def tokenize()

in pytorch_transformers/tokenization_utils.py [0:0]

• 40 lines of code
• 16 McCabe index (conditional complexity)

    def tokenize(self, text, **kwargs):
        """ Converts a string in a sequence of tokens (string), using the tokenizer.
            Split in words for word-based vocabulary or sub-words for sub-word-based
            vocabularies (BPE/SentencePieces/WordPieces).

            Take care of added tokens.
        """
        def split_on_token(tok, text):
            result = []
            split_text = text.split(tok)
            for i, sub_text in enumerate(split_text):
                sub_text = sub_text.strip()
                if i == 0 and not sub_text:
                    result += [tok]
                elif i == len(split_text) - 1:
                    if sub_text:
                        result += [sub_text]
                    else:
                        pass
                else:
                    if sub_text:
                        result += [sub_text]
                    result += [tok]
            return result

        def split_on_tokens(tok_list, text):
            if not text:
                return []
            if not tok_list:
                return self._tokenize(text, **kwargs)

            tokenized_text = []
            text_list = [text]
            for tok in tok_list:
                tokenized_text = []
                for sub_text in text_list:
                    if sub_text not in self.added_tokens_encoder \
                            and sub_text not in self.all_special_tokens:
                        tokenized_text += split_on_token(tok, sub_text)
                    else:
                        tokenized_text += [sub_text]
                text_list = tokenized_text

            return sum((self._tokenize(token, **kwargs) if token not \
                    in self.added_tokens_encoder and token not in self.all_special_tokens \
                    else [token] for token in tokenized_text), [])

        added_tokens = list(self.added_tokens_encoder.keys()) + self.all_special_tokens
        tokenized_text = split_on_tokens(added_tokens, text)
        return tokenized_text