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from itertools import chain
from typing import Optional, Dict, Iterable, Set, NamedTuple
import pickle
import os
from enum import Enum
from config import Config
import tensorflow as tf
from argparse import Namespace
from common import common
class VocabType(Enum):
Token = 1
Target = 2
Path = 3
SpecialVocabWordsType = Namespace
_SpecialVocabWords_OnlyOov = Namespace(
OOV='<OOV>'
)
_SpecialVocabWords_SeparateOovPad = Namespace(
PAD='<PAD>',
OOV='<OOV>'
)
_SpecialVocabWords_JoinedOovPad = Namespace(
PAD_OR_OOV='<PAD_OR_OOV>',
PAD='<PAD_OR_OOV>',
OOV='<PAD_OR_OOV>'
)
class Vocab:
def __init__(self, vocab_type: VocabType, words: Iterable[str],
special_words: Optional[SpecialVocabWordsType] = None):
if special_words is None:
special_words = Namespace()
self.vocab_type = vocab_type
self.word_to_index: Dict[str, int] = {}
self.index_to_word: Dict[int, str] = {}
self._word_to_index_lookup_table = None
self._index_to_word_lookup_table = None
self.special_words: SpecialVocabWordsType = special_words
for index, word in enumerate(chain(common.get_unique_list(special_words.__dict__.values()), words)):
self.word_to_index[word] = index
self.index_to_word[index] = word
self.size = len(self.word_to_index)
def save_to_file(self, file):
# Notice: From historical reasons, a saved vocab doesn't include special words.
special_words_as_unique_list = common.get_unique_list(self.special_words.__dict__.values())
nr_special_words = len(special_words_as_unique_list)
word_to_index_wo_specials = {word: idx for word, idx in self.word_to_index.items() if idx >= nr_special_words}
index_to_word_wo_specials = {idx: word for idx, word in self.index_to_word.items() if idx >= nr_special_words}
size_wo_specials = self.size - nr_special_words
pickle.dump(word_to_index_wo_specials, file)
pickle.dump(index_to_word_wo_specials, file)
pickle.dump(size_wo_specials, file)
@classmethod
def load_from_file(cls, vocab_type: VocabType, file, special_words: SpecialVocabWordsType) -> 'Vocab':
special_words_as_unique_list = common.get_unique_list(special_words.__dict__.values())
# Notice: From historical reasons, a saved vocab doesn't include special words,
# so they should be added upon loading.
word_to_index_wo_specials = pickle.load(file)
index_to_word_wo_specials = pickle.load(file)
size_wo_specials = pickle.load(file)
assert len(index_to_word_wo_specials) == len(word_to_index_wo_specials) == size_wo_specials
min_word_idx_wo_specials = min(index_to_word_wo_specials.keys())
if min_word_idx_wo_specials != len(special_words_as_unique_list):
raise ValueError(
"Error while attempting to load vocabulary `{vocab_type}` from file `{file_path}`. "
"The stored vocabulary has minimum word index {min_word_idx}, "
"while expecting minimum word index to be {nr_special_words} "
"because having to use {nr_special_words} special words, which are: {special_words}. "
"Please check the parameter `config.SEPARATE_OOV_AND_PAD`.".format(
vocab_type=vocab_type, file_path=file.name, min_word_idx=min_word_idx_wo_specials,
nr_special_words=len(special_words_as_unique_list), special_words=special_words))
vocab = cls(vocab_type, [], special_words)
vocab.word_to_index = {**word_to_index_wo_specials,
**{word: idx for idx, word in enumerate(special_words_as_unique_list)}}
vocab.index_to_word = {**index_to_word_wo_specials,
**{idx: word for idx, word in enumerate(special_words_as_unique_list)}}
vocab.size = size_wo_specials + len(special_words_as_unique_list)
return vocab
@classmethod
def create_from_freq_dict(cls, vocab_type: VocabType, word_to_count: Dict[str, int], max_size: int,
special_words: Optional[SpecialVocabWordsType] = None):
if special_words is None:
special_words = Namespace()
words_sorted_by_counts = sorted(word_to_count, key=word_to_count.get, reverse=True)
words_sorted_by_counts_and_limited = words_sorted_by_counts[:max_size]
return cls(vocab_type, words_sorted_by_counts_and_limited, special_words)
@staticmethod
def _create_word_to_index_lookup_table(word_to_index: Dict[str, int], default_value: int):
return tf.lookup.StaticHashTable(
tf.lookup.KeyValueTensorInitializer(
list(word_to_index.keys()), list(word_to_index.values()), key_dtype=tf.string, value_dtype=tf.int32),
default_value=tf.constant(default_value, dtype=tf.int32))
@staticmethod
def _create_index_to_word_lookup_table(index_to_word: Dict[int, str], default_value: str) \
-> tf.lookup.StaticHashTable:
return tf.lookup.StaticHashTable(
tf.lookup.KeyValueTensorInitializer(
list(index_to_word.keys()), list(index_to_word.values()), key_dtype=tf.int32, value_dtype=tf.string),
default_value=tf.constant(default_value, dtype=tf.string))
def get_word_to_index_lookup_table(self) -> tf.lookup.StaticHashTable:
if self._word_to_index_lookup_table is None:
self._word_to_index_lookup_table = self._create_word_to_index_lookup_table(
self.word_to_index, default_value=self.word_to_index[self.special_words.OOV])
return self._word_to_index_lookup_table
def get_index_to_word_lookup_table(self) -> tf.lookup.StaticHashTable:
if self._index_to_word_lookup_table is None:
self._index_to_word_lookup_table = self._create_index_to_word_lookup_table(
self.index_to_word, default_value=self.special_words.OOV)
return self._index_to_word_lookup_table
def lookup_index(self, word: tf.Tensor) -> tf.Tensor:
return self.get_word_to_index_lookup_table().lookup(word)
def lookup_word(self, index: tf.Tensor) -> tf.Tensor:
return self.get_index_to_word_lookup_table().lookup(index)
WordFreqDictType = Dict[str, int]
class Code2VecWordFreqDicts(NamedTuple):
token_to_count: WordFreqDictType
path_to_count: WordFreqDictType
target_to_count: WordFreqDictType
class Code2VecVocabs:
def __init__(self, config: Config):
self.config = config
self.token_vocab: Optional[Vocab] = None
self.path_vocab: Optional[Vocab] = None
self.target_vocab: Optional[Vocab] = None
# Used to avoid re-saving a non-modified vocabulary to a path it is already saved in.
self._already_saved_in_paths: Set[str] = set()
self._load_or_create()
def _load_or_create(self):
assert self.config.is_training or self.config.is_loading
if self.config.is_loading:
vocabularies_load_path = self.config.get_vocabularies_path_from_model_path(self.config.MODEL_LOAD_PATH)
if not os.path.isfile(vocabularies_load_path):
raise ValueError(
"Model dictionaries file is not found in model load dir. "
"Expecting file `{vocabularies_load_path}`.".format(vocabularies_load_path=vocabularies_load_path))
self._load_from_path(vocabularies_load_path)
else:
self._create_from_word_freq_dict()
def _load_from_path(self, vocabularies_load_path: str):
assert os.path.exists(vocabularies_load_path)
self.config.log('Loading model vocabularies from: `%s` ... ' % vocabularies_load_path)
with open(vocabularies_load_path, 'rb') as file:
self.token_vocab = Vocab.load_from_file(
VocabType.Token, file, self._get_special_words_by_vocab_type(VocabType.Token))
self.target_vocab = Vocab.load_from_file(
VocabType.Target, file, self._get_special_words_by_vocab_type(VocabType.Target))
self.path_vocab = Vocab.load_from_file(
VocabType.Path, file, self._get_special_words_by_vocab_type(VocabType.Path))
self.config.log('Done loading model vocabularies.')
self._already_saved_in_paths.add(vocabularies_load_path)
def _create_from_word_freq_dict(self):
word_freq_dict = self._load_word_freq_dict()
self.config.log('Word frequencies dictionaries loaded. Now creating vocabularies.')
self.token_vocab = Vocab.create_from_freq_dict(
VocabType.Token, word_freq_dict.token_to_count, self.config.MAX_TOKEN_VOCAB_SIZE,
special_words=self._get_special_words_by_vocab_type(VocabType.Token))
self.config.log('Created token vocab. size: %d' % self.token_vocab.size)
self.path_vocab = Vocab.create_from_freq_dict(
VocabType.Path, word_freq_dict.path_to_count, self.config.MAX_PATH_VOCAB_SIZE,
special_words=self._get_special_words_by_vocab_type(VocabType.Path))
self.config.log('Created path vocab. size: %d' % self.path_vocab.size)
self.target_vocab = Vocab.create_from_freq_dict(
VocabType.Target, word_freq_dict.target_to_count, self.config.MAX_TARGET_VOCAB_SIZE,
special_words=self._get_special_words_by_vocab_type(VocabType.Target))
self.config.log('Created target vocab. size: %d' % self.target_vocab.size)
def _get_special_words_by_vocab_type(self, vocab_type: VocabType) -> SpecialVocabWordsType:
if not self.config.SEPARATE_OOV_AND_PAD:
return _SpecialVocabWords_JoinedOovPad
if vocab_type == VocabType.Target:
return _SpecialVocabWords_OnlyOov
return _SpecialVocabWords_SeparateOovPad
def save(self, vocabularies_save_path: str):
if vocabularies_save_path in self._already_saved_in_paths:
return
with open(vocabularies_save_path, 'wb') as file:
self.token_vocab.save_to_file(file)
self.target_vocab.save_to_file(file)
self.path_vocab.save_to_file(file)
self._already_saved_in_paths.add(vocabularies_save_path)
def _load_word_freq_dict(self) -> Code2VecWordFreqDicts:
assert self.config.is_training
self.config.log('Loading word frequencies dictionaries from: %s ... ' % self.config.word_freq_dict_path)
with open(self.config.word_freq_dict_path, 'rb') as file:
token_to_count = pickle.load(file)
path_to_count = pickle.load(file)
target_to_count = pickle.load(file)
self.config.log('Done loading word frequencies dictionaries.')
# assert all(isinstance(item, WordFreqDictType) for item in {token_to_count, path_to_count, target_to_count})
return Code2VecWordFreqDicts(
token_to_count=token_to_count, path_to_count=path_to_count, target_to_count=target_to_count)
def get(self, vocab_type: VocabType) -> Vocab:
if not isinstance(vocab_type, VocabType):
raise ValueError('`vocab_type` should be `VocabType.Token`, `VocabType.Target` or `VocabType.Path`.')
if vocab_type == VocabType.Token:
return self.token_vocab
if vocab_type == VocabType.Target:
return self.target_vocab
if vocab_type == VocabType.Path:
return self.path_vocab