FIX Failing target_parameters param usage count

For testing target_parameters, we use a tiny Llama4 model. This model
was refactored in
huggingface/transformers#39501, resulting in one
parameter being accessed an additional time:

https://github.com/huggingface/transformers/pull/39501/files#diff-e668ec07f78afdb2cb805d939e47453757f0b9437436cb860fcb7cb2431c9cf5R69

Therefore, a unit test that relied on how often this parameter was
accessed started failing. This PR updates the count to the correct
number.

Additionally debug print statements that were accidentally left over are
now removed.

tests/test_target_parameters.py

@@ -370,7 +370,9 @@ def mock_forward(self, W):
             # Note: We call forward twice per step, once to create the parametrization and once for the actual forward
             # step. This may be a bit wasteful but it's not clear how to prevent this and overall is probably negligible
             num_forward_per_step = 2
-            expected_call_count = num_steps * num_layers * num_params * num_forward_per_step
+            # Since https://github.com/huggingface/transformers/pull/39501, one of the parameters is accessed twice per
+            # forward call, so add +1.
+            expected_call_count = num_steps * num_layers * (1 + num_params * num_forward_per_step)
             assert actual_call_count == expected_call_count
 
             actual_shapes = {W.shape for W in weights}
@@ -382,7 +384,6 @@ def mock_forward(self, W):
             lora_weights_before = {
                 k: v.clone() for k, v in model.named_parameters() if "lora_A.default" in k or "lora_B.default" in k
             }
-            print(lora_weights_before)
             # sanity check:
             assert len(lora_weights_before) == 2 * num_layers * num_params
             # train
@@ -394,7 +395,6 @@ def mock_forward(self, W):
                 loss.backward()
                 optim.step()
 
-            print(lora_weights_before)
             lora_weights_after = {
                 k: v for k, v in model.named_parameters() if "lora_A.default" in k or "lora_B.default" in k
             }