Refactor prepare_seq2seq_batch (#9524)

* Add target contextmanager and rework prepare_seq2seq_batch

* Fix tests, treat BART and Barthez

* Add last tokenizers

* Fix test

* Set src token before calling the superclass

* Remove special behavior for T5

* Remove needless imports

* Remove needless asserts

src/transformers/models/rag/tokenization_rag.py

@@ -16,8 +16,7 @@
 import os
 from typing import List, Optional
 
-from ...file_utils import add_start_docstrings
-from ...tokenization_utils_base import PREPARE_SEQ2SEQ_BATCH_DOCSTRING, BatchEncoding
+from ...tokenization_utils_base import BatchEncoding
 from ...utils import logging
 from .configuration_rag import RagConfig
 
@@ -63,42 +62,18 @@ class RagTokenizer:
     def batch_decode(self, *args, **kwargs):
         return self.generator.batch_decode(*args, **kwargs)
 
-    @add_start_docstrings(PREPARE_SEQ2SEQ_BATCH_DOCSTRING)
     def prepare_seq2seq_batch(
         self,
         src_texts: List[str],
         tgt_texts: Optional[List[str]] = None,
         max_length: Optional[int] = None,
         max_target_length: Optional[int] = None,
-        padding: str = "longest",
-        return_tensors: str = None,
-        truncation=True,
         **kwargs,
     ) -> BatchEncoding:
         if max_length is None:
             max_length = self.question_encoder.model_max_length
-        model_inputs: BatchEncoding = self.question_encoder(
-            src_texts,
-            add_special_tokens=True,
-            return_tensors=return_tensors,
-            max_length=max_length,
-            padding=padding,
-            truncation=truncation,
-            **kwargs,
-        )
-        if tgt_texts is None:
-            return model_inputs
-        # Process tgt_texts
         if max_target_length is None:
             max_target_length = self.generator.model_max_length
-        labels = self.generator(
-            tgt_texts,
-            add_special_tokens=True,
-            return_tensors=return_tensors,
-            padding=padding,
-            max_length=max_target_length,
-            truncation=truncation,
-            **kwargs,
-        )["input_ids"]
-        model_inputs["labels"] = labels
-        return model_inputs
+        return super().prepare_seq2seq_batch(
+            src_texts, tgt_texts, max_length=max_length, max_target_length=max_target_length, **kwargs
+        )