"""
This script is just intended as a placeholder for your own text preprocessing
script
"""

from collections import Counter


def nltk_tokenizer(txt):
    import nltk

    return nltk.word_tokenize(txt)


def keras_tokenizer(txt):
    from tensorflow.keras.preprocessing.text import text_to_word_sequence

    return text_to_word_sequence(
        txt, filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n', lower=False, split=" "
    )


class Text:
    def __init__(self, txt):
        self.txt = txt

    def tokenize(self, method="keras"):
        if method == "keras":
            self.tokens = keras_tokenizer(self.txt)
        elif method == "nltk":
            self.tokens = nltk_tokenizer(self.txt)

    def lemmatize_stanza(self):
        """
        tokenlist (list): A list of tokens

        lemmatize a tokenlist using stanza
        """

        import stanza

        nlp = stanza.Pipeline(
            lang="en", processors="tokenize,lemma", tokenize_pretokenized=False
        )

        doc = nlp(self.txt)

        res = [
            (word.lemma)
            for n_sent, sent in enumerate(doc.sentences)
            for word in sent.words
        ]

        return res

    def get_frequencies(self):
        return Counter(self.tokens)

    def get_tokens(self):
        return self.tokens

9ffe724ef51b64f5f4fb4cb8fb3891c2a023c36b
<mask token>


class Text:

    def __init__(self, txt):
        self.txt = txt

    def tokenize(self, method='keras'):
        if method == 'keras':
            self.tokens = keras_tokenizer(self.txt)
        elif method == 'nltk':
            self.tokens = nltk_tokenizer(self.txt)
    <mask token>
    <mask token>
    <mask token>

<mask token>


class Text:

    def __init__(self, txt):
        self.txt = txt

    def tokenize(self, method='keras'):
        if method == 'keras':
            self.tokens = keras_tokenizer(self.txt)
        elif method == 'nltk':
            self.tokens = nltk_tokenizer(self.txt)
    <mask token>

    def get_frequencies(self):
        return Counter(self.tokens)
    <mask token>

<mask token>


def nltk_tokenizer(txt):
    import nltk
    return nltk.word_tokenize(txt)


<mask token>


class Text:

    def __init__(self, txt):
        self.txt = txt

    def tokenize(self, method='keras'):
        if method == 'keras':
            self.tokens = keras_tokenizer(self.txt)
        elif method == 'nltk':
            self.tokens = nltk_tokenizer(self.txt)

    def lemmatize_stanza(self):
        """
        tokenlist (list): A list of tokens

        lemmatize a tokenlist using stanza
        """
        import stanza
        nlp = stanza.Pipeline(lang='en', processors='tokenize,lemma',
            tokenize_pretokenized=False)
        doc = nlp(self.txt)
        res = [word.lemma for n_sent, sent in enumerate(doc.sentences) for
            word in sent.words]
        return res

    def get_frequencies(self):
        return Counter(self.tokens)

    def get_tokens(self):
        return self.tokens

<mask token>


def nltk_tokenizer(txt):
    import nltk
    return nltk.word_tokenize(txt)


def keras_tokenizer(txt):
    from tensorflow.keras.preprocessing.text import text_to_word_sequence
    return text_to_word_sequence(txt, filters=
        '!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n', lower=False, split=' ')


class Text:

    def __init__(self, txt):
        self.txt = txt

    def tokenize(self, method='keras'):
        if method == 'keras':
            self.tokens = keras_tokenizer(self.txt)
        elif method == 'nltk':
            self.tokens = nltk_tokenizer(self.txt)

    def lemmatize_stanza(self):
        """
        tokenlist (list): A list of tokens

        lemmatize a tokenlist using stanza
        """
        import stanza
        nlp = stanza.Pipeline(lang='en', processors='tokenize,lemma',
            tokenize_pretokenized=False)
        doc = nlp(self.txt)
        res = [word.lemma for n_sent, sent in enumerate(doc.sentences) for
            word in sent.words]
        return res

    def get_frequencies(self):
        return Counter(self.tokens)

    def get_tokens(self):
        return self.tokens

"""
This script is just intended as a placeholder for your own text preprocessing
script
"""

from collections import Counter


def nltk_tokenizer(txt):
    import nltk

    return nltk.word_tokenize(txt)


def keras_tokenizer(txt):
    from tensorflow.keras.preprocessing.text import text_to_word_sequence

    return text_to_word_sequence(
        txt, filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n', lower=False, split=" "
    )


class Text:
    def __init__(self, txt):
        self.txt = txt

    def tokenize(self, method="keras"):
        if method == "keras":
            self.tokens = keras_tokenizer(self.txt)
        elif method == "nltk":
            self.tokens = nltk_tokenizer(self.txt)

    def lemmatize_stanza(self):
        """
        tokenlist (list): A list of tokens

        lemmatize a tokenlist using stanza
        """

        import stanza

        nlp = stanza.Pipeline(
            lang="en", processors="tokenize,lemma", tokenize_pretokenized=False
        )

        doc = nlp(self.txt)

        res = [
            (word.lemma)
            for n_sent, sent in enumerate(doc.sentences)
            for word in sent.words
        ]

        return res

    def get_frequencies(self):
        return Counter(self.tokens)

    def get_tokens(self):
        return self.tokens
