Remove unused code

TTS/tts/layers/xtts/stream_generator.py

@@ -24,21 +24,6 @@
 from transformers.generation.stopping_criteria import validate_stopping_criteria
 from transformers.generation.utils import GenerateOutput, SampleOutput, logger
 
-def custom_isin(elements, test_elements):
-    # Flatten the tensors
-    elements_flat = elements.view(-1)
-    test_elements_flat = test_elements.view(-1)
-
-    # Create a mask tensor
-    mask = torch.zeros_like(elements_flat, dtype=torch.bool)
-
-    # Compare each element
-    for test_element in test_elements_flat:
-        mask |= (elements_flat == test_element)
-
-    # Reshape the mask to the original elements shape
-    return mask.view(elements.shape)
-
 def setup_seed(seed: int) -> None:
     if seed == -1:
         return
@@ -195,41 +180,6 @@ def generate(  # noqa: PLR0911
                 generation_config.pad_token_id,
                 generation_config.eos_token_id,
             )
-            # pad_token_tensor = (
-            #     torch.tensor([generation_config.pad_token_id], device=inputs_tensor.device)
-            #     if generation_config.pad_token_id is not None
-            #     else None
-            # )
-            # eos_token_tensor = (
-            #     torch.tensor([generation_config.eos_token_id], device=inputs_tensor.device)
-            #     if generation_config.eos_token_id is not None
-            #     else None
-            # )
-
-            # # hack to produce attention mask for mps devices since transformers bails but pytorch supports torch.isin on mps now
-            # # for this to work, you must run with PYTORCH_ENABLE_MPS_FALLBACK=1 and call model.to(mps_device) on the XttsModel
-            # if inputs_tensor.device.type == "mps":
-            #     default_attention_mask = torch.ones(inputs_tensor.shape[:2], dtype=torch.long, device=inputs_tensor.device)
-
-            #     is_pad_token_in_inputs = (pad_token_tensor is not None) and (
-            #         custom_isin(elements=inputs_tensor, test_elements=pad_token_tensor).any()
-            #     )
-            #     is_pad_token_not_equal_to_eos_token_id = (eos_token_tensor is None) or ~(
-            #         custom_isin(elements=eos_token_tensor, test_elements=pad_token_tensor).any()
-            #     )
-            #     can_infer_attention_mask = is_pad_token_in_inputs * is_pad_token_not_equal_to_eos_token_id
-            #     attention_mask_from_padding = inputs_tensor.ne(pad_token_tensor).long()
-
-            #     model_kwargs["attention_mask"] = (
-            #         attention_mask_from_padding * can_infer_attention_mask
-            #         + default_attention_mask * ~can_infer_attention_mask
-            #     )
-            # else:
-            #     model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
-            #         inputs_tensor,
-            #         pad_token_tensor,
-            #         eos_token_tensor,
-            #     )
 
         # decoder-only models should use left-padding for generation
         if not self.config.is_encoder_decoder: