from ngrammatcher import NGramMatcher

# init NgramMatcher object
ngm = NGramMatcher()

# add ngrams and their corresponding data
ngm.insert_ngram(['programming','language'], 'programming language')
ngm.insert_ngram(['Python'], 'Python')

# match ngrams
ngm.match_ngrams(['Python', 'is', 'a', 'programming', 'language'])
# ['Python', 'programming language']

from ngrammatcher import NGramMatcher
ngm = NGramMatcher()

# You can add n-grams of any size
ngm.insert_ngram(['programming','language'], 'programming language') # 2-gram
ngm.insert_ngram(['Python'], 'Python') # 1-gram
ngm.insert_ngram(['a']*10000, 'a'*10000) # 10_000-gram

# you can map any kind of data to an n-gram
data = {
    'word': 'programming language',
    'wikipedia': 'https://en.wikipedia.org/wiki/Programming_language'
    'desc': 'A programming language is any set of rules that converts...'
}
ngm.insert_ngram(['programming', 'language'], data)

# you can also insert n-grams using dictionary sytax
ngm[['c','plus','plus']] = 'c++'

# or add words
ngm.insert_ngram(list('test'), 'test')

from ngrammatcher import NGramMatcher
ngm = NGramMatcher()
ngm.insert_ngram(['programming','language'], 'programming language')
ngm.insert_ngram(['Python'], 'Python')

# here we will use spacy to create tokens
import spacy
nlp = spacy.load('en_core_web_lg')
text = 'Python is a programming language'

tokens = [tok.text for tok in nlp(text)]

# find n-grams
ngm.match_ngrams(tokens)
# ['Python', 'programming language']

from ngrammatcher import NGramMatcher
ngm = NGramMatcher()
ngm.insert_ngram(['programming','language'], 'programming language')
ngm.insert_ngram(['Python'], 'Python')

# get all n-grams in the trie
ngm.get_all_ngrams()
# [(['Python'], 'Python'), (['programming', 'language'], 'programming language')]

# you can exclude the data object too
ngm.get_all_ngrams(keys_only=True)
# [['Python'], ['programming', 'language']]

# delete n-grams (returns True if deleted, False otherwise)
ngm.delete_ngram(['Python'])
# True


# Additional Quality-of-Life functionality
len(ngm) # get the number of n-grams in trie

['programming', 'language'] in ngm # check if n-gram is in trie

ngm[['programming', 'language']] = 'PL' # insert an n-gram into the trie

ngm[['programming', 'language']] # get the data for a specific n-gram

del ngm[['programming', 'language']] # delete an ngram using del