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