r $MODEL_DIR $PRETRAINING_DIR gs://$BUCKET_NAME

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BUCKET_NAME = "bert_resourses" #@param {type:"string"}
MODEL_DIR = "bert_model" #@param {type:"string"}
PRETRAINING_DIR = "pretraining_data" #@param {type:"string"}
VOC_FNAME = "vocab.txt" #@param {type:"string"}

# Input data pipeline config
TRAIN_BATCH_SIZE = 128 #@param {type:"integer"}
MAX_PREDICTIONS = 20 #@param {type:"integer"}
MAX_SEQ_LENGTH = 128 #@param {type:"integer"}
MASKED_LM_PROB = 0.15 #@param

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model_fn = model_fn_builder(
      bert_config=bert_config,
      init_checkpoint=INIT_CHECKPOINT,
      learning_rate=LEARNING_RATE,
      num_train_steps=TRAIN_STEPS,
      num_warmup_steps=10,
      use_tpu=USE_TPU,
      use_one_hot_embeddings=True)

tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(TPU_ADDRESS)

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estimator.train(input_fn=train_input_fn, max_steps=TRAIN_STEPS)

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import os
import tensorflow as tf

from bert_serving.server.graph import optimize_graph
from bert_serving.server.helper import get_args_parser


MODEL_DIR = '/content/wwm_uncased_L-24_H-1024_A-16/' #@param {type:"string"}
GRAPH_DIR = '/content/graph/' #@param {type:"string"}
GRAPH_OUT = 'extractor.pbtxt' #@param {type:"string"}

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def model_fn(features, mode):
    with tf.gfile.GFile(GRAPH_PATH, 'rb') as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())
        
    output = tf.import_graph_def(graph_def,
                                 input_map={k + ':0': features[k] 
                                            for k in INPUT_NAMES},
                                 return_elements=['final_encodes:0'])

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INPUT_NAMES = ['input_ids', 'input_mask', 'input_type_ids']
bert_tokenizer = FullTokenizer(VOCAB_PATH)

def build_feed_dict(texts):
    
    text_features = list(convert_lst_to_features(
        texts, SEQ_LEN, SEQ_LEN, 
        bert_tokenizer, log, False, False))

    target_shape = (len(texts), -1)

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def build_input_fn(container):
    
    def gen():
        while True:
          try:
            yield build_feed_dict(container.get())
          except StopIteration:
            yield build_feed_dict(container.get())

    def input_fn():

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def batch(iterable, n=1):
    l = len(iterable)
    for ndx in range(0, l, n):
        yield iterable[ndx:min(ndx + n, l)]

def build_vectorizer(_estimator, _input_fn_builder, batch_size=128):
  container = DataContainer()
  predict_fn = _estimator.predict(_input_fn_builder(container), yield_single_examples=False)
  
  def vectorize(text, verbose=False):

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dim = 1024
graph = tf.Graph()
sess = tf.InteractiveSession(graph=graph)

Q = tf.placeholder("float", [dim])
S = tf.placeholder("float", [None, dim])