import re

import numpy as np
from sklearn.base import BaseEstimator, TransformerMixin


STOPWORDS = {
    "a", "an", "the", "is", "it", "in", "on", "at", "to", "for",
    "of", "and", "or", "but", "not", "with", "as", "by", "from",
    "this", "that", "was", "are", "be", "been", "have", "has",
    "had", "do", "did", "will", "would", "could", "should",
    "i", "me", "my", "you", "your", "he", "she", "we", "they",
    "his", "her", "our", "their", "its", "what", "which",
}


class LowercaseTransformer(BaseEstimator, TransformerMixin):
    def fit(self, X, y=None):
        self.fitted_ = True
        return self

    def transform(self, X):
        return np.array([msg.lower() for msg in X])


class StopwordRemover(BaseEstimator, TransformerMixin):
    def fit(self, X, y=None):
        self.fitted_ = True
        return self

    def transform(self, X):
        return np.array([self._remove(msg) for msg in X])

    def _remove(self, message):
        words = message.split()
        return " ".join(w for w in words if w.lower() not in STOPWORDS)


class PunctuationRemover(BaseEstimator, TransformerMixin):
    def fit(self, X, y=None):
        self.fitted_ = True
        return self

    def transform(self, X):
        return np.array([re.sub(r"[^\w\s]", " ", msg) for msg in X])