Merge pull request #6 from ringoldsdev/feat/20250722/tap-accepts-a-transformer

feat: tap accepts a transformer

Author: ringoldsdev

laygo/transformers/transformer.py

@@ -123,13 +123,54 @@ def flatten[T](
     """Flattens nested lists; the context is passed through the operation."""
     return self._pipe(lambda chunk, ctx: [item for sublist in chunk for item in sublist])  # type: ignore
 
-  def tap(self, function: PipelineFunction[Out, Any]) -> "Transformer[In, Out]":
-    """Applies a side-effect function without modifying the data."""
+  @overload
+  def tap(self, arg: "Transformer[Out, Any]") -> "Transformer[In, Out]": ...
 
-    if is_context_aware(function):
-      return self._pipe(lambda chunk, ctx: [x for x in chunk if function(x, ctx) or True])
+  @overload
+  def tap(self, arg: PipelineFunction[Out, Any]) -> "Transformer[In, Out]": ...
+
+  def tap(
+    self,
+    arg: Union["Transformer[Out, Any]", PipelineFunction[Out, Any]],
+  ) -> "Transformer[In, Out]":
+    """
+    Applies a side-effect without modifying the main data stream.
+
+    This method can be used in two ways:
+    1.  With a `Transformer`: Applies a sub-pipeline to each chunk for side-effects
+        (e.g., logging a chunk), discarding the sub-pipeline's output.
+    2.  With a `function`: Applies a function to each element individually for
+        side-effects (e.g., printing an item).
+
+    Args:
+        arg: A `Transformer` instance or a function to be applied for side-effects.
+
+    Returns:
+        The transformer instance for method chaining.
+    """
+    match arg:
+      # Case 1: The argument is another Transformer
+      case Transformer() as tapped_transformer:
+        tapped_func = tapped_transformer.transformer
+
+        def operation(chunk: list[Out], ctx: PipelineContext) -> list[Out]:
+          # Execute the tapped transformer's logic on the chunk for side-effects.
+          _ = tapped_func(chunk, ctx)
+          # Return the original chunk to continue the main pipeline.
+          return chunk
+
+        return self._pipe(operation)
+
+      # Case 2: The argument is a callable function
+      case function if callable(function):
+        if is_context_aware(function):
+          return self._pipe(lambda chunk, ctx: [x for x in chunk if function(x, ctx) or True])
+
+        return self._pipe(lambda chunk, _ctx: [x for x in chunk if function(x) or True])  # type: ignore
 
-    return self._pipe(lambda chunk, _ctx: [function(x) or x for x in chunk])  # type: ignore
+      # Default case for robustness
+      case _:
+        raise TypeError(f"tap() argument must be a Transformer or a callable, not {type(arg).__name__}")
 
   def apply[T](self, t: Callable[[Self], "Transformer[In, T]"]) -> "Transformer[In, T]":
     """Apply another pipeline to the current one."""

tests/test_transformer.py

@@ -85,6 +85,60 @@ def test_tap_with_context(self):
     assert result == [1, 2, 3]
     assert side_effects == ["item:1", "item:2", "item:3"]
 
+  def test_tap_with_transformer(self):
+    """Test tap with a transformer for side effects."""
+    side_effects = []
+
+    # Create a side-effect transformer that logs processed values
+    side_effect_transformer = (
+      createTransformer(int)
+      .map(lambda x: x * 10)  # Transform for side effect
+      .tap(lambda x: side_effects.append(x))  # Capture the transformed values
+    )
+
+    # Main transformer that uses the side-effect transformer via tap
+    main_transformer = (
+      createTransformer(int)
+      .map(lambda x: x * 2)  # Main transformation
+      .tap(side_effect_transformer)  # Apply side-effect transformer
+      .map(lambda x: x + 1)  # Continue main transformation
+    )
+
+    result = list(main_transformer([1, 2, 3]))
+
+    # Main pipeline should produce: [1,2,3] -> [2,4,6] -> [3,5,7]
+    assert result == [3, 5, 7]
+
+    # Side effects should capture: [2,4,6] -> [20,40,60]
+    assert side_effects == [20, 40, 60]
+
+  def test_tap_with_transformer_and_context(self):
+    """Test tap with a transformer that uses context."""
+    side_effects = []
+    context = PipelineContext({"multiplier": 5, "log_prefix": "processed:"})
+
+    # Create a context-aware side-effect transformer
+    side_effect_transformer = (
+      createTransformer(int)
+      .map(lambda x, ctx: x * ctx["multiplier"])  # Use context multiplier
+      .tap(lambda x, ctx: side_effects.append(f"{ctx['log_prefix']}{x}"))  # Log with context prefix
+    )
+
+    # Main transformer
+    main_transformer = (
+      createTransformer(int)
+      .map(lambda x: x + 10)  # Main transformation
+      .tap(side_effect_transformer)  # Apply side-effect transformer with context
+    )
+
+    result = list(main_transformer([1, 2, 3], context))
+
+    # Main pipeline: [1,2,3] -> [11,12,13]
+    assert result == [11, 12, 13]
+
+    # Side effects: [11,12,13] -> [55,60,65] -> ["processed:55", "processed:60", "processed:65"]
+    assert side_effects == ["processed:55", "processed:60", "processed:65"]
+
 
 class TestTransformerChaining:
   """Test chaining multiple transformer operations."""