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>

docs/source/en/model_doc/m2m_100.mdx

@@ -55,9 +55,7 @@ tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M", src_lang="en
 src_text = "Life is like a box of chocolates."
 tgt_text = "La vie est comme une boîte de chocolat."
 
-model_inputs = tokenizer(src_text, return_tensors="pt")
-with tokenizer.as_target_tokenizer():
-    labels = tokenizer(tgt_text, return_tensors="pt").input_ids
+model_inputs = tokenizer(src_text, text_target=tgt_text, return_tensors="pt")
 
 loss = model(**model_inputs, labels=labels)  # forward pass
 ```

docs/source/en/model_doc/marian.mdx

@@ -155,7 +155,7 @@ Example of translating english to many romance languages, using old-style 2 char
 ## MarianTokenizer
 
 [[autodoc]] MarianTokenizer
-    - as_target_tokenizer
+    - build_inputs_with_special_tokens
 
 ## MarianModel
 

docs/source/en/model_doc/mbart.mdx

@@ -34,8 +34,8 @@ model is multilingual it expects the sequences in a different format. A special
 source and target text. The source text format is `X [eos, src_lang_code]` where `X` is the source text. The
 target text format is `[tgt_lang_code] X [eos]`. `bos` is never used.
 
-The regular [`~MBartTokenizer.__call__`] will encode source text format, and it should be wrapped
-inside the context manager [`~MBartTokenizer.as_target_tokenizer`] to encode target text format.
+The regular [`~MBartTokenizer.__call__`] will encode source text format passed as first argument or with the `text`
+keyword, and target text format passed with the `text_label` keyword argument.
 
 - Supervised training
 
@@ -46,13 +46,11 @@ inside the context manager [`~MBartTokenizer.as_target_tokenizer`] to encode tar
 >>> example_english_phrase = "UN Chief Says There Is No Military Solution in Syria"
 >>> expected_translation_romanian = "Şeful ONU declară că nu există o soluţie militară în Siria"
 
->>> inputs = tokenizer(example_english_phrase, return_tensors="pt")
->>> with tokenizer.as_target_tokenizer():
-...     labels = tokenizer(expected_translation_romanian, return_tensors="pt")
+>>> inputs = tokenizer(example_english_phrase, text_target=expected_translation_romanian, return_tensors="pt")
 
 >>> model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-en-ro")
 >>> # forward pass
->>> model(**inputs, labels=batch["labels"])
+>>> model(**inputs)
 ```
 
 - Generation
@@ -108,11 +106,9 @@ tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50", src_
 src_text = " UN Chief Says There Is No Military Solution in Syria"
 tgt_text = "Şeful ONU declară că nu există o soluţie militară în Siria"
 
-model_inputs = tokenizer(src_text, return_tensors="pt")
-with tokenizer.as_target_tokenizer():
-    labels = tokenizer(tgt_text, return_tensors="pt").input_ids
+model_inputs = tokenizer(src_text, text_target=tgt_text, return_tensors="pt")
 
-model(**model_inputs, labels=labels)  # forward pass
+model(**model_inputs)  # forward pass
 ```
 
 - Generation
@@ -154,7 +150,6 @@ tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
 ## MBartTokenizer
 
 [[autodoc]] MBartTokenizer
-    - as_target_tokenizer
     - build_inputs_with_special_tokens
 
 ## MBartTokenizerFast

docs/source/en/model_doc/mctct.mdx

@@ -48,7 +48,6 @@ This model was contributed by [cwkeam](https://huggingface.co/cwkeam). The origi
     - save_pretrained
     - batch_decode
     - decode
-    - as_target_processor
 
 
 ## MCTCTModel

docs/source/en/model_doc/nllb.mdx

@@ -91,7 +91,6 @@ UN-Chef sagt, es gibt keine militärische Lösung in Syrien
 ## NllbTokenizer
 
 [[autodoc]] NllbTokenizer
-    - as_target_tokenizer
     - build_inputs_with_special_tokens
 
 ## NllbTokenizerFast

docs/source/en/model_doc/plbart.mdx

@@ -45,8 +45,9 @@ target text format is `[tgt_lang_code] X [eos]`. `bos` is never used.
 
 However, for fine-tuning, in some cases no language token is provided in cases where a single language is used. Please refer to [the paper](https://arxiv.org/abs/2103.06333) to learn more about this.
 
-In cases where the language code is needed, The regular [`~PLBartTokenizer.__call__`] will encode source text format, and it should be wrapped
-inside the context manager [`~PLBartTokenizer.as_target_tokenizer`] to encode target text format.
+In cases where the language code is needed, the regular [`~PLBartTokenizer.__call__`] will encode source text format 
+when you pass texts as the first argument or with the keyword argument `text`, and will encode target text format if
+it's passed with the `text_target` keyword argument.
 
 - Supervised training
 
@@ -56,11 +57,7 @@ inside the context manager [`~PLBartTokenizer.as_target_tokenizer`] to encode ta
 >>> tokenizer = PLBartTokenizer.from_pretrained("uclanlp/plbart-base", src_lang="en_XX", tgt_lang="python")
 >>> example_python_phrase = "def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])"
 >>> expected_translation_english = "Returns the maximum value of a b c."
->>> inputs = tokenizer(example_python_phrase, return_tensors="pt")
->>> with tokenizer.as_target_tokenizer():
-...     labels = tokenizer(expected_translation_english, return_tensors="pt")
->>> inputs["labels"] = labels["input_ids"]
->>> # forward pass
+>>> inputs = tokenizer(example_python_phrase, text_target=expected_translation_english, return_tensors="pt")
 >>> model(**inputs)
 ```
 
@@ -88,7 +85,6 @@ inside the context manager [`~PLBartTokenizer.as_target_tokenizer`] to encode ta
 ## PLBartTokenizer
 
 [[autodoc]] PLBartTokenizer
-    - as_target_tokenizer
     - build_inputs_with_special_tokens
 
 ## PLBartModel

docs/source/en/model_doc/speech-encoder-decoder.mdx

@@ -107,7 +107,7 @@ speech inputs) and `labels` (which are the `input_ids` of the encoded target seq
 >>> labels = tokenizer(ds[0]["text"], return_tensors="pt").input_ids
 
 >>> # the forward function automatically creates the correct decoder_input_ids
->>> loss = model(input_values, labels=labels).loss
+>>> loss = model(**input_features).loss
 >>> loss.backward()
 ```
 

docs/source/en/model_doc/speech_to_text.mdx

@@ -120,7 +120,6 @@ See the [model hub](https://huggingface.co/models?filter=speech_to_text) to look
     - save_pretrained
     - batch_decode
     - decode
-    - as_target_processor
 
 ## Speech2TextModel
 

docs/source/en/model_doc/speech_to_text_2.mdx

@@ -114,7 +114,6 @@ See [model hub](https://huggingface.co/models?filter=speech2text2) to look for S
     - save_pretrained
     - batch_decode
     - decode
-    - as_target_processor
 
 ## Speech2Text2ForCausalLM
 

docs/source/en/model_doc/trocr.mdx

@@ -94,7 +94,6 @@ See the [model hub](https://huggingface.co/models?filter=trocr) to look for TrOC
     - save_pretrained
     - batch_decode
     - decode
-    - as_target_processor
 
 ## TrOCRForCausalLM
 

docs/source/en/model_doc/wav2vec2.mdx

@@ -62,7 +62,6 @@ This model was contributed by [patrickvonplaten](https://huggingface.co/patrickv
     - save_pretrained
     - batch_decode
     - decode
-    - as_target_processor
 
 ## Wav2Vec2ProcessorWithLM
 
@@ -73,7 +72,6 @@ This model was contributed by [patrickvonplaten](https://huggingface.co/patrickv
     - save_pretrained
     - batch_decode
     - decode
-    - as_target_processor
 
 ## Wav2Vec2 specific outputs
 

docs/source/en/preprocessing.mdx

@@ -486,10 +486,8 @@ A processor combines a feature extractor and tokenizer. Load a processor with [`
 >>> def prepare_dataset(example):
 ...     audio = example["audio"]
 
-...     example["input_values"] = processor(audio["array"], sampling_rate=16000)
+...     example.update(processor(audio=audio["array"], text=example["text"], sampling_rate=16000))
 
-...     with processor.as_target_processor():
-...         example["labels"] = processor(example["text"]).input_ids
 ...     return example
 ```
 

docs/source/en/tasks/asr.mdx

@@ -109,11 +109,10 @@ The preprocessing function needs to:
 >>> def prepare_dataset(batch):
 ...     audio = batch["audio"]
 
-...     batch["input_values"] = processor(audio["array"], sampling_rate=audio["sampling_rate"]).input_values[0]
+...     batch = processor(audio=audio["array"], sampling_rate=audio["sampling_rate"]).input_values[0]
 ...     batch["input_length"] = len(batch["input_values"])
 
-...     with processor.as_target_processor():
-...         batch["labels"] = processor(batch["transcription"]).input_ids
+...     batch["labels"] = processor(text=batch["transcription"]).input_ids
 ...     return batch
 ```
 
@@ -146,17 +145,9 @@ Unlike other data collators, this specific data collator needs to apply a differ
 ...         input_features = [{"input_values": feature["input_values"]} for feature in features]
 ...         label_features = [{"input_ids": feature["labels"]} for feature in features]
 
-...         batch = self.processor.pad(
-...             input_features,
-...             padding=self.padding,
-...             return_tensors="pt",
-...         )
-...         with self.processor.as_target_processor():
-...             labels_batch = self.processor.pad(
-...                 label_features,
-...                 padding=self.padding,
-...                 return_tensors="pt",
-...             )
+...         batch = self.processor.pad(input_features, padding=self.padding, return_tensors="pt")
+
+...         labels_batch = self.processor.pad(labels=label_features, padding=self.padding, return_tensors="pt")
 
 ...         # replace padding with -100 to ignore loss correctly
 ...         labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)

docs/source/en/tasks/summarization.mdx

@@ -67,7 +67,7 @@ Load the T5 tokenizer to process `text` and `summary`:
 The preprocessing function needs to:
 
 1. Prefix the input with a prompt so T5 knows this is a summarization task. Some models capable of multiple NLP tasks require prompting for specific tasks.
-2. Use a context manager with the `as_target_tokenizer()` function to parallelize tokenization of inputs and labels.
+2. Use the keyword `text_target` argument when tokenizing labels.
 3. Truncate sequences to be no longer than the maximum length set by the `max_length` parameter.
 
 ```py
@@ -78,8 +78,7 @@ The preprocessing function needs to:
 ...     inputs = [prefix + doc for doc in examples["text"]]
 ...     model_inputs = tokenizer(inputs, max_length=1024, truncation=True)
 
-...     with tokenizer.as_target_tokenizer():
-...         labels = tokenizer(examples["summary"], max_length=128, truncation=True)
+...     labels = tokenizer(text_target=examples["summary"], max_length=128, truncation=True)
 
 ...     model_inputs["labels"] = labels["input_ids"]
 ...     return model_inputs

docs/source/en/tasks/translation.mdx

@@ -78,12 +78,7 @@ The preprocessing function needs to:
 >>> def preprocess_function(examples):
 ...     inputs = [prefix + example[source_lang] for example in examples["translation"]]
 ...     targets = [example[target_lang] for example in examples["translation"]]
-...     model_inputs = tokenizer(inputs, max_length=128, truncation=True)
-
-...     with tokenizer.as_target_tokenizer():
-...         labels = tokenizer(targets, max_length=128, truncation=True)
-
-...     model_inputs["labels"] = labels["input_ids"]
+...     model_inputs = tokenizer(inputs, text_target=targets, max_length=128, truncation=True)
 ...     return model_inputs
 ```
 

docs/source/es/preprocessing.mdx

@@ -471,10 +471,8 @@ Un processor combina un extractor de características y un tokenizador. Cargue u
 >>> def prepare_dataset(example):
 ...     audio = example["audio"]
 
-...     example["input_values"] = processor(audio["array"], sampling_rate=16000)
+...     example.update(processor(audio=audio["array"], text=example["text"], sampling_rate=16000))
 
-...     with processor.as_target_processor():
-...         example["labels"] = processor(example["text"]).input_ids
 ...     return example
 ```
 

docs/source/it/preprocessing.mdx

@@ -471,10 +471,8 @@ Un processor combina un estrattore di caratteristiche e un tokenizer. Carica un
 >>> def prepare_dataset(example):
 ...     audio = example["audio"]
 
-...     example["input_values"] = processor(audio["array"], sampling_rate=16000)
+...     example.update(processor(audio=audio["array"], text=example["text"], sampling_rate=16000))
 
-...     with processor.as_target_processor():
-...         example["labels"] = processor(example["text"]).input_ids
 ...     return example
 ```
 

examples/flax/image-captioning/run_image_captioning_flax.py

@@ -552,10 +552,13 @@ def main():
         targets = captions
 
         model_inputs = {}
-        # Setup the tokenizer for targets
-        with tokenizer.as_target_tokenizer():
+
         labels = tokenizer(
-                targets, max_length=max_target_length, padding="max_length", truncation=True, return_tensors="np"
+            text_target=targets,
+            max_length=max_target_length,
+            padding="max_length",
+            truncation=True,
+            return_tensors="np",
         )
         model_inputs["labels"] = labels["input_ids"]
         decoder_input_ids = shift_tokens_right_fn(

examples/flax/summarization/run_summarization_flax.py

@@ -590,9 +590,12 @@ def main():
         )
 
         # Setup the tokenizer for targets
-        with tokenizer.as_target_tokenizer():
         labels = tokenizer(
-                targets, max_length=max_target_length, padding="max_length", truncation=True, return_tensors="np"
+            text_target=targets,
+            max_length=max_target_length,
+            padding="max_length",
+            truncation=True,
+            return_tensors="np",
         )
 
         model_inputs["labels"] = labels["input_ids"]

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.