How to use the spacy.util.ensure_path function in spacy

def test_issue4054(en_vocab):
    """Test that a new blank model can be made with a vocab from file,
    and that serialization does not drop the language at any point."""
    nlp1 = English()
    vocab1 = nlp1.vocab

    with make_tempdir() as d:
        vocab_dir = ensure_path(d / "vocab")
        if not vocab_dir.exists():
            vocab_dir.mkdir()
        vocab1.to_disk(vocab_dir)

        vocab2 = Vocab().from_disk(vocab_dir)
        print("lang", vocab2.lang)
        nlp2 = spacy.blank("en", vocab=vocab2)

        nlp_dir = ensure_path(d / "nlp")
        if not nlp_dir.exists():
            nlp_dir.mkdir()
        nlp2.to_disk(nlp_dir)
        nlp3 = spacy.load(nlp_dir)
        assert nlp3.lang == "en"

def test_serialize_kb_disk(en_vocab):
    # baseline assertions
    kb1 = _get_dummy_kb(en_vocab)
    _check_kb(kb1)

    # dumping to file & loading back in
    with make_tempdir() as d:
        dir_path = ensure_path(d)
        if not dir_path.exists():
            dir_path.mkdir()
        file_path = dir_path / "kb"
        kb1.dump(str(file_path))

        kb2 = KnowledgeBase(vocab=en_vocab, entity_vector_length=3)
        kb2.load_bulk(str(file_path))

    # final assertions
    _check_kb(kb2)

def package(input_dir, output_dir, meta_path=None, create_meta=False, force=False):
    """
    Generate Python package for model data, including meta and required
    installation files. A new directory will be created in the specified
    output directory, and model data will be copied over. If --create-meta is
    set and a meta.json already exists in the output directory, the existing
    values will be used as the defaults in the command-line prompt.
    """
    input_path = util.ensure_path(input_dir)
    output_path = util.ensure_path(output_dir)
    meta_path = util.ensure_path(meta_path)
    if not input_path or not input_path.exists():
        msg.fail("Can't locate model data", input_path, exits=1)
    if not output_path or not output_path.exists():
        msg.fail("Output directory not found", output_path, exits=1)
    if meta_path and not meta_path.exists():
        msg.fail("Can't find model meta.json", meta_path, exits=1)

    meta_path = meta_path or input_path / "meta.json"
    if meta_path.is_file():
        meta = srsly.read_json(meta_path)
        if not create_meta:  # only print if user doesn't want to overwrite
            msg.good("Loaded meta.json from file", meta_path)
        else:
            meta = generate_meta(input_dir, meta, msg)
    for key in ("lang", "name", "version"):
        if key not in meta or meta[key] == "":

def from_disk(self, path, exclude=tuple(), disable=None):
        """Loads state from a directory. Modifies the object in place and
        returns it. If the saved `Language` object contains a model, the
        model will be loaded.

        path (unicode or Path): A path to a directory.
        exclude (list): Names of components or serialization fields to exclude.
        RETURNS (Language): The modified `Language` object.

        DOCS: https://spacy.io/api/language#from_disk
        """
        if disable is not None:
            deprecation_warning(Warnings.W014)
            exclude = disable
        path = util.ensure_path(path)
        deserializers = OrderedDict()
        deserializers["meta.json"] = lambda p: self.meta.update(srsly.read_json(p))
        deserializers["vocab"] = lambda p: self.vocab.from_disk(
            p
        ) and _fix_pretrained_vectors_name(self)
        deserializers["tokenizer"] = lambda p: self.tokenizer.from_disk(
            p, exclude=["vocab"]
        )
        for name, proc in self.pipeline:
            if name in exclude:
                continue
            if not hasattr(proc, "from_disk"):
                continue
            deserializers[name] = lambda p, proc=proc: proc.from_disk(
                p, exclude=["vocab"]
            )

textcat_positive_label=None,
    verbose=False,
    debug=False,
):
    """
    Train or update a spaCy model. Requires data to be formatted in spaCy's
    JSON format. To convert data from other formats, use the `spacy convert`
    command.
    """
    util.fix_random_seed()
    util.set_env_log(verbose)

    # Make sure all files and paths exists if they are needed
    train_path = util.ensure_path(train_path)
    dev_path = util.ensure_path(dev_path)
    meta_path = util.ensure_path(meta_path)
    output_path = util.ensure_path(output_path)
    if raw_text is not None:
        raw_text = list(srsly.read_jsonl(raw_text))
    if not train_path or not train_path.exists():
        msg.fail("Training data not found", train_path, exits=1)
    if not dev_path or not dev_path.exists():
        msg.fail("Development data not found", dev_path, exits=1)
    if meta_path is not None and not meta_path.exists():
        msg.fail("Can't find model meta.json", meta_path, exits=1)
    meta = srsly.read_json(meta_path) if meta_path else {}
    if output_path.exists() and [p for p in output_path.iterdir() if p.is_dir()]:
        msg.warn(
            "Output directory is not empty",
            "This can lead to unintended side effects when saving the model. "
            "Please use an empty directory or a different path instead. If "
            "the specified output path doesn't exist, the directory will be "

gpu_id=-1,
    gold_preproc=False,
    displacy_path=None,
    displacy_limit=25,
    return_scores=False,
):
    """
    Evaluate a model. To render a sample of parses in a HTML file, set an
    output directory as the displacy_path argument.
    """
    util.fix_random_seed()
    if gpu_id >= 0:
        util.use_gpu(gpu_id)
    util.set_env_log(False)
    data_path = util.ensure_path(data_path)
    displacy_path = util.ensure_path(displacy_path)
    if not data_path.exists():
        msg.fail("Evaluation data not found", data_path, exits=1)
    if displacy_path and not displacy_path.exists():
        msg.fail("Visualization output directory not found", displacy_path, exits=1)
    corpus = GoldCorpus(data_path, data_path)
    nlp = util.load_model(model)
    dev_docs = list(corpus.dev_docs(nlp, gold_preproc=gold_preproc))
    begin = timer()
    scorer = nlp.evaluate(dev_docs, verbose=False)
    end = timer()
    nwords = sum(len(doc_gold[0]) for doc_gold in dev_docs)
    results = {
        "Time": "%.2f s" % (end - begin),
        "Words": nwords,
        "Words/s": "%.0f" % (nwords / (end - begin)),
        "TOK": "%.2f" % scorer.token_acc,

settings.append("-f")
            if clusters_loc:
                settings.append("-c")
            msg.warn(
                "Incompatible arguments",
                "The -f and -c arguments are deprecated, and not compatible "
                "with the -j argument, which should specify the same "
                "information. Either merge the frequencies and clusters data "
                "into the JSONL-formatted file (recommended), or use only the "
                "-f and -c files, without the other lexical attributes.",
            )
        jsonl_loc = ensure_path(jsonl_loc)
        lex_attrs = srsly.read_jsonl(jsonl_loc)
    else:
        clusters_loc = ensure_path(clusters_loc)
        freqs_loc = ensure_path(freqs_loc)
        if freqs_loc is not None and not freqs_loc.exists():
            msg.fail("Can't find words frequencies file", freqs_loc, exits=1)
        lex_attrs = read_attrs_from_deprecated(freqs_loc, clusters_loc)

    with msg.loading("Creating model..."):
        nlp = create_model(lang, lex_attrs, name=model_name)
    msg.good("Successfully created model")
    if vectors_loc is not None:
        add_vectors(nlp, vectors_loc, prune_vectors, vectors_name)
    vec_added = len(nlp.vocab.vectors)
    lex_added = len(nlp.vocab)
    msg.good(
        "Sucessfully compiled vocab",
        "{} entries, {} vectors".format(lex_added, vec_added),
    )
    if not output_dir.exists():

def make_vocab(cmd, lang, output_dir, lexemes_loc,
               vectors_loc=None, prune_vectors=-1):
    """Compile a vocabulary from a lexicon jsonl file and word vectors."""
    if not lexemes_loc.exists():
        prints(lexemes_loc, title="Can't find lexical data", exits=1)
    vectors_loc = ensure_path(vectors_loc)
    nlp = spacy.blank(lang)
    for word in nlp.vocab:
        word.rank = 0
    lex_added = 0
    with lexemes_loc.open() as file_:
        for line in file_:
            if line.strip():
                attrs = json.loads(line)
                if 'settings' in attrs:
                    nlp.vocab.cfg.update(attrs['settings'])
                else:
                    lex = nlp.vocab[attrs['orth']]
                    lex.set_attrs(**attrs)
                    assert lex.rank == attrs['id']
                lex_added += 1
    if vectors_loc is not None:

def init_model(lang, output_dir, freqs_loc=None,
               vectors_loc=None, no_expand_vectors=False,
               meta_overrides=None, prune_vectors=-1, min_word_frequency=50):
    """
    Create a new model from raw data, like word frequencies, Brown clusters
    and word vectors.
    """
    output_dir = ensure_path(output_dir)
    if vectors_loc is not None:
        vectors_loc = cached_path(vectors_loc)
        vectors_loc = ensure_path(vectors_loc)
    if freqs_loc is not None:
        freqs_loc = cached_path(freqs_loc)
        freqs_loc = ensure_path(freqs_loc)

    if freqs_loc is not None and not freqs_loc.exists():
        msg.fail("Can't find words frequencies file", freqs_loc, exits=1)
    probs, oov_prob = read_freqs(freqs_loc, min_freq=min_word_frequency) if freqs_loc is not None else ({}, -20)
    vectors_data, vector_keys = read_vectors(vectors_loc) if vectors_loc else (None, None)
    nlp = create_model(lang, probs, oov_prob, vectors_data, vector_keys, not no_expand_vectors, prune_vectors)

    # Insert our custom tokenizer into the base model.
    #nlp.tokenizer = combined_rule_tokenizer(nlp)

    nlp.tokenizer = Tokenizer(nlp.vocab)

    if meta_overrides is not None:

def from_disk(self, path, **kwargs):
        """Load the entity ruler from a file. Expects a file containing
        newline-delimited JSON (JSONL) with one entry per line.

        path (unicode / Path): The JSONL file to load.
        **kwargs: Other config paramters, mostly for consistency.
        RETURNS (EntityRuler): The loaded entity ruler.

        DOCS: https://spacy.io/api/entityruler#from_disk
        """
        path = ensure_path(path)
        depr_patterns_path = path.with_suffix(".jsonl")
        if depr_patterns_path.is_file():
            patterns = srsly.read_jsonl(depr_patterns_path)
            self.add_patterns(patterns)
        else:
            cfg = {}
            deserializers_patterns = {
                "patterns": lambda p: self.add_patterns(
                    srsly.read_jsonl(p.with_suffix(".jsonl"))
                )
            }
            deserializers_cfg = {"cfg": lambda p: cfg.update(srsly.read_json(p))}
            from_disk(path, deserializers_cfg, {})
            self.overwrite = cfg.get("overwrite", False)
            self.phrase_matcher_attr = cfg.get("phrase_matcher_attr")
            self.ent_id_sep = cfg.get("ent_id_sep", DEFAULT_ENT_ID_SEP)