refactor(tests): streamline transition creation in processor tests

- Replaced custom transition creation functions with a centralized `create_transition` function imported from converters across multiple test files.
- Updated test cases to utilize keyword arguments for better readability and maintainability, ensuring consistent transition creation throughout the test suite.

tests/processor/test_act_processor.py

@@ -33,19 +33,7 @@ from lerobot.processor import (
     TransitionKey,
     UnnormalizerProcessorStep,
 )
-
-
-def create_transition(observation=None, action=None, **kwargs):
-    """Helper function to create a transition dictionary."""
-    transition = {}
-    if observation is not None:
-        transition[TransitionKey.OBSERVATION] = observation
-    if action is not None:
-        transition[TransitionKey.ACTION] = action
-    for key, value in kwargs.items():
-        if hasattr(TransitionKey, key.upper()):
-            transition[getattr(TransitionKey, key.upper())] = value
-    return transition
+from lerobot.processor.converters import create_transition
 
 
 def create_default_config():
@@ -112,7 +100,8 @@ def test_act_processor_normalization():
     # Create test data
     observation = {OBS_STATE: torch.randn(7)}
     action = torch.randn(4)
-    transition = create_transition(observation, action)
+    transition = create_transition(observation=observation)
+    transition[TransitionKey.ACTION] = action
 
     # Process through preprocessor
     processed = preprocessor(transition)
@@ -122,7 +111,8 @@ def test_act_processor_normalization():
     assert processed[TransitionKey.ACTION].shape == (1, 4)
 
     # Process action through postprocessor
-    action_transition = create_transition(action=processed[TransitionKey.ACTION])
+    action_transition = create_transition()
+    action_transition[TransitionKey.ACTION] = processed[TransitionKey.ACTION]
     postprocessed = postprocessor(action_transition)
 
     # Check that action is unnormalized
@@ -146,7 +136,8 @@ def test_act_processor_cuda():
     # Create CPU data
     observation = {OBS_STATE: torch.randn(7)}
     action = torch.randn(4)
-    transition = create_transition(observation, action)
+    transition = create_transition(observation=observation)
+    transition[TransitionKey.ACTION] = action
 
     # Process through preprocessor
     processed = preprocessor(transition)
@@ -156,7 +147,8 @@ def test_act_processor_cuda():
     assert processed[TransitionKey.ACTION].device.type == "cuda"
 
     # Process through postprocessor
-    action_transition = create_transition(action=processed[TransitionKey.ACTION])
+    action_transition = create_transition()
+    action_transition[TransitionKey.ACTION] = processed[TransitionKey.ACTION]
     postprocessed = postprocessor(action_transition)
 
     # Check that action is back on CPU
@@ -181,7 +173,8 @@ def test_act_processor_accelerate_scenario():
     device = torch.device("cuda:0")
     observation = {OBS_STATE: torch.randn(1, 7).to(device)}  # Already batched and on GPU
     action = torch.randn(1, 4).to(device)
-    transition = create_transition(observation, action)
+    transition = create_transition(observation=observation)
+    transition[TransitionKey.ACTION] = action
 
     # Process through preprocessor
     processed = preprocessor(transition)
@@ -204,7 +197,8 @@ def test_act_processor_multi_gpu():
     device = torch.device("cuda:1")
     observation = {OBS_STATE: torch.randn(1, 7).to(device)}
     action = torch.randn(1, 4).to(device)
-    transition = create_transition(observation, action)
+    transition = create_transition(observation=observation)
+    transition[TransitionKey.ACTION] = action
 
     # Process through preprocessor
     processed = preprocessor(transition)
@@ -227,7 +221,8 @@ def test_act_processor_without_stats():
     # Process should still work (but won't normalize without stats)
     observation = {OBS_STATE: torch.randn(7)}
     action = torch.randn(4)
-    transition = create_transition(observation, action)
+    transition = create_transition(observation=observation)
+    transition[TransitionKey.ACTION] = action
 
     processed = preprocessor(transition)
     assert processed is not None
@@ -257,7 +252,8 @@ def test_act_processor_save_and_load():
         # Test that loaded processor works
         observation = {OBS_STATE: torch.randn(7)}
         action = torch.randn(4)
-        transition = create_transition(observation, action)
+        transition = create_transition(observation=observation)
+        transition[TransitionKey.ACTION] = action
 
         processed = loaded_preprocessor(transition)
         assert processed[TransitionKey.OBSERVATION][OBS_STATE].shape == (1, 7)
@@ -281,7 +277,8 @@ def test_act_processor_device_placement_preservation():
     # Process CPU data
     observation = {OBS_STATE: torch.randn(7)}
     action = torch.randn(4)
-    transition = create_transition(observation, action)
+    transition = create_transition(observation=observation)
+    transition[TransitionKey.ACTION] = action
 
     processed = preprocessor(transition)
     assert processed[TransitionKey.OBSERVATION][OBS_STATE].device.type == "cpu"
@@ -326,7 +323,8 @@ def test_act_processor_mixed_precision():
     # Create test data
     observation = {OBS_STATE: torch.randn(7, dtype=torch.float32)}
     action = torch.randn(4, dtype=torch.float32)
-    transition = create_transition(observation, action)
+    transition = create_transition(observation=observation)
+    transition[TransitionKey.ACTION] = action
 
     # Process through preprocessor
     processed = preprocessor(transition)
@@ -351,7 +349,8 @@ def test_act_processor_batch_consistency():
     # Test single sample (unbatched)
     observation = {OBS_STATE: torch.randn(7)}
     action = torch.randn(4)
-    transition = create_transition(observation, action)
+    transition = create_transition(observation=observation)
+    transition[TransitionKey.ACTION] = action
 
     processed = preprocessor(transition)
     assert processed[TransitionKey.OBSERVATION][OBS_STATE].shape[0] == 1  # Batched
@@ -359,7 +358,8 @@ def test_act_processor_batch_consistency():
     # Test already batched data
     observation_batched = {OBS_STATE: torch.randn(8, 7)}  # Batch of 8
     action_batched = torch.randn(8, 4)
-    transition_batched = create_transition(observation_batched, action_batched)
+    transition_batched = create_transition(observation=observation_batched)
+    transition_batched[TransitionKey.ACTION] = action_batched
 
     processed_batched = preprocessor(transition_batched)
     assert processed_batched[TransitionKey.OBSERVATION][OBS_STATE].shape[0] == 8
@@ -407,7 +407,8 @@ def test_act_processor_bfloat16_device_float32_normalizer():
     # Create test data
     observation = {OBS_STATE: torch.randn(7, dtype=torch.float32)}  # Start with float32
     action = torch.randn(4, dtype=torch.float32)
-    transition = create_transition(observation, action)
+    transition = create_transition(observation=observation)
+    transition[TransitionKey.ACTION] = action
 
     # Process through full pipeline
     processed = preprocessor(transition)