Replace as_target context managers by direct calls

src/transformers/models/m2m_100/tokenization_m2m_100.py

@@ -14,7 +14,6 @@
 """Tokenization classes for M2M100."""
 import json
 import os
-from contextlib import contextmanager
 from pathlib import Path
 from shutil import copyfile
 from typing import Any, Dict, List, Optional, Tuple, Union
@@ -346,16 +345,12 @@ def _build_translation_inputs(self, raw_inputs, src_lang: Optional[str], tgt_lan
         inputs["forced_bos_token_id"] = tgt_lang_id
         return inputs
 
-    @contextmanager
-    def as_target_tokenizer(self):
-        """
-        Temporarily sets the tokenizer for encoding the targets. Useful for tokenizer associated to
-        sequence-to-sequence models that need a slightly different processing for the labels.
-        """
-        self.set_tgt_lang_special_tokens(self.tgt_lang)
-        yield
+    def _switch_to_input_mode(self):
         self.set_src_lang_special_tokens(self.src_lang)
 
+    def _switch_to_target_mode(self):
+        self.set_tgt_lang_special_tokens(self.tgt_lang)
+
     def set_src_lang_special_tokens(self, src_lang: str) -> None:
         """Reset the special tokens to the source lang setting. No prefix and suffix=[eos, src_lang_code]."""
         lang_token = self.get_lang_token(src_lang)

src/transformers/models/marian/tokenization_marian.py

@@ -15,7 +15,6 @@
 import os
 import re
 import warnings
-from contextlib import contextmanager
 from pathlib import Path
 from shutil import copyfile
 from typing import Any, Dict, List, Optional, Tuple, Union
@@ -281,18 +280,14 @@ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None) -> Lis
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return token_ids_0 + token_ids_1 + [self.eos_token_id]
 
-    @contextmanager
-    def as_target_tokenizer(self):
-        """
-        Temporarily sets the tokenizer for encoding the targets. Useful for tokenizer associated to
-        sequence-to-sequence models that need a slightly different processing for the labels.
-        """
+    def _switch_to_input_mode(self):
+        self.current_spm = self.spm_source
+        self.current_encoder = self.encoder
+
+    def _switch_to_target_mode(self):
         self.current_spm = self.spm_target
         if self.separate_vocabs:
             self.current_encoder = self.target_encoder
-        yield
-        self.current_spm = self.spm_source
-        self.current_encoder = self.encoder
 
     @property
     def vocab_size(self) -> int:

src/transformers/models/mbart/tokenization_mbart.py

@@ -14,7 +14,6 @@
 # limitations under the License.
 
 import os
-from contextlib import contextmanager
 from shutil import copyfile
 from typing import Any, Dict, List, Optional, Tuple
 
@@ -340,15 +339,11 @@ def prepare_seq2seq_batch(
         self.tgt_lang = tgt_lang
         return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kwargs)
 
-    @contextmanager
-    def as_target_tokenizer(self):
-        """
-        Temporarily sets the tokenizer for encoding the targets. Useful for tokenizer associated to
-        sequence-to-sequence models that need a slightly different processing for the labels.
-        """
-        self.set_tgt_lang_special_tokens(self.tgt_lang)
-        yield
-        self.set_src_lang_special_tokens(self.src_lang)
+    def _switch_to_input_mode(self):
+        return self.set_src_lang_special_tokens(self.src_lang)
+
+    def _switch_to_target_mode(self):
+        return self.set_tgt_lang_special_tokens(self.tgt_lang)
 
     def set_src_lang_special_tokens(self, src_lang) -> None:
         """Reset the special tokens to the source lang setting. No prefix and suffix=[eos, src_lang_code]."""

src/transformers/models/mbart/tokenization_mbart_fast.py

@@ -14,7 +14,6 @@
 # limitations under the License.
 
 import os
-from contextlib import contextmanager
 from shutil import copyfile
 from typing import List, Optional, Tuple
 
@@ -240,15 +239,11 @@ def prepare_seq2seq_batch(
         self.tgt_lang = tgt_lang
         return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kwargs)
 
-    @contextmanager
-    def as_target_tokenizer(self):
-        """
-        Temporarily sets the tokenizer for encoding the targets. Useful for tokenizer associated to
-        sequence-to-sequence models that need a slightly different processing for the labels.
-        """
-        self.set_tgt_lang_special_tokens(self.tgt_lang)
-        yield
-        self.set_src_lang_special_tokens(self.src_lang)
+    def _switch_to_input_mode(self):
+        return self.set_src_lang_special_tokens(self.src_lang)
+
+    def _switch_to_target_mode(self):
+        return self.set_tgt_lang_special_tokens(self.tgt_lang)
 
     def set_src_lang_special_tokens(self, src_lang) -> None:
         """Reset the special tokens to the source lang setting. No prefix and suffix=[eos, src_lang_code]."""

src/transformers/models/mbart50/tokenization_mbart50.py

@@ -14,7 +14,6 @@
 # limitations under the License.
 
 import os
-from contextlib import contextmanager
 from shutil import copyfile
 from typing import Any, Dict, List, Optional, Tuple
 
@@ -337,15 +336,11 @@ def prepare_seq2seq_batch(
         self.tgt_lang = tgt_lang
         return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kwargs)
 
-    @contextmanager
-    def as_target_tokenizer(self):
-        """
-        Temporarily sets the tokenizer for encoding the targets. Useful for tokenizer associated to
-        sequence-to-sequence models that need a slightly different processing for the labels.
-        """
-        self.set_tgt_lang_special_tokens(self.tgt_lang)
-        yield
-        self.set_src_lang_special_tokens(self.src_lang)
+    def _switch_to_input_mode(self):
+        return self.set_src_lang_special_tokens(self.src_lang)
+
+    def _switch_to_target_mode(self):
+        return self.set_tgt_lang_special_tokens(self.tgt_lang)
 
     def set_src_lang_special_tokens(self, src_lang: str) -> None:
         """Reset the special tokens to the source lang setting. prefix=[src_lang_code] and suffix=[eos]."""

src/transformers/models/mbart50/tokenization_mbart50_fast.py

@@ -14,7 +14,6 @@
 # limitations under the License.
 
 import os
-from contextlib import contextmanager
 from shutil import copyfile
 from typing import List, Optional, Tuple
 
@@ -211,15 +210,11 @@ def prepare_seq2seq_batch(
         self.tgt_lang = tgt_lang
         return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kwargs)
 
-    @contextmanager
-    def as_target_tokenizer(self):
-        """
-        Temporarily sets the tokenizer for encoding the targets. Useful for tokenizer associated to
-        sequence-to-sequence models that need a slightly different processing for the labels.
-        """
-        self.set_tgt_lang_special_tokens(self.tgt_lang)
-        yield
-        self.set_src_lang_special_tokens(self.src_lang)
+    def _switch_to_input_mode(self):
+        return self.set_src_lang_special_tokens(self.src_lang)
+
+    def _switch_to_target_mode(self):
+        return self.set_tgt_lang_special_tokens(self.tgt_lang)
 
     def set_src_lang_special_tokens(self, src_lang: str) -> None:
         """Reset the special tokens to the source lang setting. prefix=[src_lang_code] and suffix=[eos]."""

src/transformers/models/mctct/processing_mctct.py

@@ -15,6 +15,7 @@
 """
 Speech processor class for M-CTC-T
 """
+import warnings
 from contextlib import contextmanager
 
 from ...processing_utils import ProcessorMixin
@@ -39,6 +40,7 @@ class MCTCTProcessor(ProcessorMixin):
     def __init__(self, feature_extractor, tokenizer):
         super().__init__(feature_extractor, tokenizer)
         self.current_processor = self.feature_extractor
+        self._in_target_context_manager = False
 
     def __call__(self, *args, **kwargs):
         """
@@ -47,7 +49,35 @@ def __call__(self, *args, **kwargs):
         [`~MCTCTProcessor.as_target_processor`] this method forwards all its arguments to AutoTokenizer's
         [`~AutoTokenizer.__call__`]. Please refer to the doctsring of the above two methods for more information.
         """
-        return self.current_processor(*args, **kwargs)
+        # For backward compatibility
+        if self._in_target_context_manager:
+            return self.current_processor(*args, **kwargs)
+
+        if "raw_speech" in kwargs:
+            warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.")
+            audio = kwargs.pop("raw_speech")
+        else:
+            audio = kwargs.pop("audio", None)
+        text = kwargs.pop("text", None)
+        if len(args) > 0:
+            audio = args[0]
+            args = args[1:]
+
+        if audio is None and text is None:
+            raise ValueError("You need to specify either an `audio` or `text` input to process.")
+
+        if audio is not None:
+            inputs = self.feature_extractor(audio, *args, **kwargs)
+        if text is not None:
+            encodings = self.tokenizer(text, **kwargs)
+
+        if text is None:
+            return inputs
+        elif audio is None:
+            return encodings
+        else:
+            inputs["labels"] = encodings["input_ids"]
+            return inputs
 
     def batch_decode(self, *args, **kwargs):
         """
@@ -63,7 +93,28 @@ def pad(self, *args, **kwargs):
         [`~MCTCTProcessor.as_target_processor`] this method forwards all its arguments to PreTrainedTokenizer's
         [`~PreTrainedTokenizer.pad`]. Please refer to the docstring of the above two methods for more information.
         """
-        return self.current_processor.pad(*args, **kwargs)
+        # For backward compatibility
+        if self._in_target_context_manager:
+            return self.current_processor.pad(*args, **kwargs)
+
+        input_features = kwargs.pop("input_features", None)
+        labels = kwargs.pop("labels", None)
+        if len(args) > 0:
+            input_features = args[0]
+            args = args[1:]
+
+        if input_features is not None:
+            input_features = self.feature_extractor.pad(input_features, *args, **kwargs)
+        if labels is not None:
+            labels = self.tokenizer.pad(labels, **kwargs)
+
+        if labels is None:
+            return input_features
+        elif input_features is None:
+            return labels
+        else:
+            input_features["labels"] = labels["input_ids"]
+            return input_features
 
     def decode(self, *args, **kwargs):
         """
@@ -77,6 +128,13 @@ def as_target_processor(self):
         """
         Temporarily sets the tokenizer for processing the input. Useful for encoding the labels when fine-tuning MCTCT.
         """
+        warnings.warn(
+            "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
+            "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
+            "your audio inputs, or in a separate call."
+        )
+        self._in_target_context_manager = True
         self.current_processor = self.tokenizer
         yield
         self.current_processor = self.feature_extractor
+        self._in_target_context_manager = False

src/transformers/models/nllb/tokenization_nllb.py

@@ -14,7 +14,6 @@
 # limitations under the License.
 
 import os
-from contextlib import contextmanager
 from shutil import copyfile
 from typing import Any, Dict, List, Optional, Tuple
 
@@ -386,15 +385,11 @@ def prepare_seq2seq_batch(
         self.tgt_lang = tgt_lang
         return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kwargs)
 
-    @contextmanager
-    def as_target_tokenizer(self):
-        """
-        Temporarily sets the tokenizer for encoding the targets. Useful for tokenizer associated to
-        sequence-to-sequence models that need a slightly different processing for the labels.
-        """
-        self.set_tgt_lang_special_tokens(self.tgt_lang)
-        yield
-        self.set_src_lang_special_tokens(self.src_lang)
+    def _switch_to_input_mode(self):
+        return self.set_src_lang_special_tokens(self.src_lang)
+
+    def _switch_to_target_mode(self):
+        return self.set_tgt_lang_special_tokens(self.tgt_lang)
 
     def set_src_lang_special_tokens(self, src_lang) -> None:
         """Reset the special tokens to the source lang setting. No prefix and suffix=[eos, src_lang_code]."""

src/transformers/models/nllb/tokenization_nllb_fast.py

@@ -14,7 +14,6 @@
 # limitations under the License.
 
 import os
-from contextlib import contextmanager
 from shutil import copyfile
 from typing import List, Optional, Tuple
 
@@ -284,15 +283,11 @@ def prepare_seq2seq_batch(
         self.tgt_lang = tgt_lang
         return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kwargs)
 
-    @contextmanager
-    def as_target_tokenizer(self):
-        """
-        Temporarily sets the tokenizer for encoding the targets. Useful for tokenizer associated to
-        sequence-to-sequence models that need a slightly different processing for the labels.
-        """
-        self.set_tgt_lang_special_tokens(self.tgt_lang)
-        yield
-        self.set_src_lang_special_tokens(self.src_lang)
+    def _switch_to_input_mode(self):
+        return self.set_src_lang_special_tokens(self.src_lang)
+
+    def _switch_to_target_mode(self):
+        return self.set_tgt_lang_special_tokens(self.tgt_lang)
 
     def set_src_lang_special_tokens(self, src_lang) -> None:
         """Reset the special tokens to the source lang setting. No prefix and suffix=[eos, src_lang_code]."""

src/transformers/models/plbart/tokenization_plbart.py

@@ -14,7 +14,6 @@
 # limitations under the License.
 
 import os
-from contextlib import contextmanager
 from shutil import copyfile
 from typing import Any, Dict, List, Optional, Tuple
 
@@ -441,15 +440,11 @@ def prepare_seq2seq_batch(
         self.tgt_lang = tgt_lang
         return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kwargs)
 
-    @contextmanager
-    def as_target_tokenizer(self):
-        """
-        Temporarily sets the tokenizer for encoding the targets. Useful for tokenizer associated to
-        sequence-to-sequence models that need a slightly different processing for the labels.
-        """
-        self.set_tgt_lang_special_tokens(self.tgt_lang)
-        yield
-        self.set_src_lang_special_tokens(self.src_lang)
+    def _switch_to_input_mode(self):
+        return self.set_src_lang_special_tokens(self.src_lang)
+
+    def _switch_to_target_mode(self):
+        return self.set_tgt_lang_special_tokens(self.tgt_lang)
 
     def set_src_lang_special_tokens(self, src_lang) -> None:
         """Reset the special tokens to the source lang setting. No prefix and suffix=[eos, src_lang_code]."""

src/transformers/models/rag/tokenization_rag.py

@@ -15,7 +15,6 @@
 """Tokenization classes for RAG."""
 import os
 import warnings
-from contextlib import contextmanager
 from typing import List, Optional
 
 from ...tokenization_utils_base import BatchEncoding
@@ -68,16 +67,12 @@ def batch_decode(self, *args, **kwargs):
     def decode(self, *args, **kwargs):
         return self.generator.decode(*args, **kwargs)
 
-    @contextmanager
-    def as_target_tokenizer(self):
-        """
-        Temporarily sets the tokenizer for encoding the targets. Useful for tokenizer associated to
-        sequence-to-sequence models that need a slightly different processing for the labels.
-        """
-        self.current_tokenizer = self.generator
-        yield
+    def _switch_to_input_mode(self):
         self.current_tokenizer = self.question_encoder
 
+    def _switch_to_target_mode(self):
+        self.current_tokenizer = self.generator
+
     def prepare_seq2seq_batch(
         self,
         src_texts: List[str],