def load(cls, *args, **kwargs):             Save :class:`~gensim.models.fasttext.FastText` model.          """-        model = super(FastText, cls).load(*args, rethrow=True, **kwargs)--        if not hasattr(model.wv, 'vectors_vocab_lockf') and hasattr(model.wv, 'vectors_vocab'):-            # TODO: try trainables-location-            model.wv.vectors_vocab_lockf = ones(1, dtype=REAL)-        if not hasattr(model, 'vectors_ngrams_lockf') and hasattr(model.wv, 'vectors_ngrams'):-            # TODO: try trainables-location-            model.wv.vectors_ngrams_lockf = ones(1, dtype=REAL)-        # fixup mistakenly overdimensioned gensim-3.x lockf arrays-        if len(model.wv.vectors_vocab_lockf.shape) > 1:-            model.wv.vectors_vocab_lockf = ones(1, dtype=REAL)-        if len(model.wv.vectors_ngrams_lockf.shape) > 1:-            model.wv.vectors_ngrams_lockf = ones(1, dtype=REAL)-        if hasattr(model, 'bucket'):-            del model.bucket  # should only exist in one place: the wv subcomponent-        if not hasattr(model.wv, 'buckets_word') or not model.wv.buckets_word:-            model.wv.recalc_char_ngram_buckets()+        return super(FastText, cls).load(*args, rethrow=True, **kwargs)

 from collections import defaultdict, namedtuple from types import GeneratorType import threading-import itertools+import itertools as it

 def load(cls, *args, rethrow=False, **kwargs):             if not isinstance(model, Word2Vec):                 rethrow = True                 raise AttributeError("Model of type %s can't be loaded by %s" % (type(model), str(cls)))-            # for backward compatibility-            if not hasattr(model, 'ns_exponent'):-                model.ns_exponent = 0.75-            if model.negative and hasattr(model.wv, 'index2word'):-                model.make_cum_table()  # rebuild cum_table from vocabulary  ## TODO: ???-            if not hasattr(model, 'corpus_count'):-                model.corpus_count = None-            if not hasattr(model, 'corpus_total_words'):-                model.corpus_total_words = None-            if not hasattr(model.wv, 'vectors_lockf') and hasattr(model.wv, 'vectors'):-                model.wv.vectors_lockf = getattr(model, 'vectors_lockf', np.ones(1, dtype=REAL))-            if not hasattr(model, 'random'):-                model.random = np.random.RandomState(model.seed)-            if not hasattr(model, 'train_count'):-                model.train_count = 0-                model.total_train_time = 0-            if not hasattr(model, 'epochs'):-                model.epochs = model.iter-                del model.iter-            if not hasattr(model, 'max_final_vocab'):-                model.max_final_vocab = None-            if hasattr(model, 'vocabulary'):  # re-integrate state that had been moved-                for a in ('max_vocab_size', 'min_count', 'sample', 'sorted_vocab', 'null_word', 'raw_vocab'):-                    setattr(model, a, getattr(model.vocabulary, a))-                del model.vocabulary-            if hasattr(model, 'trainables'):  # re-integrate state that had been moved-                for a in ('hashfxn', 'layer1_size', 'seed', 'syn1neg', 'syn1'):-                    if hasattr(model.trainables, a):-                        setattr(model, a, getattr(model.trainables, a))-                if hasattr(model, 'syn1'):-                    model.syn1 = model.syn1-                    del model.syn1-                del model.trainables             return model         except AttributeError as ae:             if rethrow:                 raise ae             logger.error(                 "Model load error. Was model saved using code from an older Gensim Version? "-                "Try loading older model using gensim-3.8.1, then re-saving, to restore "+                "Try loading older model using gensim-3.8.3, then re-saving, to restore "                 "compatibility with current code.")             raise ae +    def _load_specials(self, *args, **kwargs):+        """Handle special requirements of `.load()` protocol, usually up-converting older versions."""+        super(Word2Vec, self)._load_specials(*args, **kwargs)+        # for backward compatibility, add/rearrange properties from prior versions+        if not hasattr(self, 'ns_exponent'):+            self.ns_exponent = 0.75+        if self.negative and hasattr(self.wv, 'index_to_key'):+            self.make_cum_table()  # rebuild cum_table from vocabulary+        if not hasattr(self, 'corpus_count'):+            self.corpus_count = None+        if not hasattr(self, 'corpus_total_words'):+            self.corpus_total_words = None+        if not hasattr(self.wv, 'vectors_lockf') and hasattr(self.wv, 'vectors'):+            self.wv.vectors_lockf = np.ones(1, dtype=REAL)+        if not hasattr(self, 'random'):+            # use new instance of numpy's recommended generator/algorithm+            self.random = np.random.default_rng(seed=self.seed)+        if not hasattr(self, 'train_count'):+            self.train_count = 0+            self.total_train_time = 0+        if not hasattr(self, 'epochs'):+            self.epochs = self.iter+            del self.iter+        if not hasattr(self, 'max_final_vocab'):+            self.max_final_vocab = None+        if hasattr(self, 'vocabulary'):  # re-integrate state that had been moved+            for a in ('max_vocab_size', 'min_count', 'sample', 'sorted_vocab', 'null_word', 'raw_vocab'):+                setattr(self, a, getattr(self.vocabulary, a))+            del self.vocabulary+        if hasattr(self, 'trainables'):  # re-integrate state that had been moved+            for a in ('hashfxn', 'layer1_size', 'seed', 'syn1neg', 'syn1'):+                if hasattr(self.trainables, a):+                    setattr(self, a, getattr(self.trainables, a))+            if hasattr(self, 'syn1'):+                self.syn1 = self.syn1+                del self.syn1+            del self.trainables++    def get_latest_training_loss(self):+        """Get current value of the training loss.++        Returns+        -------+        float+            Current training loss.++        """+        return self.running_training_loss+  class BrownCorpus(object):

 def load(cls, *args, **kwargs):             Save :class:`~gensim.models.fasttext.FastText` model.          """-        model = super(FastText, cls).load(*args, rethrow=True, **kwargs)--        if not hasattr(model.wv, 'vectors_vocab_lockf') and hasattr(model.wv, 'vectors_vocab'):-            # TODO: try trainables-location-            model.wv.vectors_vocab_lockf = ones(1, dtype=REAL)-        if not hasattr(model, 'vectors_ngrams_lockf') and hasattr(model.wv, 'vectors_ngrams'):-            # TODO: try trainables-location-            model.wv.vectors_ngrams_lockf = ones(1, dtype=REAL)-        # fixup mistakenly overdimensioned gensim-3.x lockf arrays-        if len(model.wv.vectors_vocab_lockf.shape) > 1:-            model.wv.vectors_vocab_lockf = ones(1, dtype=REAL)-        if len(model.wv.vectors_ngrams_lockf.shape) > 1:-            model.wv.vectors_ngrams_lockf = ones(1, dtype=REAL)-        if hasattr(model, 'bucket'):-            del model.bucket  # should only exist in one place: the wv subcomponent-        if not hasattr(model.wv, 'buckets_word') or not model.wv.buckets_word:-            model.wv.recalc_char_ngram_buckets()+        return super(FastText, cls).load(*args, rethrow=True, **kwargs)

 def load(cls, *args, rethrow=False, **kwargs):             if not isinstance(model, Word2Vec):                 rethrow = True                 raise AttributeError("Model of type %s can't be loaded by %s" % (type(model), str(cls)))-            # for backward compatibility-            if not hasattr(model, 'ns_exponent'):-                model.ns_exponent = 0.75-            if model.negative and hasattr(model.wv, 'index2word'):-                model.make_cum_table()  # rebuild cum_table from vocabulary  ## TODO: ???-            if not hasattr(model, 'corpus_count'):-                model.corpus_count = None-            if not hasattr(model, 'corpus_total_words'):-                model.corpus_total_words = None-            if not hasattr(model.wv, 'vectors_lockf') and hasattr(model.wv, 'vectors'):-                model.wv.vectors_lockf = getattr(model, 'vectors_lockf', np.ones(1, dtype=REAL))-            if not hasattr(model, 'random'):-                model.random = np.random.RandomState(model.seed)-            if not hasattr(model, 'train_count'):-                model.train_count = 0-                model.total_train_time = 0-            if not hasattr(model, 'epochs'):-                model.epochs = model.iter-                del model.iter-            if not hasattr(model, 'max_final_vocab'):-                model.max_final_vocab = None-            if hasattr(model, 'vocabulary'):  # re-integrate state that had been moved-                for a in ('max_vocab_size', 'min_count', 'sample', 'sorted_vocab', 'null_word', 'raw_vocab'):-                    setattr(model, a, getattr(model.vocabulary, a))-                del model.vocabulary-            if hasattr(model, 'trainables'):  # re-integrate state that had been moved-                for a in ('hashfxn', 'layer1_size', 'seed', 'syn1neg', 'syn1'):-                    if hasattr(model.trainables, a):-                        setattr(model, a, getattr(model.trainables, a))-                if hasattr(model, 'syn1'):-                    model.syn1 = model.syn1-                    del model.syn1-                del model.trainables             return model         except AttributeError as ae:             if rethrow:                 raise ae             logger.error(                 "Model load error. Was model saved using code from an older Gensim Version? "-                "Try loading older model using gensim-3.8.1, then re-saving, to restore "+                "Try loading older model using gensim-3.8.3, then re-saving, to restore "                 "compatibility with current code.")             raise ae +    def _load_specials(self, *args, **kwargs):+        """Handle special requirements of `.load()` protocol, usually up-converting older versions."""+        super(Word2Vec, self)._load_specials(*args, **kwargs)+        # for backward compatibility, add/rearrange properties from prior versions+        if not hasattr(self, 'ns_exponent'):+            self.ns_exponent = 0.75+        if self.negative and hasattr(self.wv, 'index_to_key'):+            self.make_cum_table()  # rebuild cum_table from vocabulary+        if not hasattr(self, 'corpus_count'):+            self.corpus_count = None+        if not hasattr(self, 'corpus_total_words'):+            self.corpus_total_words = None+        if not hasattr(self.wv, 'vectors_lockf') and hasattr(self.wv, 'vectors'):+            self.wv.vectors_lockf = np.ones(1, dtype=REAL)+        if not hasattr(self, 'random'):+            # use new instance of numpy's recommended generator/algorithm+            self.random = np.random.default_rng(seed=self.seed)+        if not hasattr(self, 'train_count'):+            self.train_count = 0+            self.total_train_time = 0+        if not hasattr(self, 'epochs'):+            self.epochs = self.iter+            del self.iter+        if not hasattr(self, 'max_final_vocab'):+            self.max_final_vocab = None+        if hasattr(self, 'vocabulary'):  # re-integrate state that had been moved+            for a in ('max_vocab_size', 'min_count', 'sample', 'sorted_vocab', 'null_word', 'raw_vocab'):+                setattr(self, a, getattr(self.vocabulary, a))+            del self.vocabulary+        if hasattr(self, 'trainables'):  # re-integrate state that had been moved+            for a in ('hashfxn', 'layer1_size', 'seed', 'syn1neg', 'syn1'):+                if hasattr(self.trainables, a):+                    setattr(self, a, getattr(self.trainables, a))+            if hasattr(self, 'syn1'):+                self.syn1 = self.syn1+                del self.syn1+            del self.trainables++    def get_latest_training_loss(self):+        """Get current value of the training loss.++        Returns+        -------+        float+            Current training loss.++        """+        return self.running_training_loss+  class BrownCorpus(object):

 def _load_word2vec_format(cls, fname, fvocab=None, binary=False, encoding='utf8'   def load_word2vec_format(*args, **kwargs):-    """Alias for `KeyedVectors.load_word2vec_format(...)`"""+    """Alias for :meth:`~gensim.models.keyedvectors.KeyedVectors.load_word2vec_format`."""     return KeyedVectors.load_word2vec_format(*args, **kwargs)   def pseudorandom_weak_vector(size, seed_string=None, hashfxn=hash):-    """Get a 'random' vector (but deterministically derived from seed_string if supplied).+    """Get a random vector, derived deterministically from `seed_string` if supplied.++    Useful for initializing KeyedVectors that will be the starting projection/input layers of _2Vec models.

 def load(cls, *args, **kwargs):             Save :class:`~gensim.models.fasttext.FastText` model.          """-        model = super(FastText, cls).load(*args, rethrow=True, **kwargs)--        if not hasattr(model.wv, 'vectors_vocab_lockf') and hasattr(model.wv, 'vectors_vocab'):-            # TODO: try trainables-location-            model.wv.vectors_vocab_lockf = ones(1, dtype=REAL)-        if not hasattr(model, 'vectors_ngrams_lockf') and hasattr(model.wv, 'vectors_ngrams'):-            # TODO: try trainables-location-            model.wv.vectors_ngrams_lockf = ones(1, dtype=REAL)-        # fixup mistakenly overdimensioned gensim-3.x lockf arrays-        if len(model.wv.vectors_vocab_lockf.shape) > 1:-            model.wv.vectors_vocab_lockf = ones(1, dtype=REAL)-        if len(model.wv.vectors_ngrams_lockf.shape) > 1:-            model.wv.vectors_ngrams_lockf = ones(1, dtype=REAL)-        if hasattr(model, 'bucket'):-            del model.bucket  # should only exist in one place: the wv subcomponent-        if not hasattr(model.wv, 'buckets_word') or not model.wv.buckets_word:-            model.wv.recalc_char_ngram_buckets()+        return super(FastText, cls).load(*args, rethrow=True, **kwargs)

 from collections import defaultdict, namedtuple from types import GeneratorType import threading-import itertools+import itertools as it

 def __init__(self, corpus=None, num_topics=100, id2word=None, workers=None,         minimum_probability : float, optional             Topics with a probability lower than this threshold will be filtered out.         random_state : {np.random.RandomState, int}, optional-            Either a randomState object or a seed to generate one. Useful for reproducibility.+            Either a randomState object or a seed to generate one. Useful for reproducibility. Note, that results can

 from collections import defaultdict, namedtuple from types import GeneratorType import threading-import itertools+import itertools as it

 def _raw_word_count(self, job):         """         return sum(len(sentence) for sentence in job) -    def _check_training_sanity(self, epochs=None, total_examples=None, total_words=None, **kwargs):+    def _check_corpus_sanity(self, corpus_iterable=None, corpus_file=None, passes=1):+        """Checks whether the corpus parameters make sense."""+        if corpus_file is None and corpus_iterable is None:+            raise TypeError("Either one of corpus_file or corpus_iterable value must be provided")+        if corpus_file is not None and corpus_iterable is not None:+            raise TypeError("Both corpus_file and corpus_iterable must not be provided at the same time")+        if corpus_iterable is None and not os.path.isfile(corpus_file):+            raise TypeError("Parameter corpus_file must be a valid path to a file, got %r instead" % corpus_file)+        if corpus_iterable is not None and not isinstance(corpus_iterable, Iterable):+            raise TypeError("The corpus_iterable must be an iterable of list, got %r instead" % corpus_iterable)+        if corpus_iterable is not None and isinstance(corpus_iterable, GeneratorType) and passes > 1:+            raise TypeError(+                f"Using a generator as corpus_iterable can't support {passes} passes. Try a re-iterable sequence.")

 def _raw_word_count(self, job):         """         return sum(len(sentence) for sentence in job) -    def _check_training_sanity(self, epochs=None, total_examples=None, total_words=None, **kwargs):+    def _check_corpus_sanity(self, corpus_iterable=None, corpus_file=None, passes=1):+        """Checks whether the corpus parameters make sense."""+        if corpus_file is None and corpus_iterable is None:+            raise TypeError("Either one of corpus_file or corpus_iterable value must be provided")+        if corpus_file is not None and corpus_iterable is not None:+            raise TypeError("Both corpus_file and corpus_iterable must not be provided at the same time")+        if corpus_iterable is None and not os.path.isfile(corpus_file):+            raise TypeError("Parameter corpus_file must be a valid path to a file, got %r instead" % corpus_file)+        if corpus_iterable is not None and not isinstance(corpus_iterable, Iterable):+            raise TypeError("The corpus_iterable must be an iterable of list, got %r instead" % corpus_iterable)+        if corpus_iterable is not None and isinstance(corpus_iterable, GeneratorType) and passes > 1:+            raise TypeError(+                f"Using a generator as corpus_iterable can't support {passes} passes. Try a re-iterable sequence.")

 def _raw_word_count(self, job):         """         return sum(len(sentence) for sentence in job) -    def _check_training_sanity(self, epochs=None, total_examples=None, total_words=None, **kwargs):+    def _check_corpus_sanity(self, corpus_iterable=None, corpus_file=None, passes=1):+        """Checks whether the corpus parameters make sense."""+        if corpus_file is None and corpus_iterable is None:+            raise TypeError("Either one of corpus_file or corpus_iterable value must be provided")+        if corpus_file is not None and corpus_iterable is not None:+            raise TypeError("Both corpus_file and corpus_iterable must not be provided at the same time")+        if corpus_iterable is None and not os.path.isfile(corpus_file):+            raise TypeError("Parameter corpus_file must be a valid path to a file, got %r instead" % corpus_file)+        if corpus_iterable is not None and not isinstance(corpus_iterable, Iterable):+            raise TypeError("The corpus_iterable must be an iterable of list, got %r instead" % corpus_iterable)+        if corpus_iterable is not None and isinstance(corpus_iterable, GeneratorType) and passes > 1:+            raise TypeError(+                f"Using a generator as corpus_iterable can't support {passes} passes. Try a re-iterable sequence.")

 def train(self, corpus_iterable=None, corpus_file=None, total_examples=None, tot          Parameters         -----------        sentences : iterable of list of str, optional-            The `sentences` iterable can be simply a list of lists of tokens, but for larger corpora,+        corpus_iterable : iterable of list of str, optional+            The `corpus_iterable` iterable can be simply a list of lists of tokens, but for larger corpora,

 def _raw_word_count(self, job):         """         return sum(len(sentence) for sentence in job) -    def _check_training_sanity(self, epochs=None, total_examples=None, total_words=None, **kwargs):+    def _check_corpus_sanity(self, corpus_iterable=None, corpus_file=None, passes=1):+        """Checks whether the corpus parameters make sense."""+        if corpus_file is None and corpus_iterable is None:+            raise TypeError("Either one of corpus_file or corpus_iterable value must be provided")+        if corpus_file is not None and corpus_iterable is not None:+            raise TypeError("Both corpus_file and corpus_iterable must not be provided at the same time")+        if corpus_iterable is None and not os.path.isfile(corpus_file):+            raise TypeError("Parameter corpus_file must be a valid path to a file, got %r instead" % corpus_file)+        if corpus_iterable is not None and not isinstance(corpus_iterable, Iterable):+            raise TypeError("The corpus_iterable must be an iterable of list, got %r instead" % corpus_iterable)+        if corpus_iterable is not None and isinstance(corpus_iterable, GeneratorType) and passes > 1:+            raise TypeError(+                f"Using a generator as corpus_iterable can't support {passes} passes. Try a re-iterable sequence.")

 def _raw_word_count(self, job):         """         return sum(len(sentence) for sentence in job) -    def _check_training_sanity(self, epochs=None, total_examples=None, total_words=None, **kwargs):+    def _check_corpus_sanity(self, corpus_iterable=None, corpus_file=None, passes=1):+        """Checks whether the corpus parameters make sense."""+        if corpus_file is None and corpus_iterable is None:+            raise TypeError("Either one of corpus_file or corpus_iterable value must be provided")+        if corpus_file is not None and corpus_iterable is not None:+            raise TypeError("Both corpus_file and corpus_iterable must not be provided at the same time")+        if corpus_iterable is None and not os.path.isfile(corpus_file):+            raise TypeError("Parameter corpus_file must be a valid path to a file, got %r instead" % corpus_file)+        if corpus_iterable is not None and not isinstance(corpus_iterable, Iterable):+            raise TypeError("The corpus_iterable must be an iterable of list, got %r instead" % corpus_iterable)+        if corpus_iterable is not None and isinstance(corpus_iterable, GeneratorType) and passes > 1:+            raise TypeError(+                f"Using a generator as corpus_iterable can't support {passes} passes. Try a re-iterable sequence.")++    def _check_training_sanity(self, epochs=0, total_examples=None, total_words=None, **kwargs):         """Checks whether the training parameters make sense. -        Called right before training starts in :meth:`~gensim.models.base_any2vec.BaseWordEmbeddingsModel.train`-        and raises warning or errors depending on the severity of the issue in case an inconsistent parameter-        combination is detected.-         Parameters         -----------        epochs : int, optional-            Number of training epochs. Must have a (non None) value.+        epochs : int+            Number of training epochs. Must have a positive value to pass check.

 def _raw_word_count(self, job):         """         return sum(len(sentence) for sentence in job) -    def _check_training_sanity(self, epochs=None, total_examples=None, total_words=None, **kwargs):+    def _check_corpus_sanity(self, corpus_iterable=None, corpus_file=None, passes=1):+        """Checks whether the corpus parameters make sense."""+        if corpus_file is None and corpus_iterable is None:+            raise TypeError("Either one of corpus_file or corpus_iterable value must be provided")+        if corpus_file is not None and corpus_iterable is not None:+            raise TypeError("Both corpus_file and corpus_iterable must not be provided at the same time")+        if corpus_iterable is None and not os.path.isfile(corpus_file):+            raise TypeError("Parameter corpus_file must be a valid path to a file, got %r instead" % corpus_file)+        if corpus_iterable is not None and not isinstance(corpus_iterable, Iterable):+            raise TypeError("The corpus_iterable must be an iterable of list, got %r instead" % corpus_iterable)

 from collections import defaultdict, namedtuple from types import GeneratorType import threading-import itertools+import itertools as it