import pickle
import re
import uuid
import warnings
from collections import defaultdict
from collections.abc import Mapping
from functools import partial
from io import StringIO
from itertools import product

import numpy as np
import pytest
from numpy.testing import assert_array_almost_equal, assert_array_equal
from scipy import sparse

from sklearn.base import clone
from sklearn.feature_extraction.text import (
    ENGLISH_STOP_WORDS,
    CountVectorizer,
    HashingVectorizer,
    TfidfTransformer,
    TfidfVectorizer,
    strip_accents_ascii,
    strip_accents_unicode,
    strip_tags,
)
from sklearn.model_selection import GridSearchCV, cross_val_score, train_test_split
from sklearn.pipeline import Pipeline
from sklearn.svm import LinearSVC
from sklearn.utils._testing import (
    assert_allclose_dense_sparse,
    assert_almost_equal,
    skip_if_32bit,
)
from sklearn.utils.fixes import _IS_WASM, CSC_CONTAINERS, CSR_CONTAINERS

JUNK_FOOD_DOCS = (
    "the pizza pizza beer copyright",
    "the pizza burger beer copyright",
    "the the pizza beer beer copyright",
    "the burger beer beer copyright",
    "the coke burger coke copyright",
    "the coke burger burger",
)

NOTJUNK_FOOD_DOCS = (
    "the salad celeri copyright",
    "the salad salad sparkling water copyright",
    "the the celeri celeri copyright",
    "the tomato tomato salad water",
    "the tomato salad water copyright",
)

ALL_FOOD_DOCS = JUNK_FOOD_DOCS + NOTJUNK_FOOD_DOCS


def uppercase(s):
    return strip_accents_unicode(s).upper()


def strip_eacute(s):
    return s.replace("é", "e")


def split_tokenize(s):
    return s.split()


def lazy_analyze(s):
    return ["the_ultimate_feature"]


def test_strip_accents():
    # check some classical latin accentuated symbols
    a = "àáâãäåçèéêë"
    expected = "aaaaaaceeee"
    assert strip_accents_unicode(a) == expected

    a = "ìíîïñòóôõöùúûüý"
    expected = "iiiinooooouuuuy"
    assert strip_accents_unicode(a) == expected

    # check some arabic
    a = "\u0625"  # alef with a hamza below: إ
    expected = "\u0627"  # simple alef: ا
    assert strip_accents_unicode(a) == expected

    # mix letters accentuated and not
    a = "this is à test"
    expected = "this is a test"
    assert strip_accents_unicode(a) == expected

    # strings that are already decomposed
    a = "o\u0308"  # o with diaeresis
    expected = "o"
    assert strip_accents_unicode(a) == expected

    # combining marks by themselves
    a = "\u0300\u0301\u0302\u0303"
    expected = ""
    assert strip_accents_unicode(a) == expected

    # Multiple combining marks on one character
    a = "o\u0308\u0304"
    expected = "o"
    assert strip_accents_unicode(a) == expected


def test_to_ascii():
    # check some classical latin accentuated symbols
    a = "àáâãäåçèéêë"
    expected = "aaaaaaceeee"
    assert strip_accents_ascii(a) == expected

    a = "ìíîïñòóôõöùúûüý"
    expected = "iiiinooooouuuuy"
    assert strip_accents_ascii(a) == expected

    # check some arabic
    a = "\u0625"  # halef with a hamza below
    expected = ""  # halef has no direct ascii match
    assert strip_accents_ascii(a) == expected

    # mix letters accentuated and not
    a = "this is à test"
    expected = "this is a test"
    assert strip_accents_ascii(a) == expected


@pytest.mark.parametrize("Vectorizer", (CountVectorizer, HashingVectorizer))
def test_word_analyzer_unigrams(Vectorizer):
    wa = Vectorizer(strip_accents="ascii").build_analyzer()
    text = "J'ai mangé du kangourou  ce midi, c'était pas très bon."
    expected = [
        "ai",
        "mange",
        "du",
        "kangourou",
        "ce",
        "midi",
        "etait",
        "pas",
        "tres",
        "bon",
    ]
    assert wa(text) == expected

    text = "This is a test, really.\n\n I met Harry yesterday."
    expected = ["this", "is", "test", "really", "met", "harry", "yesterday"]
    assert wa(text) == expected

    wa = Vectorizer(input="file").build_analyzer()
    text = StringIO("This is a test with a file-like object!")
    expected = ["this", "is", "test", "with", "file", "like", "object"]
    assert wa(text) == expected

    # with custom preprocessor
    wa = Vectorizer(preprocessor=uppercase).build_analyzer()
    text = "J'ai mangé du kangourou  ce midi,  c'était pas très bon."
    expected = [
        "AI",
        "MANGE",
        "DU",
        "KANGOUROU",
        "CE",
        "MIDI",
        "ETAIT",
        "PAS",
        "TRES",
        "BON",
    ]
    assert wa(text) == expected

    # with custom tokenizer
    wa = Vectorizer(tokenizer=split_tokenize, strip_accents="ascii").build_analyzer()
    text = "J'ai mangé du kangourou  ce midi, c'était pas très bon."
    expected = [
        "j'ai",
        "mange",
        "du",
        "kangourou",
        "ce",
        "midi,",
        "c'etait",
        "pas",
        "tres",
        "bon.",
    ]
    assert wa(text) == expected


def test_word_analyzer_unigrams_and_bigrams():
    wa = CountVectorizer(
        analyzer="word", strip_accents="unicode", ngram_range=(1, 2)
    ).build_analyzer()

    text = "J'ai mangé du kangourou  ce midi, c'était pas très bon."
    expected = [
        "ai",
        "mange",
        "du",
        "kangourou",
        "ce",
        "midi",
        "etait",
        "pas",
        "tres",
        "bon",
        "ai mange",
        "mange du",
        "du kangourou",
        "kangourou ce",
        "ce midi",
        "midi etait",
        "etait pas",
        "pas tres",
        "tres bon",
    ]
    assert wa(text) == expected


def test_unicode_decode_error():
    # decode_error default to strict, so this should fail
    # First, encode (as bytes) a unicode string.
    text = "J'ai mangé du kangourou  ce midi, c'était pas très bon."
    text_bytes = text.encode("utf-8")

    # Then let the Analyzer try to decode it as ascii. It should fail,
    # because we have given it an incorrect encoding.
    wa = CountVectorizer(ngram_range=(1, 2), encoding="ascii").build_analyzer()
    with pytest.raises(UnicodeDecodeError):
        wa(text_bytes)

    ca = CountVectorizer(
        analyzer="char", ngram_range=(3, 6), encoding="ascii"
    ).build_analyzer()
    with pytest.raises(UnicodeDecodeError):
        ca(text_bytes)


def test_char_ngram_analyzer():
    cnga = CountVectorizer(
        analyzer="char", strip_accents="unicode", ngram_range=(3, 6)
    ).build_analyzer()

    text = "J'ai mangé du kangourou  ce midi, c'était pas très bon"
    expected = ["j'a", "'ai", "ai ", "i m", " ma"]
    assert cnga(text)[:5] == expected
    expected = ["s tres", " tres ", "tres b", "res bo", "es bon"]
    assert cnga(text)[-5:] == expected

    text = "This \n\tis a test, really.\n\n I met Harry yesterday"
    expected = ["thi", "his", "is ", "s i", " is"]
    assert cnga(text)[:5] == expected

    expected = [" yeste", "yester", "esterd", "sterda", "terday"]
    assert cnga(text)[-5:] == expected

    cnga = CountVectorizer(
        input="file", analyzer="char", ngram_range=(3, 6)
    ).build_analyzer()
    text = StringIO("This is a test with a file-like object!")
    expected = ["thi", "his", "is ", "s i", " is"]
    assert cnga(text)[:5] == expected


def test_char_wb_ngram_analyzer():
    cnga = CountVectorizer(
        analyzer="char_wb", strip_accents="unicode", ngram_range=(3, 6)
    ).build_analyzer()

    text = "This \n\tis a test, really.\n\n I met Harry yesterday"
    expected = [" th", "thi", "his", "is ", " thi"]
    assert cnga(text)[:5] == expected

    expected = ["yester", "esterd", "sterda", "terday", "erday "]
    assert cnga(text)[-5:] == expected

    cnga = CountVectorizer(
        input="file", analyzer="char_wb", ngram_range=(3, 6)
    ).build_analyzer()
    text = StringIO("A test with a file-like object!")
    expected = [" a ", " te", "tes", "est", "st ", " tes"]
    assert cnga(text)[:6] == expected


def test_word_ngram_analyzer():
    cnga = CountVectorizer(
        analyzer="word", strip_accents="unicode", ngram_range=(3, 6)
    ).build_analyzer()

    text = "This \n\tis a test, really.\n\n I met Harry yesterday"
    expected = ["this is test", "is test really", "test really met"]
    assert cnga(text)[:3] == expected

    expected = [
        "test really met harry yesterday",
        "this is test really met harry",
        "is test really met harry yesterday",
    ]
    assert cnga(text)[-3:] == expected

    cnga_file = CountVectorizer(
        input="file", analyzer="word", ngram_range=(3, 6)
    ).build_analyzer()
    file = StringIO(text)
    assert cnga_file(file) == cnga(text)


def test_countvectorizer_custom_vocabulary():
    vocab = {"pizza": 0, "beer": 1}
    terms = set(vocab.keys())

    # Try a few of the supported types.
    for typ in [dict, list, iter, partial(defaultdict, int)]:
        v = typ(vocab)
        vect = CountVectorizer(vocabulary=v)
        vect.fit(JUNK_FOOD_DOCS)
        if isinstance(v, Mapping):
            assert vect.vocabulary_ == vocab
        else:
            assert set(vect.vocabulary_) == terms
        X = vect.transform(JUNK_FOOD_DOCS)
        assert X.shape[1] == len(terms)
        v = typ(vocab)
        vect = CountVectorizer(vocabulary=v)
        inv = vect.inverse_transform(X)
        assert len(inv) == X.shape[0]


def test_countvectorizer_custom_vocabulary_pipeline():
    what_we_like = ["pizza", "beer"]
    pipe = Pipeline(
        [
            ("count", CountVectorizer(vocabulary=what_we_like)),
            ("tfidf", TfidfTransformer()),
        ]
    )
    X = pipe.fit_transform(ALL_FOOD_DOCS)
    assert set(pipe.named_steps["count"].vocabulary_) == set(what_we_like)
    assert X.shape[1] == len(what_we_like)


def test_countvectorizer_custom_vocabulary_repeated_indices():
    vocab = {"pizza": 0, "beer": 0}
    msg = "Vocabulary contains repeated indices"
    with pytest.raises(ValueError, match=msg):
        vect = CountVectorizer(vocabulary=vocab)
        vect.fit(["pasta_siziliana"])


def test_countvectorizer_custom_vocabulary_gap_index():
    vocab = {"pizza": 1, "beer": 2}
    with pytest.raises(ValueError, match="doesn't contain index"):
        vect = CountVectorizer(vocabulary=vocab)
        vect.fit(["pasta_verdura"])


def test_countvectorizer_stop_words():
    cv = CountVectorizer()
    cv.set_params(stop_words="english")
    assert cv.get_stop_words() == ENGLISH_STOP_WORDS
    cv.set_params(stop_words="_bad_str_stop_")
    with pytest.raises(ValueError):
        cv.get_stop_words()
    cv.set_params(stop_words="_bad_unicode_stop_")
    with pytest.raises(ValueError):
        cv.get_stop_words()
    stoplist = ["some", "other", "words"]
    cv.set_params(stop_words=stoplist)
    assert cv.get_stop_words() == set(stoplist)


def test_countvectorizer_empty_vocabulary():
    with pytest.raises(ValueError, match="empty vocabulary"):
        vect = CountVectorizer(vocabulary=[])
        vect.fit(["foo"])

    with pytest.raises(ValueError, match="empty vocabulary"):
        v = CountVectorizer(max_df=1.0, stop_words="english")
        # fit on stopwords only
        v.fit(["to be or not to be", "and me too", "and so do you"])


def test_fit_countvectorizer_twice():
    cv = CountVectorizer()
    X1 = cv.fit_transform(ALL_FOOD_DOCS[:5])
    X2 = cv.fit_transform(ALL_FOOD_DOCS[5:])
    assert X1.shape[1] != X2.shape[1]


def test_countvectorizer_custom_token_pattern():
    """Check `get_feature_names_out()` when a custom token pattern is passed.
    Non-regression test for:
    https://github.com/scikit-learn/scikit-learn/issues/12971
    """
    corpus = [
        "This is the 1st document in my corpus.",
        "This document is the 2nd sample.",
        "And this is the 3rd one.",
        "Is this the 4th document?",
    ]
    token_pattern = r"[0-9]{1,3}(?:st|nd|rd|th)\s\b(\w{2,})\b"
    vectorizer = CountVectorizer(token_pattern=token_pattern)
    vectorizer.fit_transform(corpus)
    expected = ["document", "one", "sample"]
    feature_names_out = vectorizer.get_feature_names_out()
    assert_array_equal(feature_names_out, expected)


def test_countvectorizer_custom_token_pattern_with_several_group():
    """Check that we raise an error if token pattern capture several groups.
    Non-regression test for:
    https://github.com/scikit-learn/scikit-learn/issues/12971
    """
    corpus = [
        "This is the 1st document in my corpus.",
        "This document is the 2nd sample.",
        "And this is the 3rd one.",
        "Is this the 4th document?",
    ]

    token_pattern = r"([0-9]{1,3}(?:st|nd|rd|th))\s\b(\w{2,})\b"
    err_msg = "More than 1 capturing group in token pattern"
    vectorizer = CountVectorizer(token_pattern=token_pattern)
    with pytest.raises(ValueError, match=err_msg):
        vectorizer.fit(corpus)


def test_countvectorizer_uppercase_in_vocab():
    # Check that the check for uppercase in the provided vocabulary is only done at fit
    # time and not at transform time (#21251)
    vocabulary = ["Sample", "Upper", "Case", "Vocabulary"]
    message = (
        "Upper case characters found in"
        " vocabulary while 'lowercase'"
        " is True. These entries will not"
        " be matched with any documents"
    )

    vectorizer = CountVectorizer(lowercase=True, vocabulary=vocabulary)

    with pytest.warns(UserWarning, match=message):
        vectorizer.fit(vocabulary)

    with warnings.catch_warnings():
        warnings.simplefilter("error", UserWarning)
        vectorizer.transform(vocabulary)


def test_tf_transformer_feature_names_out():
    """Check get_feature_names_out for TfidfTransformer"""
    X = [[1, 1, 1], [1, 1, 0], [1, 0, 0]]
    tr = TfidfTransformer(smooth_idf=True, norm="l2").fit(X)

    feature_names_in = ["a", "c", "b"]
    feature_names_out = tr.get_feature_names_out(feature_names_in)
    assert_array_equal(feature_names_in, feature_names_out)


def test_tf_idf_smoothing():
    X = [[1, 1, 1], [1, 1, 0], [1, 0, 0]]
    tr = TfidfTransformer(smooth_idf=True, norm="l2")
    tfidf = tr.fit_transform(X).toarray()
    assert (tfidf >= 0).all()

    # check normalization
    assert_array_almost_equal((tfidf**2).sum(axis=1), [1.0, 1.0, 1.0])

    # this is robust to features with only zeros
    X = [[1, 1, 0], [1, 1, 0], [1, 0, 0]]
    tr = TfidfTransformer(smooth_idf=True, norm="l2")
    tfidf = tr.fit_transform(X).toarray()
    assert (tfidf >= 0).all()


@pytest.mark.xfail(
    _IS_WASM,
    reason=(
        "no floating point exceptions, see"
        " https://github.com/numpy/numpy/pull/21895#issuecomment-1311525881"
    ),
)
def test_tfidf_no_smoothing():
    X = [[1, 1, 1], [1, 1, 0], [1, 0, 0]]
    tr = TfidfTransformer(smooth_idf=False, norm="l2")
    tfidf = tr.fit_transform(X).toarray()
    assert (tfidf >= 0).all()

    # check normalization
    assert_array_almost_equal((tfidf**2).sum(axis=1), [1.0, 1.0, 1.0])

    # the lack of smoothing make IDF fragile in the presence of feature with
    # only zeros
    X = [[1, 1, 0], [1, 1, 0], [1, 0, 0]]
    tr = TfidfTransformer(smooth_idf=False, norm="l2")

    in_warning_message = "divide by zero"
    with pytest.warns(RuntimeWarning, match=in_warning_message):
        tr.fit_transform(X).toarray()


def test_sublinear_tf():
    X = [[1], [2], [3]]
    tr = TfidfTransformer(sublinear_tf=True, use_idf=False, norm=None)
    tfidf = tr.fit_transform(X).toarray()
    assert tfidf[0] == 1
    assert tfidf[1] > tfidf[0]
    assert tfidf[2] > tfidf[1]
    assert tfidf[1] < 2
    assert tfidf[2] < 3


def test_vectorizer():
    # raw documents as an iterator
    train_data = iter(ALL_FOOD_DOCS[:-1])
    test_data = [ALL_FOOD_DOCS[-1]]
    n_train = len(ALL_FOOD_DOCS) - 1

    # test without vocabulary
    v1 = CountVectorizer(max_df=0.5)
    counts_train = v1.fit_transform(train_data)
    if hasattr(counts_train, "tocsr"):
        counts_train = counts_train.tocsr()
    assert counts_train[0, v1.vocabulary_["pizza"]] == 2

    # build a vectorizer v1 with the same vocabulary as the one fitted by v1
    v2 = CountVectorizer(vocabulary=v1.vocabulary_)

    # compare that the two vectorizer give the same output on the test sample
    for v in (v1, v2):
        counts_test = v.transform(test_data)
        if hasattr(counts_test, "tocsr"):
            counts_test = counts_test.tocsr()

        vocabulary = v.vocabulary_
        assert counts_test[0, vocabulary["salad"]] == 1
        assert counts_test[0, vocabulary["tomato"]] == 1
        assert counts_test[0, vocabulary["water"]] == 1

        # stop word from the fixed list
        assert "the" not in vocabulary

        # stop word found automatically by the vectorizer DF thresholding
        # words that are high frequent across the complete corpus are likely
        # to be not informative (either real stop words of extraction
        # artifacts)
        assert "copyright" not in vocabulary

        # not present in the sample
        assert counts_test[0, vocabulary["coke"]] == 0
        assert counts_test[0, vocabulary["burger"]] == 0
        assert counts_test[0, vocabulary["beer"]] == 0
        assert counts_test[0, vocabulary["pizza"]] == 0

    # test tf-idf
    t1 = TfidfTransformer(norm="l1")
    tfidf = t1.fit(counts_train).transform(counts_train).toarray()
    assert len(t1.idf_) == len(v1.vocabulary_)
    assert tfidf.shape == (n_train, len(v1.vocabulary_))

    # test tf-idf with new data
    tfidf_test = t1.transform(counts_test).toarray()
    assert tfidf_test.shape == (len(test_data), len(v1.vocabulary_))

    # test tf alone
    t2 = TfidfTransformer(norm="l1", use_idf=False)
    tf = t2.fit(counts_train).transform(counts_train).toarray()
    assert not hasattr(t2, "idf_")

    # test idf transform with unlearned idf vector
    t3 = TfidfTransformer(use_idf=True)
    with pytest.raises(ValueError):
        t3.transform(counts_train)

    # L1-normalized term frequencies sum to one
    assert_array_almost_equal(np.sum(tf, axis=1), [1.0] * n_train)

    # test the direct tfidf vectorizer
    # (equivalent to term count vectorizer + tfidf transformer)
    train_data = iter(ALL_FOOD_DOCS[:-1])
    tv = TfidfVectorizer(norm="l1")

    tv.max_df = v1.max_df
    tfidf2 = tv.fit_transform(train_data).toarray()
    assert not tv.fixed_vocabulary_
    assert_array_almost_equal(tfidf, tfidf2)

    # test the direct tfidf vectorizer with new data
    tfidf_test2 = tv.transform(test_data).toarray()
    assert_array_almost_equal(tfidf_test, tfidf_test2)

    # test transform on unfitted vectorizer with empty vocabulary
    v3 = CountVectorizer(vocabulary=None)
    with pytest.raises(ValueError):
        v3.transform(train_data)

    # ascii preprocessor?
    v3.set_params(strip_accents="ascii", lowercase=False)
    processor = v3.build_preprocessor()
    text = "J'ai mangé du kangourou  ce midi, c'était pas très bon."
    expected = strip_accents_ascii(text)
    result = processor(text)
    assert expected == result

    # error on bad strip_accents param
    v3.set_params(strip_accents="_gabbledegook_", preprocessor=None)
    with pytest.raises(ValueError):
        v3.build_preprocessor()

    # error with bad analyzer type
    v3.set_params = "_invalid_analyzer_type_"
    with pytest.raises(ValueError):
        v3.build_analyzer()


def test_tfidf_vectorizer_setters():
    norm, use_idf, smooth_idf, sublinear_tf = "l2", False, False, False
    tv = TfidfVectorizer(
        norm=norm, use_idf=use_idf, smooth_idf=smooth_idf, sublinear_tf=sublinear_tf
    )
    tv.fit(JUNK_FOOD_DOCS)
    assert tv._tfidf.norm == norm
    assert tv._tfidf.use_idf == use_idf
    assert tv._tfidf.smooth_idf == smooth_idf
    assert tv._tfidf.sublinear_tf == sublinear_tf

    # assigning value to `TfidfTransformer` should not have any effect until
    # fitting
    tv.norm = "l1"
    tv.use_idf = True
    tv.smooth_idf = True
    tv.sublinear_tf = True
    assert tv._tfidf.norm == norm
    assert tv._tfidf.use_idf == use_idf
    assert tv._tfidf.smooth_idf == smooth_idf
    assert tv._tfidf.sublinear_tf == sublinear_tf

    tv.fit(JUNK_FOOD_DOCS)
    assert tv._tfidf.norm == tv.norm
    assert tv._tfidf.use_idf == tv.use_idf
    assert tv._tfidf.smooth_idf == tv.smooth_idf
    assert tv._tfidf.sublinear_tf == tv.sublinear_tf


def test_hashing_vectorizer():
    v = HashingVectorizer()
    X = v.transform(ALL_FOOD_DOCS)
    token_nnz = X.nnz
    assert X.shape == (len(ALL_FOOD_DOCS), v.n_features)
    assert X.dtype == v.dtype

    # By default the hashed values receive a random sign and l2 normalization
    # makes the feature values bounded
    assert np.min(X.data) > -1
    assert np.min(X.data) < 0
    assert np.max(X.data) > 0
    assert np.max(X.data) < 1

    # Check that the rows are normalized
    for i in range(X.shape[0]):
        assert_almost_equal(np.linalg.norm(X[0].data, 2), 1.0)

    # Check vectorization with some non-default parameters
    v = HashingVectorizer(ngram_range=(1, 2), norm="l1")
    X = v.transform(ALL_FOOD_DOCS)
    assert X.shape == (len(ALL_FOOD_DOCS), v.n_features)
    assert X.dtype == v.dtype

    # ngrams generate more non zeros
    ngrams_nnz = X.nnz
    assert ngrams_nnz > token_nnz
    assert ngrams_nnz < 2 * token_nnz

    # makes the feature values bounded
    assert np.min(X.data) > -1
    assert np.max(X.data) < 1

    # Check that the rows are normalized
    for i in range(X.shape[0]):
        assert_almost_equal(np.linalg.norm(X[0].data, 1), 1.0)


def test_feature_names():
    cv = CountVectorizer(max_df=0.5)

    # test for Value error on unfitted/empty vocabulary
    with pytest.raises(ValueError):
        cv.get_feature_names_out()
    assert not cv.fixed_vocabulary_

    # test for vocabulary learned from data
    X = cv.fit_transform(ALL_FOOD_DOCS)
    n_samples, n_features = X.shape
    assert len(cv.vocabulary_) == n_features

    feature_names = cv.get_feature_names_out()
    assert isinstance(feature_names, np.ndarray)
    assert feature_names.dtype == object

    assert len(feature_names) == n_features
    assert_array_equal(
        [
            "beer",
            "burger",
            "celeri",
            "coke",
            "pizza",
            "salad",
            "sparkling",
            "tomato",
            "water",
        ],
        feature_names,
    )

    for idx, name in enumerate(feature_names):
        assert idx == cv.vocabulary_.get(name)

    # test for custom vocabulary
    vocab = [
        "beer",
        "burger",
        "celeri",
        "coke",
        "pizza",
        "salad",
        "sparkling",
        "tomato",
        "water",
    ]

    cv = CountVectorizer(vocabulary=vocab)
    feature_names = cv.get_feature_names_out()
    assert_array_equal(
        [
            "beer",
            "burger",
            "celeri",
            "coke",
            "pizza",
            "salad",
            "sparkling",
            "tomato",
            "water",
        ],
        feature_names,
    )
    assert cv.fixed_vocabulary_

    for idx, name in enumerate(feature_names):
        assert idx == cv.vocabulary_.get(name)


@pytest.mark.parametrize("Vectorizer", (CountVectorizer, TfidfVectorizer))
def test_vectorizer_max_features(Vectorizer):
    expected_vocabulary = {"burger", "beer", "salad", "pizza"}

    # test bounded number of extracted features
    vectorizer = Vectorizer(max_df=0.6, max_features=4)
    vectorizer.fit(ALL_FOOD_DOCS)
    assert set(vectorizer.vocabulary_) == expected_vocabulary


def test_count_vectorizer_max_features():
    # Regression test: max_features didn't work correctly in 0.14.

    cv_1 = CountVectorizer(max_features=1)
    cv_3 = CountVectorizer(max_features=3)
    cv_None = CountVectorizer(max_features=None)

    counts_1 = cv_1.fit_transform(JUNK_FOOD_DOCS).sum(axis=0)
    counts_3 = cv_3.fit_transform(JUNK_FOOD_DOCS).sum(axis=0)
    counts_None = cv_None.fit_transform(JUNK_FOOD_DOCS).sum(axis=0)

    features_1 = cv_1.get_feature_names_out()
    features_3 = cv_3.get_feature_names_out()
    features_None = cv_None.get_feature_names_out()

    # The most common feature is "the", with frequency 7.
    assert 7 == counts_1.max()
    assert 7 == counts_3.max()
    assert 7 == counts_None.max()

    # The most common feature should be the same
    assert "the" == features_1[np.argmax(counts_1)]
    assert "the" == features_3[np.argmax(counts_3)]
    assert "the" == features_None[np.argmax(counts_None)]


def test_vectorizer_max_df():
    test_data = ["abc", "dea", "eat"]
    vect = CountVectorizer(analyzer="char", max_df=1.0)
    vect.fit(test_data)
    assert "a" in vect.vocabulary_.keys()
    assert len(vect.vocabulary_.keys()) == 6

    vect.max_df = 0.5  # 0.5 * 3 documents -> max_doc_count == 1.5
    vect.fit(test_data)
    assert "a" not in vect.vocabulary_.keys()  # {ae} ignored
    assert len(vect.vocabulary_.keys()) == 4  # {bcdt} remain

    vect.max_df = 1
    vect.fit(test_data)
    assert "a" not in vect.vocabulary_.keys()  # {ae} ignored
    assert len(vect.vocabulary_.keys()) == 4  # {bcdt} remain


def test_vectorizer_min_df():
    test_data = ["abc", "dea", "eat"]
    vect = CountVectorizer(analyzer="char", min_df=1)
    vect.fit(test_data)
    assert "a" in vect.vocabulary_.keys()
    assert len(vect.vocabulary_.keys()) == 6

    vect.min_df = 2
    vect.fit(test_data)
    assert "c" not in vect.vocabulary_.keys()  # {bcdt} ignored
    assert len(vect.vocabulary_.keys()) == 2  # {ae} remain

    vect.min_df = 0.8  # 0.8 * 3 documents -> min_doc_count == 2.4
    vect.fit(test_data)
    assert "c" not in vect.vocabulary_.keys()  # {bcdet} ignored
    assert len(vect.vocabulary_.keys()) == 1  # {a} remains


def test_count_binary_occurrences():
    # by default multiple occurrences are counted as longs
    test_data = ["aaabc", "abbde"]
    vect = CountVectorizer(analyzer="char", max_df=1.0)
    X = vect.fit_transform(test_data).toarray()
    assert_array_equal(["a", "b", "c", "d", "e"], vect.get_feature_names_out())
    assert_array_equal([[3, 1, 1, 0, 0], [1, 2, 0, 1, 1]], X)

    # using boolean features, we can fetch the binary occurrence info
    # instead.
    vect = CountVectorizer(analyzer="char", max_df=1.0, binary=True)
    X = vect.fit_transform(test_data).toarray()
    assert_array_equal([[1, 1, 1, 0, 0], [1, 1, 0, 1, 1]], X)

    # check the ability to change the dtype
    vect = CountVectorizer(analyzer="char", max_df=1.0, binary=True, dtype=np.float32)
    X_sparse = vect.fit_transform(test_data)
    assert X_sparse.dtype == np.float32


def test_hashed_binary_occurrences():
    # by default multiple occurrences are counted as longs
    test_data = ["aaabc", "abbde"]
    vect = HashingVectorizer(alternate_sign=False, analyzer="char", norm=None)
    X = vect.transform(test_data)
    assert np.max(X[0:1].data) == 3
    assert np.max(X[1:2].data) == 2
    assert X.dtype == np.float64

    # using boolean features, we can fetch the binary occurrence info
    # instead.
    vect = HashingVectorizer(
        analyzer="char", alternate_sign=False, binary=True, norm=None
    )
    X = vect.transform(test_data)
    assert np.max(X.data) == 1
    assert X.dtype == np.float64

    # check the ability to change the dtype
    vect = HashingVectorizer(
        analyzer="char", alternate_sign=False, binary=True, norm=None, dtype=np.float64
    )
    X = vect.transform(test_data)
    assert X.dtype == np.float64


@pytest.mark.parametrize("Vectorizer", (CountVectorizer, TfidfVectorizer))
def test_vectorizer_inverse_transform(Vectorizer):
    # raw documents
    data = ALL_FOOD_DOCS
    vectorizer = Vectorizer()
    transformed_data = vectorizer.fit_transform(data)
    inversed_data = vectorizer.inverse_transform(transformed_data)
    assert isinstance(inversed_data, list)

    analyze = vectorizer.build_analyzer()
    for doc, inversed_terms in zip(data, inversed_data):
        terms = np.sort(np.unique(analyze(doc)))
        inversed_terms = np.sort(np.unique(inversed_terms))
        assert_array_equal(terms, inversed_terms)

    assert sparse.issparse(transformed_data)
    assert transformed_data.format == "csr"

    # Test that inverse_transform also works with numpy arrays and
    # scipy
    transformed_data2 = transformed_data.toarray()
    inversed_data2 = vectorizer.inverse_transform(transformed_data2)
    for terms, terms2 in zip(inversed_data, inversed_data2):
        assert_array_equal(np.sort(terms), np.sort(terms2))

    # Check that inverse_transform also works on non CSR sparse data:
    transformed_data3 = transformed_data.tocsc()
    inversed_data3 = vectorizer.inverse_transform(transformed_data3)
    for terms, terms3 in zip(inversed_data, inversed_data3):
        assert_array_equal(np.sort(terms), np.sort(terms3))


def test_count_vectorizer_pipeline_grid_selection():
    # raw documents
    data = JUNK_FOOD_DOCS + NOTJUNK_FOOD_DOCS

    # label junk food as -1, the others as +1
    target = [-1] * len(JUNK_FOOD_DOCS) + [1] * len(NOTJUNK_FOOD_DOCS)

    # split the dataset for model development and final evaluation
    train_data, test_data, target_train, target_test = train_test_split(
        data, target, test_size=0.2, random_state=0
    )

    pipeline = Pipeline([("vect", CountVectorizer()), ("svc", LinearSVC())])

    parameters = {
        "vect__ngram_range": [(1, 1), (1, 2)],
        "svc__loss": ("hinge", "squared_hinge"),
    }

    # find the best parameters for both the feature extraction and the
    # classifier
    grid_search = GridSearchCV(pipeline, parameters, n_jobs=1, cv=3)

    # Check that the best model found by grid search is 100% correct on the
    # held out evaluation set.
    pred = grid_search.fit(train_data, target_train).predict(test_data)
    assert_array_equal(pred, target_test)

    # on this toy dataset bigram representation which is used in the last of
    # the grid_search is considered the best estimator since they all converge
    # to 100% accuracy models
    assert grid_search.best_score_ == 1.0
    best_vectorizer = grid_search.best_estimator_.named_steps["vect"]
    assert best_vectorizer.ngram_range == (1, 1)


def test_vectorizer_pipeline_grid_selection():
    # raw documents
    data = JUNK_FOOD_DOCS + NOTJUNK_FOOD_DOCS

    # label junk food as -1, the others as +1
    target = [-1] * len(JUNK_FOOD_DOCS) + [1] * len(NOTJUNK_FOOD_DOCS)

    # split the dataset for model development and final evaluation
    train_data, test_data, target_train, target_test = train_test_split(
        data, target, test_size=0.1, random_state=0
    )

    pipeline = Pipeline([("vect", TfidfVectorizer()), ("svc", LinearSVC())])

    parameters = {
        "vect__ngram_range": [(1, 1), (1, 2)],
        "vect__norm": ("l1", "l2"),
        "svc__loss": ("hinge", "squared_hinge"),
    }

    # find the best parameters for both the feature extraction and the
    # classifier
    grid_search = GridSearchCV(pipeline, parameters, n_jobs=1)

    # Check that the best model found by grid search is 100% correct on the
    # held out evaluation set.
    pred = grid_search.fit(train_data, target_train).predict(test_data)
    assert_array_equal(pred, target_test)

    # on this toy dataset bigram representation which is used in the last of
    # the grid_search is considered the best estimator since they all converge
    # to 100% accuracy models
    assert grid_search.best_score_ == 1.0
    best_vectorizer = grid_search.best_estimator_.named_steps["vect"]
    assert best_vectorizer.ngram_range == (1, 1)
    assert best_vectorizer.norm == "l2"
    assert not best_vectorizer.fixed_vocabulary_


def test_vectorizer_pipeline_cross_validation():
    # raw documents
    data = JUNK_FOOD_DOCS + NOTJUNK_FOOD_DOCS

    # label junk food as -1, the others as +1
    target = [-1] * len(JUNK_FOOD_DOCS) + [1] * len(NOTJUNK_FOOD_DOCS)

    pipeline = Pipeline([("vect", TfidfVectorizer()), ("svc", LinearSVC())])

    cv_scores = cross_val_score(pipeline, data, target, cv=3)
    assert_array_equal(cv_scores, [1.0, 1.0, 1.0])


def test_vectorizer_unicode():
    # tests that the count vectorizer works with cyrillic.
    document = (
        "Машинное обучение — обширный подраздел искусственного "
        "интеллекта, изучающий методы построения алгоритмов, "
        "способных обучаться."
    )

    vect = CountVectorizer()
    X_counted = vect.fit_transform([document])
    assert X_counted.shape == (1, 12)

    vect = HashingVectorizer(norm=None, alternate_sign=False)
    X_hashed = vect.transform([document])
    assert X_hashed.shape == (1, 2**20)

    # No collisions on such a small dataset
    assert X_counted.nnz == X_hashed.nnz

    # When norm is None and not alternate_sign, the tokens are counted up to
    # collisions
    assert_array_equal(np.sort(X_counted.data), np.sort(X_hashed.data))


def test_tfidf_vectorizer_with_fixed_vocabulary():
    # non regression smoke test for inheritance issues
    vocabulary = ["pizza", "celeri"]
    vect = TfidfVectorizer(vocabulary=vocabulary)
    X_1 = vect.fit_transform(ALL_FOOD_DOCS)
    X_2 = vect.transform(ALL_FOOD_DOCS)
    assert_array_almost_equal(X_1.toarray(), X_2.toarray())
    assert vect.fixed_vocabulary_


def test_pickling_vectorizer():
    instances = [
        HashingVectorizer(),
        HashingVectorizer(norm="l1"),
        HashingVectorizer(binary=True),
        HashingVectorizer(ngram_range=(1, 2)),
        CountVectorizer(),
        CountVectorizer(preprocessor=strip_tags),
        CountVectorizer(analyzer=lazy_analyze),
        CountVectorizer(preprocessor=strip_tags).fit(JUNK_FOOD_DOCS),
        CountVectorizer(strip_accents=strip_eacute).fit(JUNK_FOOD_DOCS),
        TfidfVectorizer(),
        TfidfVectorizer(analyzer=lazy_analyze),
        TfidfVectorizer().fit(JUNK_FOOD_DOCS),
    ]

    for orig in instances:
        s = pickle.dumps(orig)
        copy = pickle.loads(s)
        assert type(copy) == orig.__class__
        assert copy.get_params() == orig.get_params()
        assert_allclose_dense_sparse(
            copy.fit_transform(JUNK_FOOD_DOCS),
            orig.fit_transform(JUNK_FOOD_DOCS),
        )


@pytest.mark.parametrize(
    "factory",
    [
        CountVectorizer.build_analyzer,
        CountVectorizer.build_preprocessor,
        CountVectorizer.build_tokenizer,
    ],
)
def test_pickling_built_processors(factory):
    """Tokenizers cannot be pickled
    https://github.com/scikit-learn/scikit-learn/issues/12833
    """
    vec = CountVectorizer()
    function = factory(vec)
    text = "J'ai mangé du kangourou  ce midi, c'était pas très bon."
    roundtripped_function = pickle.loads(pickle.dumps(function))
    expected = function(text)
    result = roundtripped_function(text)
    assert result == expected


def test_countvectorizer_vocab_sets_when_pickling():
    # ensure that vocabulary of type set is coerced to a list to
    # preserve iteration ordering after deserialization
    rng = np.random.RandomState(0)
    vocab_words = np.array(
        [
            "beer",
            "burger",
            "celeri",
            "coke",
            "pizza",
            "salad",
            "sparkling",
            "tomato",
            "water",
        ]
    )
    for x in range(0, 100):
        vocab_set = set(rng.choice(vocab_words, size=5, replace=False))
        cv = CountVectorizer(vocabulary=vocab_set)
        unpickled_cv = pickle.loads(pickle.dumps(cv))
        cv.fit(ALL_FOOD_DOCS)
        unpickled_cv.fit(ALL_FOOD_DOCS)
        assert_array_equal(
            cv.get_feature_names_out(), unpickled_cv.get_feature_names_out()
        )


def test_countvectorizer_vocab_dicts_when_pickling():
    rng = np.random.RandomState(0)
    vocab_words = np.array(
        [
            "beer",
            "burger",
            "celeri",
            "coke",
            "pizza",
            "salad",
            "sparkling",
            "tomato",
            "water",
        ]
    )
    for x in range(0, 100):
        vocab_dict = dict()
        words = rng.choice(vocab_words, size=5, replace=False)
        for y in range(0, 5):
            vocab_dict[words[y]] = y
        cv = CountVectorizer(vocabulary=vocab_dict)
        unpickled_cv = pickle.loads(pickle.dumps(cv))
        cv.fit(ALL_FOOD_DOCS)
        unpickled_cv.fit(ALL_FOOD_DOCS)
        assert_array_equal(
            cv.get_feature_names_out(), unpickled_cv.get_feature_names_out()
        )


def test_pickling_transformer():
    X = CountVectorizer().fit_transform(JUNK_FOOD_DOCS)
    orig = TfidfTransformer().fit(X)
    s = pickle.dumps(orig)
    copy = pickle.loads(s)
    assert type(copy) == orig.__class__
    assert_array_equal(copy.fit_transform(X).toarray(), orig.fit_transform(X).toarray())


def test_transformer_idf_setter():
    X = CountVectorizer().fit_transform(JUNK_FOOD_DOCS)
    orig = TfidfTransformer().fit(X)
    copy = TfidfTransformer()
    copy.idf_ = orig.idf_
    assert_array_equal(copy.transform(X).toarray(), orig.transform(X).toarray())


def test_tfidf_vectorizer_setter():
    orig = TfidfVectorizer(use_idf=True)
    orig.fit(JUNK_FOOD_DOCS)
    copy = TfidfVectorizer(vocabulary=orig.vocabulary_, use_idf=True)
    copy.idf_ = orig.idf_
    assert_array_equal(
        copy.transform(JUNK_FOOD_DOCS).toarray(),
        orig.transform(JUNK_FOOD_DOCS).toarray(),
    )
    # `idf_` cannot be set with `use_idf=False`
    copy = TfidfVectorizer(vocabulary=orig.vocabulary_, use_idf=False)
    err_msg = "`idf_` cannot be set when `user_idf=False`."
    with pytest.raises(ValueError, match=err_msg):
        copy.idf_ = orig.idf_


def test_tfidfvectorizer_invalid_idf_attr():
    vect = TfidfVectorizer(use_idf=True)
    vect.fit(JUNK_FOOD_DOCS)
    copy = TfidfVectorizer(vocabulary=vect.vocabulary_, use_idf=True)
    expected_idf_len = len(vect.idf_)
    invalid_idf = [1.0] * (expected_idf_len + 1)
    with pytest.raises(ValueError):
        setattr(copy, "idf_", invalid_idf)


def test_non_unique_vocab():
    vocab = ["a", "b", "c", "a", "a"]
    vect = CountVectorizer(vocabulary=vocab)
    with pytest.raises(ValueError):
        vect.fit([])


def test_hashingvectorizer_nan_in_docs():
    # np.nan can appear when using pandas to load text fields from a csv file
    # with missing values.
    message = "np.nan is an invalid document, expected byte or unicode string."
    exception = ValueError

    def func():
        hv = HashingVectorizer()
        hv.fit_transform(["hello world", np.nan, "hello hello"])

    with pytest.raises(exception, match=message):
        func()


def test_tfidfvectorizer_binary():
    # Non-regression test: TfidfVectorizer used to ignore its "binary" param.
    v = TfidfVectorizer(binary=True, use_idf=False, norm=None)
    assert v.binary

    X = v.fit_transform(["hello world", "hello hello"]).toarray()
    assert_array_equal(X.ravel(), [1, 1, 1, 0])
    X2 = v.transform(["hello world", "hello hello"]).toarray()
    assert_array_equal(X2.ravel(), [1, 1, 1, 0])


def test_tfidfvectorizer_export_idf():
    vect = TfidfVectorizer(use_idf=True)
    vect.fit(JUNK_FOOD_DOCS)
    assert_array_almost_equal(vect.idf_, vect._tfidf.idf_)


def test_vectorizer_vocab_clone():
    vect_vocab = TfidfVectorizer(vocabulary=["the"])
    vect_vocab_clone = clone(vect_vocab)
    vect_vocab.fit(ALL_FOOD_DOCS)
    vect_vocab_clone.fit(ALL_FOOD_DOCS)
    assert vect_vocab_clone.vocabulary_ == vect_vocab.vocabulary_


@pytest.mark.parametrize(
    "Vectorizer", (CountVectorizer, TfidfVectorizer, HashingVectorizer)
)
def test_vectorizer_string_object_as_input(Vectorizer):
    message = "Iterable over raw text documents expected, string object received."
    vec = Vectorizer()

    with pytest.raises(ValueError, match=message):
        vec.fit_transform("hello world!")

    with pytest.raises(ValueError, match=message):
        vec.fit("hello world!")
    vec.fit(["some text", "some other text"])

    with pytest.raises(ValueError, match=message):
        vec.transform("hello world!")


@pytest.mark.parametrize("X_dtype", [np.float32, np.float64])
def test_tfidf_transformer_type(X_dtype):
    X = sparse.rand(10, 20000, dtype=X_dtype, random_state=42)
    X_trans = TfidfTransformer().fit_transform(X)
    assert X_trans.dtype == X.dtype


@pytest.mark.parametrize(
    "csc_container, csr_container", product(CSC_CONTAINERS, CSR_CONTAINERS)
)
def test_tfidf_transformer_sparse(csc_container, csr_container):
    X = sparse.rand(10, 20000, dtype=np.float64, random_state=42)
    X_csc = csc_container(X)
    X_csr = csr_container(X)

    X_trans_csc = TfidfTransformer().fit_transform(X_csc)
    X_trans_csr = TfidfTransformer().fit_transform(X_csr)
    assert_allclose_dense_sparse(X_trans_csc, X_trans_csr)
    assert X_trans_csc.format == X_trans_csr.format


@pytest.mark.parametrize(
    "vectorizer_dtype, output_dtype, warning_expected",
    [
        (np.int32, np.float64, True),
        (np.int64, np.float64, True),
        (np.float32, np.float32, False),
        (np.float64, np.float64, False),
    ],
)
def test_tfidf_vectorizer_type(vectorizer_dtype, output_dtype, warning_expected):
    X = np.array(["numpy", "scipy", "sklearn"])
    vectorizer = TfidfVectorizer(dtype=vectorizer_dtype)

    warning_msg_match = "'dtype' should be used."
    if warning_expected:
        with pytest.warns(UserWarning, match=warning_msg_match):
            X_idf = vectorizer.fit_transform(X)
    else:
        with warnings.catch_warnings():
            warnings.simplefilter("error", UserWarning)
            X_idf = vectorizer.fit_transform(X)
    assert X_idf.dtype == output_dtype


@pytest.mark.parametrize(
    "vec",
    [
        HashingVectorizer(ngram_range=(2, 1)),
        CountVectorizer(ngram_range=(2, 1)),
        TfidfVectorizer(ngram_range=(2, 1)),
    ],
)
def test_vectorizers_invalid_ngram_range(vec):
    # vectorizers could be initialized with invalid ngram range
    # test for raising error message
    invalid_range = vec.ngram_range
    message = re.escape(
        f"Invalid value for ngram_range={invalid_range} "
        "lower boundary larger than the upper boundary."
    )

    with pytest.raises(ValueError, match=message):
        vec.fit(["good news everyone"])

    with pytest.raises(ValueError, match=message):
        vec.fit_transform(["good news everyone"])

    if isinstance(vec, HashingVectorizer):
        with pytest.raises(ValueError, match=message):
            vec.transform(["good news everyone"])


def _check_stop_words_consistency(estimator):
    stop_words = estimator.get_stop_words()
    tokenize = estimator.build_tokenizer()
    preprocess = estimator.build_preprocessor()
    return estimator._check_stop_words_consistency(stop_words, preprocess, tokenize)


def test_vectorizer_stop_words_inconsistent():
    lstr = r"\['and', 'll', 've'\]"
    message = (
        "Your stop_words may be inconsistent with your "
        "preprocessing. Tokenizing the stop words generated "
        "tokens %s not in stop_words." % lstr
    )
    for vec in [CountVectorizer(), TfidfVectorizer(), HashingVectorizer()]:
        vec.set_params(stop_words=["you've", "you", "you'll", "AND"])
        with pytest.warns(UserWarning, match=message):
            vec.fit_transform(["hello world"])
        # reset stop word validation
        del vec._stop_words_id
        assert _check_stop_words_consistency(vec) is False

    # Only one warning per stop list
    with warnings.catch_warnings():
        warnings.simplefilter("error", UserWarning)
        vec.fit_transform(["hello world"])
    assert _check_stop_words_consistency(vec) is None

    # Test caching of inconsistency assessment
    vec.set_params(stop_words=["you've", "you", "you'll", "blah", "AND"])
    with pytest.warns(UserWarning, match=message):
        vec.fit_transform(["hello world"])


@skip_if_32bit
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_countvectorizer_sort_features_64bit_sparse_indices(csr_container):
    """
    Check that CountVectorizer._sort_features preserves the dtype of its sparse
    feature matrix.

    This test is skipped on 32bit platforms, see:
        https://github.com/scikit-learn/scikit-learn/pull/11295
    for more details.
    """

    X = csr_container((5, 5), dtype=np.int64)

    # force indices and indptr to int64.
    INDICES_DTYPE = np.int64
    X.indices = X.indices.astype(INDICES_DTYPE)
    X.indptr = X.indptr.astype(INDICES_DTYPE)

    vocabulary = {"scikit-learn": 0, "is": 1, "great!": 2}

    Xs = CountVectorizer()._sort_features(X, vocabulary)

    assert INDICES_DTYPE == Xs.indices.dtype


@pytest.mark.parametrize(
    "Estimator", [CountVectorizer, TfidfVectorizer, HashingVectorizer]
)
def test_stop_word_validation_custom_preprocessor(Estimator):
    data = [{"text": "some text"}]

    vec = Estimator()
    assert _check_stop_words_consistency(vec) is True

    vec = Estimator(preprocessor=lambda x: x["text"], stop_words=["and"])
    assert _check_stop_words_consistency(vec) == "error"
    # checks are cached
    assert _check_stop_words_consistency(vec) is None
    vec.fit_transform(data)

    class CustomEstimator(Estimator):
        def build_preprocessor(self):
            return lambda x: x["text"]

    vec = CustomEstimator(stop_words=["and"])
    assert _check_stop_words_consistency(vec) == "error"

    vec = Estimator(
        tokenizer=lambda doc: re.compile(r"\w{1,}").findall(doc), stop_words=["and"]
    )
    assert _check_stop_words_consistency(vec) is True


@pytest.mark.parametrize(
    "Estimator", [CountVectorizer, TfidfVectorizer, HashingVectorizer]
)
@pytest.mark.parametrize(
    "input_type, err_type, err_msg",
    [
        ("filename", FileNotFoundError, ""),
        ("file", AttributeError, "'str' object has no attribute 'read'"),
    ],
)
def test_callable_analyzer_error(Estimator, input_type, err_type, err_msg):
    data = ["this is text, not file or filename"]
    with pytest.raises(err_type, match=err_msg):
        Estimator(analyzer=lambda x: x.split(), input=input_type).fit_transform(data)


@pytest.mark.parametrize(
    "Estimator",
    [
        CountVectorizer,
        TfidfVectorizer,
        pytest.param(HashingVectorizer),
    ],
)
@pytest.mark.parametrize(
    "analyzer", [lambda doc: open(doc, "r"), lambda doc: doc.read()]
)
@pytest.mark.parametrize("input_type", ["file", "filename"])
def test_callable_analyzer_change_behavior(Estimator, analyzer, input_type):
    data = ["this is text, not file or filename"]
    with pytest.raises((FileNotFoundError, AttributeError)):
        Estimator(analyzer=analyzer, input=input_type).fit_transform(data)


@pytest.mark.parametrize(
    "Estimator", [CountVectorizer, TfidfVectorizer, HashingVectorizer]
)
def test_callable_analyzer_reraise_error(tmpdir, Estimator):
    # check if a custom exception from the analyzer is shown to the user
    def analyzer(doc):
        raise Exception("testing")

    f = tmpdir.join("file.txt")
    f.write("sample content\n")

    with pytest.raises(Exception, match="testing"):
        Estimator(analyzer=analyzer, input="file").fit_transform([f])


@pytest.mark.parametrize(
    "Vectorizer", [CountVectorizer, HashingVectorizer, TfidfVectorizer]
)
@pytest.mark.parametrize(
    (
        "stop_words, tokenizer, preprocessor, ngram_range, token_pattern,"
        "analyzer, unused_name, ovrd_name, ovrd_msg"
    ),
    [
        (
            ["you've", "you'll"],
            None,
            None,
            (1, 1),
            None,
            "char",
            "'stop_words'",
            "'analyzer'",
            "!= 'word'",
        ),
        (
            None,
            lambda s: s.split(),
            None,
            (1, 1),
            None,
            "char",
            "'tokenizer'",
            "'analyzer'",
            "!= 'word'",
        ),
        (
            None,
            lambda s: s.split(),
            None,
            (1, 1),
            r"\w+",
            "word",
            "'token_pattern'",
            "'tokenizer'",
            "is not None",
        ),
        (
            None,
            None,
            lambda s: s.upper(),
            (1, 1),
            r"\w+",
            lambda s: s.upper(),
            "'preprocessor'",
            "'analyzer'",
            "is callable",
        ),
        (
            None,
            None,
            None,
            (1, 2),
            None,
            lambda s: s.upper(),
            "'ngram_range'",
            "'analyzer'",
            "is callable",
        ),
        (
            None,
            None,
            None,
            (1, 1),
            r"\w+",
            "char",
            "'token_pattern'",
            "'analyzer'",
            "!= 'word'",
        ),
    ],
)
def test_unused_parameters_warn(
    Vectorizer,
    stop_words,
    tokenizer,
    preprocessor,
    ngram_range,
    token_pattern,
    analyzer,
    unused_name,
    ovrd_name,
    ovrd_msg,
):
    train_data = JUNK_FOOD_DOCS
    # setting parameter and checking for corresponding warning messages
    vect = Vectorizer()
    vect.set_params(
        stop_words=stop_words,
        tokenizer=tokenizer,
        preprocessor=preprocessor,
        ngram_range=ngram_range,
        token_pattern=token_pattern,
        analyzer=analyzer,
    )
    msg = "The parameter %s will not be used since %s %s" % (
        unused_name,
        ovrd_name,
        ovrd_msg,
    )
    with pytest.warns(UserWarning, match=msg):
        vect.fit(train_data)


@pytest.mark.parametrize(
    "Vectorizer, X",
    (
        (HashingVectorizer, [{"foo": 1, "bar": 2}, {"foo": 3, "baz": 1}]),
        (CountVectorizer, JUNK_FOOD_DOCS),
    ),
)
def test_n_features_in(Vectorizer, X):
    # For vectorizers, n_features_in_ does not make sense
    vectorizer = Vectorizer()
    assert not hasattr(vectorizer, "n_features_in_")
    vectorizer.fit(X)
    assert not hasattr(vectorizer, "n_features_in_")


def test_tie_breaking_sample_order_invariance():
    # Checks the sample order invariance when setting max_features
    # non-regression test for #17939
    vec = CountVectorizer(max_features=1)
    vocab1 = vec.fit(["hello", "world"]).vocabulary_
    vocab2 = vec.fit(["world", "hello"]).vocabulary_
    assert vocab1 == vocab2


def test_nonnegative_hashing_vectorizer_result_indices():
    # add test for pr 19035
    hashing = HashingVectorizer(n_features=1000000, ngram_range=(2, 3))
    indices = hashing.transform(["22pcs efuture"]).indices
    assert indices[0] >= 0


@pytest.mark.parametrize(
    "Estimator", [CountVectorizer, TfidfVectorizer, TfidfTransformer, HashingVectorizer]
)
def test_vectorizers_do_not_have_set_output(Estimator):
    """Check that vectorizers do not define set_output."""
    est = Estimator()
    assert not hasattr(est, "set_output")


@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_tfidf_transformer_copy(csr_container):
    """Check the behaviour of TfidfTransformer.transform with the copy parameter."""
    X = sparse.rand(10, 20000, dtype=np.float64, random_state=42)
    X_csr = csr_container(X)

    # keep a copy of the original matrix for later comparison
    X_csr_original = X_csr.copy()

    transformer = TfidfTransformer().fit(X_csr)

    X_transform = transformer.transform(X_csr, copy=True)
    assert_allclose_dense_sparse(X_csr, X_csr_original)
    assert X_transform is not X_csr

    X_transform = transformer.transform(X_csr, copy=False)
    assert X_transform is X_csr
    with pytest.raises(AssertionError):
        assert_allclose_dense_sparse(X_csr, X_csr_original)


@pytest.mark.parametrize("dtype", [np.float32, np.float64])
def test_tfidf_vectorizer_perserve_dtype_idf(dtype):
    """Check that `idf_` has the same dtype as the input data.

    Non-regression test for:
    https://github.com/scikit-learn/scikit-learn/issues/30016
    """
    X = [str(uuid.uuid4()) for i in range(100_000)]
    vectorizer = TfidfVectorizer(dtype=dtype).fit(X)
    assert vectorizer.idf_.dtype == dtype


def test_hashing_vectorizer_requires_fit_tag():
    """Test that HashingVectorizer has requires_fit=False tag."""
    vectorizer = HashingVectorizer()
    tags = vectorizer.__sklearn_tags__()
    assert not tags.requires_fit


def test_hashing_vectorizer_transform_without_fit():
    """Test that HashingVectorizer can transform without fitting."""
    vectorizer = HashingVectorizer(n_features=10)
    corpus = ["This is test", "Another test"]
    result = vectorizer.transform(corpus)
    assert result.shape == (2, 10)
