urialon

"""traverse all the datastore vectors, delete the ones that
are never hit (excluding itself)
"""

import argparse
import numpy as np
import faiss
import ctypes
import time
import pickle

urialon

from typing import Union, Tuple, Optional
from torch_geometric.typing import (OptPairTensor, Adj, Size, NoneType,
                                    OptTensor)

import torch
from torch import Tensor
import torch.nn.functional as F
from torch.nn import Parameter, Linear
from torch_sparse import SparseTensor, set_diag
from torch_geometric.nn.conv import MessagePassing

urialon

{
  "accuracy": 1.0,
  "accuracy_up_to_parametric_type": 1.0,
  "count": 29092,
  "count_on_lattice": 29092,
  "normalized_least_upper_bound_depth": 1.0,
  "per_type_stats": {
    "A": {
      "accuracy": 1.0,
      "accuracy_up_to_parametric_type": 1.0,

urialon

from argparse import ArgumentParser
import nltk
nltk.download('punkt')

def process_seq(seq):
    tokens = nltk.word_tokenize(seq.lower())
    return tokens

def load_align(align_file):
    result = {}

urialon

from argparse import ArgumentParser
import os
import json

if __name__ == '__main__':
    parser = ArgumentParser()
    parser.add_argument("-in", "--input", dest="input_file",
                        help="path to input file", required=True)
    parser.add_argument("-out", "--output", dest="output_dir", required=True)
    args = parser.parse_args()

urialon

            if self.config.LABEL_SMOOTHING == 0:
                loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels,
                                                                            logits=logits)  
            else:
                onehot_labels = tf.one_hot(indices=labels, depth=tf.shape(logits)[-1])
                loss = tf.losses.softmax_cross_entropy(
                    onehot_labels=onehot_labels,
                    logits=logits,
                    label_smoothing=self.config.LABEL_SMOOTHING, # typically 0.1
                    reduction=Reduction.NONE)  # (batch, time)

urialon

from argparse import ArgumentParser
import os
import pickle
import tensorflow as tf
from tensorflow.contrib.framework.python.framework import checkpoint_utils

vars_to_rename = {
    # 'model/old_name': 'model/new_name',
    'model/decoder/attention_wrapper/lstm_cell/bias': 'model/decoder/attention_wrapper/multi_rnn_cell/cell_0/lstm_cell/bias',
    'model/decoder/attention_wrapper/lstm_cell/kernel': 'model/decoder/attention_wrapper/multi_rnn_cell/cell_0/lstm_cell/kernel',

urialon

from argparse import ArgumentParser
import os
import git
import json
import gzip

# Based on Appendix D in: https://arxiv.org/pdf/1711.00740.pdf
# And preprocessed dataset in: https://aka.ms/iclr18-prog-graphs-dataset
repos_train = {
    'akka.net': ('https://github.com/akkadotnet/akka.net', '719335a1'),