chore: allow overriding chunker function

laygo/transformers/transformer.py

@@ -30,6 +30,20 @@ def createTransformer[T](_type_hint: type[T], chunk_size: int = DEFAULT_CHUNK_SI
   return Transformer[T, T](chunk_size=chunk_size)  # type: ignore
 
 
+def build_chunk_generator[T](chunk_size: int) -> Callable[[Iterable[T]], Iterator[list[T]]]:
+  """
+  Returns a function that breaks an iterable into chunks of a specified size.
+  This is useful for creating transformers that process data in manageable chunks.
+  """
+
+  def chunk_generator(data: Iterable[T]) -> Iterator[list[T]]:
+    data_iter = iter(data)
+    while chunk := list(itertools.islice(data_iter, chunk_size)):
+      yield chunk
+
+  return chunk_generator
+
+
 class Transformer[In, Out]:
   """
   Defines and composes data transformations by passing context explicitly.
@@ -45,6 +59,7 @@ def __init__(
     # The default transformer now accepts and ignores a context argument.
     self.transformer: InternalTransformer[In, Out] = transformer or (lambda chunk, ctx: chunk)  # type: ignore
     self.error_handler = ErrorHandler()
+    self._chunk_generator = build_chunk_generator(chunk_size) if chunk_size else lambda x: iter([list(x)])
 
   @classmethod
   def from_transformer[T, U](
@@ -58,6 +73,11 @@ def from_transformer[T, U](
       transformer=copy.deepcopy(transformer.transformer),  # type: ignore
     )
 
+  def set_chunker(self, chunker: Callable[[Iterable[In]], Iterator[list[In]]]) -> "Transformer[In, Out]":
+    """Sets a custom chunking function for the transformer."""
+    self._chunk_generator = chunker
+    return self
+
   def on_error(self, handler: ChunkErrorHandler[In, Out] | ErrorHandler) -> "Transformer[In, Out]":
     """Registers an error handler for the transformer."""
     # This method is a placeholder for future error handling logic.
@@ -69,12 +89,6 @@ def on_error(self, handler: ChunkErrorHandler[In, Out] | ErrorHandler) -> "Trans
         self.error_handler.on_error(handler)  # type: ignore
     return self
 
-  def _chunk_generator(self, data: Iterable[In]) -> Iterator[list[In]]:
-    """Breaks an iterable into chunks of a specified size."""
-    data_iter = iter(data)
-    while chunk := list(itertools.islice(data_iter, self.chunk_size)):
-      yield chunk
-
   def _pipe[U](self, operation: Callable[[list[Out], PipelineContext], list[U]]) -> "Transformer[In, U]":
     """Composes the current transformer with a new context-aware operation."""
     prev_transformer = self.transformer