Remove updates

torchtext/models/t5/model.py

@@ -18,11 +18,8 @@
 
 from .modules import DECODER_OUTPUTS_TYPE, ENCODER_OUTPUTS_TYPE, PAST_KEY_VALUES_TYPE, T5Decoder, T5Encoder
 
-# logging library is not automatically supported by Torchscript
-import warnings
-
 
-@dataclass(frozen=True)
+@dataclass
 class T5Conf:
     encoder_only: bool = False
     linear_head: bool = False
@@ -215,7 +212,6 @@ def prepare_inputs_for_generation(
             "return_past_key_values": return_past_key_values,
         }
 
-    @torch.jit.export
     def get_encoder(self) -> T5Encoder:
         return self.encoder
 
@@ -292,8 +288,6 @@ def forward(
 
             # decoder_tokens is None means at start of inference, in which case decoder sequence should begin with padding idx.
             if decoder_tokens is None:
-                batch_size = encoder_output.size()[0]
-                encoder_output_device = encoder_output.device
                 decoder_tokens = (
                     torch.ones((batch_size, 1), device=encoder_output_device, dtype=torch.long) * self.padding_idx
                 )
@@ -323,7 +317,7 @@ def forward(
                 # Rescale output before projecting on vocab. This happens when the encoder and decoder share the
                 # same word embeddings, which is always the case in our t5 implementation.
                 # See https://github.com/huggingface/transformers/blob/d0acc9537829e7d067edbb791473bbceb2ecf056/src/transformers/models/t5/modeling_t5.py#L1661
-                decoder_output = decoder_output * (self.embedding_dim**-0.5)
+                decoder_output = decoder_output * (self.embedding_dim ** -0.5)
                 decoder_output = self.lm_head(decoder_output)
                 decoder_outputs["decoder_output"] = decoder_output