augly/text/transforms.py [821:858]:
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        priority_words: Optional[List[str]] = None,
        ignore_words: Optional[List[str]] = None,
        p: float = 1.0,
    ):
        """
        @param aug_word_p: probability of words to be augmented

        @param aug_word_min: minimum # of words to be augmented

        @param aug_word_max: maximum # of words to be augmented

        @param n: number of augmentations to be performed for each text

        @param mapping: either a dictionary representing the mapping or an iopath uri where
            the mapping is stored

        @param priority_words: list of target words that the augmenter should prioritize
            to augment first

        @param ignore_words: list of words that the augmenter should not augment

        @param p: the probability of the transform being applied; default value is 1.0
        """
        super().__init__(p)
        self.aug_word_p = aug_word_p
        self.aug_word_min = aug_word_min
        self.aug_word_max = aug_word_max
        self.n = n
        self.mapping = mapping
        self.priority_words = priority_words
        self.ignore_words = ignore_words

    def apply_transform(
        self,
        texts: Union[str, List[str]],
        metadata: Optional[List[Dict[str, Any]]] = None,
        **aug_kwargs,
    ) -> List[str]:
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augly/text/transforms.py [1038:1076]:
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        priority_words: Optional[List[str]] = None,
        ignore_words: Optional[List[str]] = None,
        p: float = 1.0,
    ):
        """
        @param aug_word_p: probability of words to be augmented

        @param aug_word_min: minimum # of words to be augmented

        @param aug_word_max: maximum # of words to be augmented

        @param n: number of augmentations to be performed for each text

        @param mapping: a mapping of words from one gender to another; a mapping can be
            supplied either directly as a dict or as a filepath to a json file containing
            the dict

        @param priority_words: list of target words that the augmenter should
            prioritize to augment first

        @param ignore_words: list of words that the augmenter should not augment

        @param p: the probability of the transform being applied; default value is 1.0
        """
        super().__init__(p)
        self.aug_word_p = aug_word_p
        self.aug_word_min = aug_word_min
        self.aug_word_max = aug_word_max
        self.n = n
        self.mapping = mapping
        self.priority_words = priority_words
        self.ignore_words = ignore_words

    def apply_transform(
        self,
        texts: Union[str, List[str]],
        metadata: Optional[List[Dict[str, Any]]] = None,
        **aug_kwargs,
    ) -> List[str]:
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