Final update of doctest (#22299)

* update

* update

---------
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

src/transformers/models/auto/feature_extraction_auto.py

@@ -303,7 +303,7 @@ class AutoFeatureExtractor:
         >>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h")
 
         >>> # If feature extractor files are in a directory (e.g. feature extractor was saved using *save_pretrained('./test/saved_model/')*)
-        >>> feature_extractor = AutoFeatureExtractor.from_pretrained("./test/saved_model/")
+        >>> # feature_extractor = AutoFeatureExtractor.from_pretrained("./test/saved_model/")
         ```"""
         config = kwargs.pop("config", None)
         trust_remote_code = kwargs.pop("trust_remote_code", False)

src/transformers/models/auto/image_processing_auto.py

@@ -306,7 +306,7 @@ class AutoImageProcessor:
         >>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k")
 
         >>> # If image processor files are in a directory (e.g. image processor was saved using *save_pretrained('./test/saved_model/')*)
-        >>> image_processor = AutoImageProcessor.from_pretrained("./test/saved_model/")
+        >>> # image_processor = AutoImageProcessor.from_pretrained("./test/saved_model/")
         ```"""
         config = kwargs.pop("config", None)
         trust_remote_code = kwargs.pop("trust_remote_code", False)

src/transformers/models/auto/processing_auto.py

@@ -188,7 +188,7 @@ class AutoProcessor:
         >>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h")
 
         >>> # If processor files are in a directory (e.g. processor was saved using *save_pretrained('./test/saved_model/')*)
-        >>> processor = AutoProcessor.from_pretrained("./test/saved_model/")
+        >>> # processor = AutoProcessor.from_pretrained("./test/saved_model/")
         ```"""
         config = kwargs.pop("config", None)
         trust_remote_code = kwargs.pop("trust_remote_code", False)

src/transformers/models/auto/tokenization_auto.py

@@ -575,7 +575,7 @@ class AutoTokenizer:
         >>> tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-german-cased")
 
         >>> # If vocabulary files are in a directory (e.g. tokenizer was saved using *save_pretrained('./test/saved_model/')*)
-        >>> tokenizer = AutoTokenizer.from_pretrained("./test/bert_saved_model/")
+        >>> # tokenizer = AutoTokenizer.from_pretrained("./test/bert_saved_model/")
 
         >>> # Download vocabulary from huggingface.co and define model-specific arguments
         >>> tokenizer = AutoTokenizer.from_pretrained("roberta-base", add_prefix_space=True)

src/transformers/models/bertweet/tokenization_bertweet.py

@@ -640,9 +640,17 @@ def _replace_html_entities(text, keep=(), remove_illegal=True, encoding="utf-8")
 
     See https://github.com/scrapy/w3lib/blob/master/w3lib/html.py
 
-        >>> from nltk.tokenize.casual import _replace_html_entities >>> _replace_html_entities(b'Price: £100')
-        'Price: \\xa3100' >>> print(_replace_html_entities(b'Price: £100')) Price: £100 >>>
-    """
+    Examples:
+
+    ```python
+    >>> from nltk.tokenize.casual import _replace_html_entities
+
+    >>> _replace_html_entities(b"Price: £100")
+    'Price: \\xa3100'
+
+    >>> print(_replace_html_entities(b"Price: £100"))
+    Price: £100
+    ```"""
 
     def _convert_entity(match):
         entity_body = match.group(3)

src/transformers/models/dpr/tokenization_dpr.py

@@ -316,6 +316,7 @@ class CustomDPRReaderTokenizerMixin:
         >>> outputs = model(**encoded_inputs)
         >>> predicted_spans = tokenizer.decode_best_spans(encoded_inputs, outputs)
         >>> print(predicted_spans[0].text)  # best span
+        a song
         ```"""
         input_ids = reader_input["input_ids"]
         start_logits, end_logits, relevance_logits = reader_output[:3]

src/transformers/models/dpr/tokenization_dpr_fast.py

@@ -316,6 +316,7 @@ class CustomDPRReaderTokenizerMixin:
         >>> outputs = model(**encoded_inputs)
         >>> predicted_spans = tokenizer.decode_best_spans(encoded_inputs, outputs)
         >>> print(predicted_spans[0].text)  # best span
+        a song
         ```"""
         input_ids = reader_input["input_ids"]
         start_logits, end_logits, relevance_logits = reader_output[:3]

src/transformers/models/gptsan_japanese/tokenization_gptsan_japanese.py

@@ -96,7 +96,7 @@ class GPTSanJapaneseTokenizer(PreTrainedTokenizer):
     >>> tokenizer = GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese")
     >>> # You can confirm both 慶応 and 慶應 are encoded to 17750
     >>> tokenizer("吾輩は猫である🐯。実は慶応(慶應)大学出身")["input_ids"]
-    [34347, 31459, 30647, 31448, 25, 30659, 35729, 35676, 32417, 30647, 17750, 35589, 17750, 35590, 321, 1281]
+    [35993, 35998, 34347, 31459, 30647, 31448, 25, 30659, 35729, 35676, 32417, 30647, 17750, 35589, 17750, 35590, 321, 1281]
 
     >>> # Both 慶応 and 慶應 are decoded to 慶応
     >>> tokenizer.decode(tokenizer("吾輩は猫である🐯。実は慶応(慶應)大学出身")["input_ids"])
@@ -311,6 +311,9 @@ class GPTSanJapaneseTokenizer(PreTrainedTokenizer):
 
         Example:
         ```python
+        >>> from transformers import GPTSanJapaneseTokenizer
+
+        >>> tokenizer = GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese")
         >>> x_token = tokenizer("ｱｲｳｴ")
         >>> # input_ids:      | SOT | SEG | ｱ | ｲ | ｳ | ｴ |
         >>> # token_type_ids: | 1   | 0   | 0 | 0 | 0 | 0 |

src/transformers/models/m2m_100/tokenization_m2m_100.py

@@ -110,13 +110,14 @@ class M2M100Tokenizer(PreTrainedTokenizer):
     Examples:
 
     ```python
-    >>> from transformers import M2M100Tokenizer
+    >>> from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
 
+    >>> model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M")
     >>> tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M", src_lang="en", tgt_lang="ro")
     >>> 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, text_target=tgt_text, return_tensors="pt")
-    >>> model(**model_inputs)  # should work
+    >>> outputs = model(**model_inputs)  # should work
     ```"""
 
     vocab_files_names = VOCAB_FILES_NAMES

src/transformers/models/marian/tokenization_marian.py

@@ -106,13 +106,13 @@ class MarianTokenizer(PreTrainedTokenizer):
     Examples:
 
     ```python
-    >>> from transformers import MarianTokenizer
+    >>> from transformers import MarianForCausalLM, MarianTokenizer
 
+    >>> model = MarianForCausalLM.from_pretrained("Helsinki-NLP/opus-mt-en-de")
     >>> tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
     >>> src_texts = ["I am a small frog.", "Tom asked his teacher for advice."]
     >>> tgt_texts = ["Ich bin ein kleiner Frosch.", "Tom bat seinen Lehrer um Rat."]  # optional
     >>> inputs = tokenizer(src_texts, text_target=tgt_texts, return_tensors="pt", padding=True)
-    # keys  [input_ids, attention_mask, labels].
 
     >>> outputs = model(**inputs)  # should work
     ```"""

src/transformers/models/roformer/tokenization_roformer.py

@@ -344,7 +344,7 @@ class RoFormerTokenizer(PreTrainedTokenizer):
 
     >>> tokenizer = RoFormerTokenizer.from_pretrained("junnyu/roformer_chinese_base")
     >>> tokenizer.tokenize("今天天气非常好。")
-    # ['今', '天', '天', '气', '非常', '好', '。']
+    ['今', '天', '天', '气', '非常', '好', '。']
     ```"""
     vocab_files_names = VOCAB_FILES_NAMES
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP

src/transformers/models/roformer/tokenization_roformer_fast.py

@@ -85,7 +85,7 @@ class RoFormerTokenizerFast(PreTrainedTokenizerFast):
 
     >>> tokenizer = RoFormerTokenizerFast.from_pretrained("junnyu/roformer_chinese_base")
     >>> tokenizer.tokenize("今天天气非常好。")
-    # ['今', '天', '天', '气', '非常', '好', '。']
+    ['今', '天', '天', '气', '非常', '好', '。']
     ```"""
 
     vocab_files_names = VOCAB_FILES_NAMES

src/transformers/models/transfo_xl/tokenization_transfo_xl.py

@@ -88,7 +88,7 @@ def tokenize_numbers(text_array: List[str]) -> List[str]:
 
     ```python
     >>> tokenize_numbers(["$", "5,000", "1.73", "m"])
-    ["$", "5", "@,@", "000", "1", "@.@", "73", "m"]
+    ['$', '5', '@,@', '000', '1', '@.@', '73', 'm']
     ```"""
     tokenized = []
     for i in range(len(text_array)):
@@ -113,7 +113,7 @@ def detokenize_numbers(text: str) -> str:
 
     ```python
     >>> detokenize_numbers("$ 5 @,@ 000 1 @.@ 73 m")
-    "$ 5,000 1.73 m"
+    '$ 5,000 1.73 m'
     ```"""
     for reg, sub in DETOKENIZE_NUMBERS:
         text = re.sub(reg, sub, text)

utils/documentation_tests.txt

@@ -467,3 +467,16 @@ src/transformers/models/mvp/tokenization_mvp.py
 src/transformers/models/mvp/tokenization_mvp_fast.py
 src/transformers/models/roberta/tokenization_roberta.py
 src/transformers/models/roberta/tokenization_roberta_fast.py
+src/transformers/models/auto/feature_extraction_auto.py
+src/transformers/models/auto/image_processing_auto.py
+src/transformers/models/auto/processing_auto.py
+src/transformers/models/auto/tokenization_auto.py
+src/transformers/models/bertweet/tokenization_bertweet.py
+src/transformers/models/dpr/tokenization_dpr.py
+src/transformers/models/dpr/tokenization_dpr_fast.py
+src/transformers/models/gptsan_japanese/tokenization_gptsan_japanese.py
+src/transformers/models/m2m_100/tokenization_m2m_100.py
+src/transformers/models/marian/tokenization_marian.py
+src/transformers/models/roformer/tokenization_roformer.py
+src/transformers/models/roformer/tokenization_roformer_fast.py
+src/transformers/models/transfo_xl/tokenization_transfo_xl.py
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