Fix TF T5 only encoder model with booleans (#8925)

src/transformers/models/t5/modeling_tf_t5.py

@@ -1336,14 +1336,6 @@ class TFT5ForConditionalGeneration(TFT5PreTrainedModel, TFCausalLanguageModeling
             output = (logits,) + decoder_outputs[1:] + inputs["encoder_outputs"]
             return ((loss,) + output) if loss is not None else output
 
-        # Putting this before breaks tf compilation.
-        output_attentions = (
-            output_attentions if inputs["output_attentions"] is not None else self.config.output_attentions
-        )
-        output_hidden_states = (
-            output_hidden_states if inputs["output_hidden_states"] is not None else self.config.output_hidden_states
-        )
-
         # This is long and annoying but if we introduce return_dict at the TFT5MainLayer level (like in PyTorch)
         # TF refuses to compile anymore.
         if not cast_bool_to_primitive(inputs["use_cache"], self.config.use_cache):
@@ -1481,6 +1473,7 @@ class TFT5EncoderModel(TFT5PreTrainedModel):
         """
         inputs = input_processing(
             func=self.call,
+            config=self.config,
             input_ids=input_ids,
             attention_mask=attention_mask,
             head_mask=head_mask,
@@ -1492,12 +1485,6 @@ class TFT5EncoderModel(TFT5PreTrainedModel):
             kwargs_call=kwargs,
         )
 
-        output_attentions = inputs["output_attentions"] if output_attentions else self.config.output_attentions
-        output_hidden_states = (
-            inputs["output_hidden_states"] if output_hidden_states else self.config.output_hidden_states
-        )
-        return_dict = return_dict if inputs["return_dict"] is not None else self.config.return_dict
-
         encoder_outputs = self.encoder(
             input_ids,
             attention_mask=inputs["attention_mask"],
@@ -1507,17 +1494,17 @@ class TFT5EncoderModel(TFT5PreTrainedModel):
             head_mask=head_mask,
             past_key_values=None,
             use_cache=False,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
+            output_attentions=inputs["output_attentions"],
+            output_hidden_states=inputs["output_hidden_states"],
             training=inputs["training"],
         )
 
-        if not return_dict:
+        if not inputs["return_dict"]:
             return encoder_outputs
 
-        if not cast_bool_to_primitive(output_hidden_states, self.config.output_hidden_states):
+        if not cast_bool_to_primitive(inputs["output_hidden_states"], self.config.output_hidden_states):
             encoder_outputs = encoder_outputs[:1] + (None,) + encoder_outputs[1:]
-        if not cast_bool_to_primitive(output_attentions, self.config.output_attentions):
+        if not cast_bool_to_primitive(inputs["output_attentions"], self.config.output_attentions):
             encoder_outputs = encoder_outputs + (None,)
 
         return TFBaseModelOutput(