rewrite minibatch_by_words function

spacy/util.py

@@ -656,45 +656,47 @@ def decaying(start, stop, decay):
         curr -= decay
 
 
-def minibatch_by_words(examples, size, tuples=True, count_words=len, tolerance=0.2):
+def minibatch_by_words(examples, size, count_words=len, tolerance=0.2, discard_oversize=False):
     """Create minibatches of roughly a given number of words. If any examples
     are longer than the specified batch length, they will appear in a batch by
-    themselves."""
+    themselves, or be discarded if discard_oversize=True."""
     if isinstance(size, int):
         size_ = itertools.repeat(size)
     elif isinstance(size, List):
         size_ = iter(size)
     else:
         size_ = size
-    examples = iter(examples)
-    oversize = []
-    while True:
-        batch_size = next(size_)
-        tol_size = batch_size * 0.2
-        batch = []
-        if oversize:
-            example = oversize.pop(0)
-            n_words = count_words(example.doc)
+
+    target_size = next(size_)
+    tol_size = target_size * tolerance
+    batch = []
+    current_size = 0
+
+    for example in examples:
+        n_words = count_words(example.doc)
+        # add the example to the current batch if it still fits
+        if (current_size + n_words) < (target_size + tol_size):
             batch.append(example)
-            batch_size -= n_words
-        while batch_size >= 1:
-            try:
-                example = next(examples)
-            except StopIteration:
-                if oversize:
-                    example = oversize.pop(0)
-                    batch.append(example)
-                if batch:
-                    yield batch
-                return
-            n_words = count_words(example.doc)
-            if n_words < (batch_size + tol_size):
-                batch_size -= n_words
-                batch.append(example)
+            current_size += n_words
+        else:
+            # if the current example exceeds the batch size, it is returned separately
+            # but only if discard_oversize=False.
+            if current_size > target_size:
+                if not discard_oversize:
+                    yield [example]
+            # yield the previous batch and start a new one
             else:
-                oversize.append(example)
-        if batch:
-            yield batch
+                yield batch
+                target_size = next(size_)
+                tol_size = target_size * tolerance
+                # In theory it may happen that the current example now exceeds the new target_size,
+                # but that seems like an unimportant edge case if batch sizes are variable anyway?
+                batch = [example]
+                current_size = n_words
+
+    # yield the final batch
+    if batch:
+        yield batch
 
 
 def itershuffle(iterable, bufsize=1000):