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FROM tensorflow/tensorflow:1.12.0-py3
ENV LANG=C.UTF-8
RUN mkdir /gpt-2
WORKDIR /gpt-2
ADD . /gpt-2
RUN pip3 install -r requirements.txt

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def create_model(is_predicting, input_ids, input_mask, segment_ids, labels,
                 num_labels):
  
  bert_module = hub.Module(
      BERT_MODEL_HUB,
      trainable=True)
  bert_inputs = dict(
      input_ids=input_ids,
      input_mask=input_mask,
      segment_ids=segment_ids)

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# This is a path to an uncased (all lowercase) version of BERT
BERT_MODEL_HUB = "https://tfhub.dev/google/bert_uncased_L-12_H-768_A-12/1"

def create_tokenizer_from_hub_module():
  """Get the vocab file and casing info from the Hub module."""
  with tf.Graph().as_default():
    bert_module = hub.Module(BERT_MODEL_HUB)
    tokenization_info = bert_module(signature="tokenization_info", as_dict=True)
    with tf.Session() as sess:
      vocab_file, do_lower_case = sess.run([tokenization_info["vocab_file"],

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train_InputExamples = train.apply(lambda x: bert.run_classifier.InputExample(guid=None,
                                                                   text_a = x[DATA_COLUMN], 
                                                                   text_b = None, 
                                                                   label = x[LABEL_COLUMN]), axis = 1)
val_InputExamples = val.apply(lambda x: bert.run_classifier.InputExample(guid=None, 
                                                                   text_a = x[DATA_COLUMN], 
                                                                   text_b = None, 
                                                                   label = x[LABEL_COLUMN]), axis = 1)

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train_InputExamples = train.apply(lambda x: bert.run_classifier.InputExample(guid=None,
                                                                   text_a = x[DATA_COLUMN], 
                                                                   text_b = None, 
                                                                   label = x[LABEL_COLUMN]), axis = 1)
val_InputExamples = val.apply(lambda x: bert.run_classifier.InputExample(guid=None, 
                                                                   text_a = x[DATA_COLUMN], 
                                                                   text_b = None, 
                                                                   label = x[LABEL_COLUMN]), axis = 1)

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# Training the model for each combination of the hyperparameters.

x_train = X_train
x_test, y_test = X_val , y_val

#A unique number for each training session
session_num = 0

#Nested for loop training with all possible  combinathon of hyperparameters
for num_units in HP_NUM_UNITS.domain.values:

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#A function to log the training process
def run(run_dir, hparams):
  with tf.summary.create_file_writer(run_dir).as_default():
    hp.hparams(hparams)  
    rmse = train_test_model(hparams)
    tf.summary.scalar(METRIC_RMSE, rmse, step=10)