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_sac_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():
@@ -117,7 +105,8 @@ def test_sac_processor_normalization_modes():
     # Create test data
     observation = {OBS_STATE: torch.randn(10) * 2}  # Larger values to test normalization
     action = torch.rand(5) * 2 - 1  # Range [-1, 1]
-    transition = create_transition(observation, action)
+    transition = create_transition(observation=observation)
+    transition[TransitionKey.ACTION] = action
 
     # Process through preprocessor
     processed = preprocessor(transition)
@@ -129,7 +118,8 @@ def test_sac_processor_normalization_modes():
     assert processed[TransitionKey.ACTION].shape == (1, 5)
 
     # 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 (but still batched)
@@ -153,7 +143,8 @@ def test_sac_processor_cuda():
     # Create CPU data
     observation = {OBS_STATE: torch.randn(10)}
     action = torch.randn(5)
-    transition = create_transition(observation, action)
+    transition = create_transition(observation=observation)
+    transition[TransitionKey.ACTION] = action
 
     # Process through preprocessor
     processed = preprocessor(transition)
@@ -163,7 +154,8 @@ def test_sac_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
@@ -188,7 +180,8 @@ def test_sac_processor_accelerate_scenario():
     device = torch.device("cuda:0")
     observation = {OBS_STATE: torch.randn(10).to(device)}
     action = torch.randn(5).to(device)
-    transition = create_transition(observation, action)
+    transition = create_transition(observation=observation)
+    transition[TransitionKey.ACTION] = action
 
     # Process through preprocessor
     processed = preprocessor(transition)
@@ -216,7 +209,8 @@ def test_sac_processor_multi_gpu():
     device = torch.device("cuda:1")
     observation = {OBS_STATE: torch.randn(10).to(device)}
     action = torch.randn(5).to(device)
-    transition = create_transition(observation, action)
+    transition = create_transition(observation=observation)
+    transition[TransitionKey.ACTION] = action
 
     # Process through preprocessor
     processed = preprocessor(transition)
@@ -254,7 +248,8 @@ def test_sac_processor_without_stats():
     # Process should still work
     observation = {OBS_STATE: torch.randn(10)}
     action = torch.randn(5)
-    transition = create_transition(observation, action)
+    transition = create_transition(observation=observation)
+    transition[TransitionKey.ACTION] = action
 
     processed = preprocessor(transition)
     assert processed is not None
@@ -284,7 +279,8 @@ def test_sac_processor_save_and_load():
         # Test that loaded processor works
         observation = {OBS_STATE: torch.randn(10)}
         action = torch.randn(5)
-        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, 10)
@@ -329,7 +325,8 @@ def test_sac_processor_mixed_precision():
     # Create test data
     observation = {OBS_STATE: torch.randn(10, dtype=torch.float32)}
     action = torch.randn(5, dtype=torch.float32)
-    transition = create_transition(observation, action)
+    transition = create_transition(observation=observation)
+    transition[TransitionKey.ACTION] = action
 
     # Process through preprocessor
     processed = preprocessor(transition)
@@ -355,7 +352,8 @@ def test_sac_processor_batch_data():
     batch_size = 32
     observation = {OBS_STATE: torch.randn(batch_size, 10)}
     action = torch.randn(batch_size, 5)
-    transition = create_transition(observation, action)
+    transition = create_transition(observation=observation)
+    transition[TransitionKey.ACTION] = action
 
     # Process through preprocessor
     processed = preprocessor(transition)
@@ -378,13 +376,14 @@ def test_sac_processor_edge_cases():
     )
 
     # Test with empty observation
-    transition = create_transition(observation={}, action=torch.randn(5))
+    transition = create_transition(observation={})
+    transition[TransitionKey.ACTION] = torch.randn(5)
     processed = preprocessor(transition)
     assert processed[TransitionKey.OBSERVATION] == {}
     assert processed[TransitionKey.ACTION].shape == (1, 5)
 
     # Test with None action
-    transition = create_transition(observation={OBS_STATE: torch.randn(10)}, action=None)
+    transition = create_transition(observation={OBS_STATE: torch.randn(10)})
     processed = preprocessor(transition)
     assert processed[TransitionKey.OBSERVATION][OBS_STATE].shape == (1, 10)
     # When action is None, it may still be present with None value
@@ -433,7 +432,8 @@ def test_sac_processor_bfloat16_device_float32_normalizer():
     # Create test data
     observation = {OBS_STATE: torch.randn(10, dtype=torch.float32)}  # Start with float32
     action = torch.randn(5, dtype=torch.float32)
-    transition = create_transition(observation, action)
+    transition = create_transition(observation=observation)
+    transition[TransitionKey.ACTION] = action
 
     # Process through full pipeline
     processed = preprocessor(transition)