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soliditygen.py
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soliditygen.py
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import tensorflow as tf
from tensorflow.keras.layers.experimental import preprocessing
import numpy as np
import os
import time
import re
import json
comment_re = re.compile(
r'(^)?[^\S\n]*/(?:\*(.*?)\*/[^\S\n]*|/[^\n]*)($)?',
re.DOTALL | re.MULTILINE
)
def comment_replacer(match):
start,mid,end = match.group(1,2,3)
if mid is None:
# single line comment
return ''
elif start is not None or end is not None:
# multi line comment at start or end of a line
return ''
elif '\n' in mid:
# multi line comment with line break
return '\n'
else:
# multi line comment without line break
return ' '
def remove_comments(text):
return comment_re.sub(comment_replacer, text)
def cleanup(textIn):
tx = []
for line in remove_comments(textIn).split("\n"):
lineStrip = line.strip()
if not len(lineStrip): continue
if any(lineStrip.startswith(s) for s in ["//", "pragma ", "import "] ): continue
tx.append(line)
return '\n'.join(tx)
def nextFile():
baseurl = "https://raw.githubusercontent.com/tintinweb/smart-contract-sanctuary/master/contracts/mainnet/%s/%s.sol"
index_file = tf.keras.utils.get_file('contracts.json', 'https://github.com/tintinweb/smart-contract-sanctuary/blob/3c4e1fe4672177eea850cda031c5b779f707b2ec/contracts/mainnet/contracts.json?raw=true')
with open(index_file,'r') as f:
for nr,line in enumerate(f):
if not line.strip(): continue
linej = json.loads(line)
ftarget = linej["address"].replace("0x","")
ffolder = ftarget[:2].lower()
fname = linej["name"]
ftotal = baseurl % (ffolder, "%s_%s"%(ftarget, fname))
yield ftotal
class TrainingData(object):
def __init__(self, text=None):
self.text = text
if not self.text or len(self.text)==0:
return # dummy object, can only load existing predictionmodel
self.len = len(text)
self.vocab = sorted(set(self.text))
self.vocab_size = len(self.vocab)
self.ids_from_chars = preprocessing.StringLookup(
vocabulary=list(self.vocab), mask_token=None)
self.chars_from_ids = tf.keras.layers.experimental.preprocessing.StringLookup(
vocabulary=self.ids_from_chars.get_vocabulary(), invert=True, mask_token=None)
self.all_ids = self.ids_from_chars(tf.strings.unicode_split(self.text, 'UTF-8'))
self.ids_dataset = tf.data.Dataset.from_tensor_slices(self.all_ids)
def text_from_ids(self, ids):
return tf.strings.reduce_join(self.chars_from_ids(ids), axis=-1)
def split_input_target(self, sequence):
input_text = sequence[:-1]
target_text = sequence[1:]
return input_text, target_text
def getSampledDataset(self, seq_length, batch_size=64, buffer_size=10000):
self.seq_length = seq_length
self.examples_per_epoch = len(self.text)//(self.seq_length+1)
self.sequences = self.ids_dataset.batch(self.seq_length+1, drop_remainder=True)
self.dataset = self.sequences.map(self.split_input_target)
return (
self.dataset
.shuffle(buffer_size)
.batch(batch_size, drop_remainder=True)
.prefetch(tf.data.experimental.AUTOTUNE)
)
def newModel(self, embedding_dim=256, rnn_units=1024):
self.model = MyModel(
# Be sure the vocabulary size matches the `StringLookup` layers.
vocab_size=len(self.ids_from_chars.get_vocabulary()),
embedding_dim=embedding_dim,
rnn_units=rnn_units)
loss = tf.losses.SparseCategoricalCrossentropy(from_logits=True)
self.model.compile(optimizer='adam', loss=loss)
return self.model
def train(self, dataset, epochs=15):
# Directory where the checkpoints will be saved
checkpoint_dir = './training_checkpoints'
# Name of the checkpoint files
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt_{epoch}")
checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
filepath=checkpoint_prefix,
save_weights_only=True)
self.history = self.model.fit(dataset, epochs=epochs, callbacks=[checkpoint_callback])
self.predictionModel = OneStep(self.model, self.chars_from_ids, self.ids_from_chars)
def predict(self, inputs=['contract ', 'contract ', 'abstract ', 'interface ', 'library '], targetLen=3000):
assert(self.predictionModel)
states = None
next_char = tf.constant(inputs)
result = [next_char]
for _ in range(targetLen):
next_char, states = self.predictionModel.generate_one_step(next_char, states=states)
result.append(next_char)
result = tf.strings.join(result)
return result[0].numpy().decode('utf-8')
def save_model(self, name="one_step"):
tf.saved_model.save(self.predictionModel, name)
def load_model(self, name="one_step"):
self.predictionModel = tf.saved_model.load(name)
class SolidityTrainer(object):
@staticmethod
def get_training_data(maxfiles=1000, maxlen=10_000_000):
total = []
path_to_file = ""
for nr,dlink in enumerate(nextFile()):
if path_to_file: os.unlink(path_to_file)
path_to_file = tf.keras.utils.get_file("temp", dlink)
textIn = open(path_to_file, 'rb').read().decode(encoding='utf-8')
textIn = cleanup(textIn)
total.append(textIn)
if nr >= maxfiles: break
if len("\n\n".join(total)) >= maxlen: break
text = "\n\n".join(total)
return TrainingData(text)
class MyModel(tf.keras.Model):
def __init__(self, vocab_size, embedding_dim, rnn_units):
super().__init__(self)
self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)
self.gru = tf.keras.layers.GRU(rnn_units,
return_sequences=True,
return_state=True)
self.dense = tf.keras.layers.Dense(vocab_size)
def call(self, inputs, states=None, return_state=False, training=False):
x = inputs
x = self.embedding(x, training=training)
if states is None:
states = self.gru.get_initial_state(x)
x, states = self.gru(x, initial_state=states, training=training)
x = self.dense(x, training=training)
if return_state:
return x, states
else:
return x
class OneStep(tf.keras.Model):
def __init__(self, model, chars_from_ids, ids_from_chars, temperature=1.0):
super().__init__()
self.temperature = temperature
self.model = model
self.chars_from_ids = chars_from_ids
self.ids_from_chars = ids_from_chars
# Create a mask to prevent "[UNK]" from being generated.
skip_ids = self.ids_from_chars(['[UNK]'])[:, None]
sparse_mask = tf.SparseTensor(
# Put a -inf at each bad index.
values=[-float('inf')]*len(skip_ids),
indices=skip_ids,
# Match the shape to the vocabulary
dense_shape=[len(ids_from_chars.get_vocabulary())])
self.prediction_mask = tf.sparse.to_dense(sparse_mask)
@tf.function
def generate_one_step(self, inputs, states=None):
# Convert strings to token IDs.
input_chars = tf.strings.unicode_split(inputs, 'UTF-8')
input_ids = self.ids_from_chars(input_chars).to_tensor()
# Run the model.
# predicted_logits.shape is [batch, char, next_char_logits]
predicted_logits, states = self.model(inputs=input_ids, states=states,
return_state=True)
# Only use the last prediction.
predicted_logits = predicted_logits[:, -1, :]
predicted_logits = predicted_logits/self.temperature
# Apply the prediction mask: prevent "[UNK]" from being generated.
predicted_logits = predicted_logits + self.prediction_mask
# Sample the output logits to generate token IDs.
predicted_ids = tf.random.categorical(predicted_logits, num_samples=1)
predicted_ids = tf.squeeze(predicted_ids, axis=-1)
# Convert from token ids to characters
predicted_chars = self.chars_from_ids(predicted_ids)
# Return the characters and model state.
return predicted_chars, states