model=gensim.models.KeyedVectors.load_word2vec_format(
    fname='/home/zhlin/GloVe/vectors.txt',binary=False
)
words=model.vocab
with open('vocab.txt','w') as f:
    f.write('<S>')
    f.write('\n'）
    f.write('</S>')
    f.write('\n')
    f.write('<UNK>')
    f.write('\n')    # bilm-tf 要求vocab有這三個符號，並且在最前面
    for word in words:
        f.write(word)
        f.write('\n')

import tensorflow as tf
import os
from bilm import TokenBatcher, BidirectionalLanguageModel, weight_layers, \
    dump_token_embeddings

# Our small dataset.
raw_context = [
    '這 是 測試 .',
    '好的 .'
]
tokenized_context = [sentence.split() for sentence in raw_context]
tokenized_question = [
    ['這', '是', '什麼'],
]

vocab_file='/home/zhlin/bilm-tf/glove_embedding_vocab8.10/vocab.txt'
options_file='/home/zhlin/bilm-tf/try/options.json'
weight_file='/home/zhlin/bilm-tf/try/weights.hdf5'
token_embedding_file='/home/zhlin/bilm-tf/glove_embedding_vocab8.10/vocab_embedding.hdf5'

## Now we can do inference.
# Create a TokenBatcher to map text to token ids.
batcher = TokenBatcher(vocab_file)

# Input placeholders to the biLM.
context_token_ids = tf.placeholder('int32', shape=(None, None))
question_token_ids = tf.placeholder('int32', shape=(None, None))

# Build the biLM graph.
bilm = BidirectionalLanguageModel(
    options_file,
    weight_file,
    use_character_inputs=False,
    embedding_weight_file=token_embedding_file
)

# Get ops to compute the LM embeddings.
context_embeddings_op = bilm(context_token_ids)
question_embeddings_op = bilm(question_token_ids)

elmo_context_input = weight_layers('input', context_embeddings_op, l2_coef=0.0)
with tf.variable_scope('', reuse=True):
    # the reuse=True scope reuses weights from the context for the question
    elmo_question_input = weight_layers(
        'input', question_embeddings_op, l2_coef=0.0
    )

elmo_context_output = weight_layers(
    'output', context_embeddings_op, l2_coef=0.0
)
with tf.variable_scope('', reuse=True):
    # the reuse=True scope reuses weights from the context for the question
    elmo_question_output = weight_layers(
        'output', question_embeddings_op, l2_coef=0.0
    )


with tf.Session() as sess:
    # It is necessary to initialize variables once before running inference.
    sess.run(tf.global_variables_initializer())

    # Create batches of data.
    context_ids = batcher.batch_sentences(tokenized_context)
    question_ids = batcher.batch_sentences(tokenized_question)

    # Compute ELMo representations (here for the input only, for simplicity).
    elmo_context_input_, elmo_question_input_ = sess.run(
        [elmo_context_input['weighted_op'], elmo_question_input['weighted_op']],
        feed_dict={context_token_ids: context_ids,
                   question_token_ids: question_ids}
    )

print(elmo_context_input_,elmo_context_input_)