Repository: huggingface/transformers
Pull request: Make canine model exportable by removing unncessary complicated logic

# What does this PR do?

In this code, char_seq_length is guaranteed to be an integer, so there is no need to cast it to tensor. By removing this, canine model is now exportable. 
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Changed files:
filename: src/transformers/models/canine/modeling_canine.py
patch: @@ -1056,7 +1056,7 @@ def _downsample_attention_mask(self, char_attention_mask: torch.Tensor, downsamp
 
         return molecule_attention_mask
 
-    def _repeat_molecules(self, molecules: torch.Tensor, char_seq_length: torch.Tensor) -> torch.Tensor:
+    def _repeat_molecules(self, molecules: torch.Tensor, char_seq_length: int) -> torch.Tensor:
         """Repeats molecules to make them the same length as the char sequence."""
 
         rate = self.config.downsampling_rate
@@ -1070,7 +1070,7 @@ def _repeat_molecules(self, molecules: torch.Tensor, char_seq_length: torch.Tens
         # n elements (n < `downsampling_rate`), i.e. the remainder of floor
         # division. We do this by repeating the last molecule a few extra times.
         last_molecule = molecules[:, -1:, :]
-        remainder_length = torch.fmod(torch.tensor(char_seq_length), torch.tensor(rate)).item()
+        remainder_length = char_seq_length % rate
         remainder_repeated = torch.repeat_interleave(
             last_molecule,
             # +1 molecule to compensate for truncation.