"""
Small script creating the models for the N-Gramm Viewer containing all
the different n-gramm data.
"""

corpus_type_list = ["lm_ns_year", "tk_ws_year", "lm_ns_speaker", "tk_ws_speaker"]
sort_key_list = ([i for i in range(10)]
                 + "A B C D E F G H I J K L M N O P Q R S T U V W X Y Z".split()
                 + ["_Non_ASCII"])
ngram_kinds = ["One", "Two", "Three", "Four", "Five"]

template_class = """
class Key{}_{}Gram_{}(models.Model):
    ngram = models.CharField(verbose_name='{}Gram',
                             max_length=255,
                             default=None,
                             null=True,
                             blank=True)
    key = models.CharField(max_length=255)
    count = models.IntegerField()

    def __str__(self):
        return str(self.ngram) + " " + str(self.key)
                 """

classes = []
for corpus_type in corpus_type_list:
    for ngram_kind in ngram_kinds:
        for key in sort_key_list:
            cls = template_class.format(key, ngram_kind, corpus_type,
                                        ngram_kind)
            classes.append(cls)

with open("classes.txt", "w") as file:
    for cls in classes:
        file.write("{}\n".format(cls))