From e8663dd3f871f09be4a56c68bda3562f43b2d97c Mon Sep 17 00:00:00 2001
From: Daniel Walmsley <dan.walmsley@a8c.com>
Date: Mon, 8 Jul 2024 15:55:33 -0700
Subject: [PATCH] Comment out hack for now

---
 TTS/tts/layers/xtts/stream_generator.py | 59 ++++++++++++++-----------
 1 file changed, 32 insertions(+), 27 deletions(-)

diff --git a/TTS/tts/layers/xtts/stream_generator.py b/TTS/tts/layers/xtts/stream_generator.py
index 91905d3d..de3ae760 100644
--- a/TTS/tts/layers/xtts/stream_generator.py
+++ b/TTS/tts/layers/xtts/stream_generator.py
@@ -195,36 +195,41 @@ class NewGenerationMixin(GenerationMixin):
                 generation_config.pad_token_id,
                 generation_config.eos_token_id,
             )
-            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
-            )
+            # 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)
+            # # 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()
+            #     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,
-                )
+            #     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: