Replace as_target context managers by direct calls (#18325)

* Preliminary work on tokenizers

* Quality + fix tests

* Treat processors

* Fix pad

* Remove all uses of  in tests, docs and examples

* Replace all as_target_tokenizer

* Fix tests

* Fix quality

* Update examples/flax/image-captioning/run_image_captioning_flax.py

Co-authored-by: amyeroberts <amy@huggingface.co>

* Style

Co-authored-by: amyeroberts <amy@huggingface.co>

examples/pytorch/question-answering/run_seq2seq_qa.py

@@ -453,9 +453,8 @@ def main():
         inputs, targets = preprocess_squad_batch(examples, question_column, context_column, answer_column)
 
         model_inputs = tokenizer(inputs, max_length=max_seq_length, padding=padding, truncation=True)
-        # Setup the tokenizer for targets
-        with tokenizer.as_target_tokenizer():
-            labels = tokenizer(targets, max_length=max_answer_length, padding=padding, truncation=True)
+        # Tokenize targets with text_target=...
+        labels = tokenizer(text_target=targets, max_length=max_answer_length, padding=padding, truncation=True)
 
         # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore
         # padding in the loss.
@@ -479,9 +478,8 @@ def main():
             return_overflowing_tokens=True,
             return_offsets_mapping=True,
         )
-        # Setup the tokenizer for targets
-        with tokenizer.as_target_tokenizer():
-            labels = tokenizer(targets, max_length=max_answer_length, padding=padding, truncation=True)
+        # Tokenize targets with the `text_target` keyword argument
+        labels = tokenizer(text_target=targets, max_length=max_answer_length, padding=padding, truncation=True)
 
         # Since one example might give us several features if it has a long context, we need a map from a feature to
         # its corresponding example. This key gives us just that.