:type s: str
    :type word_dict: Set[str]
    :rtype: bool
18918
def word_break(s, word_dict):
    
    dp = [False] * (len(s)+1)
    dp[0] = True
    for i in range(1, len(s)+1):
        for j in range(0, i):
            if dp[j] and s[j:i] in word_dict:
                dp[i] = True
                break
    return dp[-1]
482352
def p_word_list(p):
    ''''''
    parserobj = p.context
    if len(p) == 2:
        p[0] = [_expandword(parserobj, p.slice[1])]
    else:
        p[0] = p[1]
        p[0].append(_expandword(parserobj, p.slice[2]))
1210393
def encodeSentence(self, *words):
        
        encoded = map(self.encodeWord, words)
        encoded = b''.join(encoded)
        # append EOS (end of sentence) byte
        encoded += b'\x00'
        return encoded
15284
def get_subwords(self, word, on_unicode_error='strict'):
        
        pair = self.f.getSubwords(word, on_unicode_error)
        return pair[0], np.array(pair[1])
241246
def set_embeddings(self, embeddings):
        '''
        
        '''
        if self.embeddings_ is not None:
            raise Exception("You have already set embeddings by running set_embeddings or set_embeddings_model.")
        assert embeddings.shape[1] == self.corpus_.get_num_terms()
        self.embeddings_ = embeddings.T
        self.vocab_ = self.corpus_.get_terms()
        return self
306280
def word(ctx, text, number, by_spaces=False):
    
    return word_slice(ctx, text, number, conversions.to_integer(number, ctx) + 1, by_spaces)
298841
def _setup(self, word) -> List[str]:
        
        if len(word) == 1:
            return [word]
        for prefix in self.constants.PREFIXES:
            if word.startswith(prefix):
                (first, rest) = string_utils.split_on(word, prefix)
                if self._contains_vowels(rest):
                    return string_utils.remove_blank_spaces(
                        self._process(first) + self._process(rest))
                # a word like pror can happen from ellision
                return string_utils.remove_blank_spaces(self._process(word))
        if word in self.constants.UI_EXCEPTIONS.keys():
            return self.constants.UI_EXCEPTIONS[word]
        return string_utils.remove_blank_spaces(self._process(word))
1277360
def inflect(word: str,
            count: int):
    
    if word in ['time', 'sentence']:
        return word if count == 1 else word + 's'
    elif word == 'was':
        return 'was' if count == 1 else 'were'
    else:
        return word + '(s)'
426104
def distinct_words(string_matrix: List[List[str]]) -> Set[str]:
    
    return set([word
                for sentence in string_matrix
                for word in sentence])
333749
def from_dict(raw_data):
        """"""
        try:
            set_id = raw_data['id']
            title = raw_data['title']
            terms = [Term.from_dict(term) for term in raw_data['terms']]
            return WordSet(set_id, title, terms)
        except KeyError:
            raise ValueError('Unexpected set json structure')
528337
def set(i)
      check_range(i)
      w = self.word_index(i)
      result = @words[w] |= (1 << (i % BITS_PER_WORD))
      result
    end
775289
def vocab_values
      type = Schema.find(self.schema.prefix, stix_version).find_type(name.gsub("Vocab", "Enum"))

      if type
        type.enumeration_values
      else
        raise "Unable to find corresponding enumeration for vocabulary"
      end
    end
952852
def as_dict(self) -> Dict[str, str]:
        
        items: Dict[str, str] = {}

        for k, v in self.items():

            if type(v) is str:
                items.update({k: v})

        return items
1133185
def get_grammatically_correct_vocabulary_subset(self, text,
                                                    sent_filter='combined'):
        
        tokens = word_tokenize(text)
        sent_tokens = get_partial_sentence(tokens)

        if not sent_tokens:
            return self.vocabulary

        if sent_filter == 'combined':
            if len(sent_tokens) < 2:
                return self.get_bigram_filtered_vocab(sent_tokens)

            combined_filters = self.get_pos_filtered_vocab(sent_tokens) + \
                self.get_trigram_filtered_vocab(sent_tokens) + \
                self.get_bigram_filtered_vocab(sent_tokens)
            return combined_filters

        if sent_filter == 'pos' and len(sent_tokens) > 1:
            return self.get_pos_filtered_vocab(sent_tokens)
        elif sent_filter == 'bigram' or len(sent_tokens) < 2:
            return self.get_bigram_filtered_vocab(sent_tokens)
        elif sent_filter == 'trigram':
            return self.get_trigram_filtered_vocab(sent_tokens)
1245135
def compare_words_lexicographic( word_a, word_b ):
    
    # sanity check for words to be all Tamil
    if ( not all_tamil(word_a) ) or (not all_tamil(word_b)) :
        #print("## ")
        #print(word_a)
        #print(word_b)
        #print("Both operands need to be Tamil words")
        pass
    La = len(word_a)
    Lb = len(word_b)
    all_TA_letters = u"".join(tamil_letters)
    for itr in range(0,min(La,Lb)):
            pos1 = all_TA_letters.find( word_a[itr] )
            pos2 = all_TA_letters.find( word_b[itr] )

            if pos1 != pos2 :
                    #print  not( pos1 > pos2), pos1, pos2
                    return cmp(pos1, pos2)

    # result depends on if La is shorter than Lb, or 0 if La == Lb  i.e. cmp
    return cmp(La,Lb)
789708
def combine_word_list(word_list):
    
    bag_of_words = collections.defaultdict(int)

    for word in word_list:
        bag_of_words[word] += 1

    return bag_of_words
989385
def _is_word_type(token_type):
    """"""
    return token_type in [TokenType.Word,
                          TokenType.QuotedLiteral,
                          TokenType.UnquotedLiteral,
                          TokenType.Number,
                          TokenType.Deref]