Doc styler examples (#14953)

* Fix bad examples

* Add black formatting to style_doc

* Use first nonempty line

* Put it at the right place

* Don't add spaces to empty lines

* Better templates

* Deal with triple quotes in docstrings

* Result of style_doc

* Enable mdx treatment and fix code examples in MDXs

* Result of doc styler on doc source files

* Last fixes

* Break copy from

src/transformers/models/bert/modeling_bert.py

@@ -1082,8 +1082,8 @@ class BertForPreTraining(BertPreTrainedModel):
         >>> from transformers import BertTokenizer, BertForPreTraining
         >>> import torch
 
-        >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-        >>> model = BertForPreTraining.from_pretrained('bert-base-uncased')
+        >>> tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+        >>> model = BertForPreTraining.from_pretrained("bert-base-uncased")
 
         >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
         >>> outputs = model(**inputs)
@@ -1208,10 +1208,10 @@ class BertLMHeadModel(BertPreTrainedModel):
         >>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig
         >>> import torch
 
-        >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
+        >>> tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
         >>> config = BertConfig.from_pretrained("bert-base-cased")
         >>> config.is_decoder = True
-        >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config)
+        >>> model = BertLMHeadModel.from_pretrained("bert-base-cased", config=config)
 
         >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
         >>> outputs = model(**inputs)
@@ -1436,12 +1436,12 @@ class BertForNextSentencePrediction(BertPreTrainedModel):
         >>> from transformers import BertTokenizer, BertForNextSentencePrediction
         >>> import torch
 
-        >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-        >>> model = BertForNextSentencePrediction.from_pretrained('bert-base-uncased')
+        >>> tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+        >>> model = BertForNextSentencePrediction.from_pretrained("bert-base-uncased")
 
         >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
         >>> next_sentence = "The sky is blue due to the shorter wavelength of blue light."
-        >>> encoding = tokenizer(prompt, next_sentence, return_tensors='pt')
+        >>> encoding = tokenizer(prompt, next_sentence, return_tensors="pt")
 
         >>> outputs = model(**encoding, labels=torch.LongTensor([1]))
         >>> logits = outputs.logits

src/transformers/models/bert/modeling_flax_bert.py

@@ -839,8 +839,8 @@ FLAX_BERT_FOR_PRETRAINING_DOCSTRING = """
     ```python
     >>> from transformers import BertTokenizer, FlaxBertForPreTraining
 
-    >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-    >>> model = FlaxBertForPreTraining.from_pretrained('bert-base-uncased')
+    >>> tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+    >>> model = FlaxBertForPreTraining.from_pretrained("bert-base-uncased")
 
     >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="np")
     >>> outputs = model(**inputs)
@@ -985,12 +985,12 @@ FLAX_BERT_FOR_NEXT_SENT_PRED_DOCSTRING = """
     ```python
     >>> from transformers import BertTokenizer, FlaxBertForNextSentencePrediction
 
-    >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-    >>> model = FlaxBertForNextSentencePrediction.from_pretrained('bert-base-uncased')
+    >>> tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+    >>> model = FlaxBertForNextSentencePrediction.from_pretrained("bert-base-uncased")
 
     >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
     >>> next_sentence = "The sky is blue due to the shorter wavelength of blue light."
-    >>> encoding = tokenizer(prompt, next_sentence, return_tensors='jax')
+    >>> encoding = tokenizer(prompt, next_sentence, return_tensors="jax")
 
     >>> outputs = model(**encoding)
     >>> logits = outputs.logits

src/transformers/models/bert/modeling_tf_bert.py

@@ -1233,9 +1233,11 @@ class TFBertForPreTraining(TFBertPreTrainedModel, TFBertPreTrainingLoss):
         >>> import tensorflow as tf
         >>> from transformers import BertTokenizer, TFBertForPreTraining
 
-        >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-        >>> model = TFBertForPreTraining.from_pretrained('bert-base-uncased')
-        >>> input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :]  # Batch size 1
+        >>> tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+        >>> model = TFBertForPreTraining.from_pretrained("bert-base-uncased")
+        >>> input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[
+        ...     None, :
+        >>> ]  # Batch size 1
         >>> outputs = model(input_ids)
         >>> prediction_scores, seq_relationship_scores = outputs[:2]
         ```"""
@@ -1609,14 +1611,14 @@ class TFBertForNextSentencePrediction(TFBertPreTrainedModel, TFNextSentencePredi
         >>> import tensorflow as tf
         >>> from transformers import BertTokenizer, TFBertForNextSentencePrediction
 
-        >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-        >>> model = TFBertForNextSentencePrediction.from_pretrained('bert-base-uncased')
+        >>> tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+        >>> model = TFBertForNextSentencePrediction.from_pretrained("bert-base-uncased")
 
         >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
         >>> next_sentence = "The sky is blue due to the shorter wavelength of blue light."
-        >>> encoding = tokenizer(prompt, next_sentence, return_tensors='tf')
+        >>> encoding = tokenizer(prompt, next_sentence, return_tensors="tf")
 
-        >>> logits = model(encoding['input_ids'], token_type_ids=encoding['token_type_ids'])[0]
+        >>> logits = model(encoding["input_ids"], token_type_ids=encoding["token_type_ids"])[0]
         >>> assert logits[0][0] < logits[0][1]  # the next sentence was random
         ```"""
         inputs = input_processing(

src/transformers/models/bert_generation/modeling_bert_generation.py

@@ -513,10 +513,12 @@ class BertGenerationDecoder(BertGenerationPreTrainedModel):
         >>> from transformers import BertGenerationTokenizer, BertGenerationDecoder, BertGenerationConfig
         >>> import torch
 
-        >>> tokenizer = BertGenerationTokenizer.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder')
+        >>> tokenizer = BertGenerationTokenizer.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder")
         >>> config = BertGenerationConfig.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder")
         >>> config.is_decoder = True
-        >>> model = BertGenerationDecoder.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder', config=config)
+        >>> model = BertGenerationDecoder.from_pretrained(
+        ...     "google/bert_for_seq_generation_L-24_bbc_encoder", config=config
+        ... )
 
         >>> inputs = tokenizer("Hello, my dog is cute", return_token_type_ids=False, return_tensors="pt")
         >>> outputs = model(**inputs)

src/transformers/models/bertweet/tokenization_bertweet.py

@@ -685,6 +685,7 @@ class TweetTokenizer:
     ```python
     >>> # Tokenizer for tweets.
     >>> from nltk.tokenize import TweetTokenizer
+
     >>> tknzr = TweetTokenizer()
     >>> s0 = "This is a cooool #dummysmiley: :-) :-P <3 and some arrows < > -> <--"
     >>> tknzr.tokenize(s0)
@@ -692,7 +693,7 @@ class TweetTokenizer:
 
     >>> # Examples using *strip_handles* and *reduce_len parameters*:
     >>> tknzr = TweetTokenizer(strip_handles=True, reduce_len=True)
-    >>> s1 = '@remy: This is waaaaayyyy too much for you!!!!!!'
+    >>> s1 = "@remy: This is waaaaayyyy too much for you!!!!!!"
     >>> tknzr.tokenize(s1)
     [':', 'This', 'is', 'waaayyy', 'too', 'much', 'for', 'you', '!', '!', '!']
     ```"""

src/transformers/models/big_bird/modeling_big_bird.py

@@ -2308,8 +2308,8 @@ class BigBirdForPreTraining(BigBirdPreTrainedModel):
         >>> from transformers import BigBirdTokenizer, BigBirdForPreTraining
         >>> import torch
 
-        >>> tokenizer = BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base')
-        >>> model = BigBirdForPreTraining.from_pretrained('google/bigbird-roberta-base')
+        >>> tokenizer = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")
+        >>> model = BigBirdForPreTraining.from_pretrained("google/bigbird-roberta-base")
 
         >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
         >>> outputs = model(**inputs)
@@ -2532,10 +2532,10 @@ class BigBirdForCausalLM(BigBirdPreTrainedModel):
         >>> from transformers import BigBirdTokenizer, BigBirdForCausalLM, BigBirdConfig
         >>> import torch
 
-        >>> tokenizer = BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base')
+        >>> tokenizer = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")
         >>> config = BigBirdConfig.from_pretrained("google/bigbird-roberta-base")
         >>> config.is_decoder = True
-        >>> model = BigBirdForCausalLM.from_pretrained('google/bigbird-roberta-base', config=config)
+        >>> model = BigBirdForCausalLM.from_pretrained("google/bigbird-roberta-base", config=config)
 
         >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
         >>> outputs = model(**inputs)

src/transformers/models/big_bird/modeling_flax_big_bird.py

@@ -1644,8 +1644,8 @@ FLAX_BIG_BIRD_FOR_PRETRAINING_DOCSTRING = """
     ```python
     >>> from transformers import BigBirdTokenizer, FlaxBigBirdForPreTraining
 
-    >>> tokenizer = BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base')
-    >>> model = FlaxBigBirdForPreTraining.from_pretrained('google/bigbird-roberta-base')
+    >>> tokenizer = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")
+    >>> model = FlaxBigBirdForPreTraining.from_pretrained("google/bigbird-roberta-base")
 
     >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="np")
     >>> outputs = model(**inputs)

src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py

@@ -2861,7 +2861,6 @@ class BigBirdPegasusDecoderWrapper(BigBirdPegasusPreTrainedModel):
         return self.decoder(*args, **kwargs)
 
 
-# Copied from transformers.models.bart.modeling_bart.BartForCausalLM with BartDecoderWrapper->BigBirdPegasusDecoderWrapper, BartForCausalLM->BigBirdPegasusForCausalLM, BartPreTrainedModel->BigBirdPegasusPreTrainedModel, BartTokenizer->PegasusTokenizer, 'facebook/bart-large'->"google/bigbird-pegasus-large-arxiv"
 class BigBirdPegasusForCausalLM(BigBirdPegasusPreTrainedModel):
     def __init__(self, config):
         config = copy.deepcopy(config)
@@ -2984,7 +2983,9 @@ class BigBirdPegasusForCausalLM(BigBirdPegasusPreTrainedModel):
         >>> from transformers import PegasusTokenizer, BigBirdPegasusForCausalLM
 
         >>> tokenizer = PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv")
-        >>> model = BigBirdPegasusForCausalLM.from_pretrained("google/bigbird-pegasus-large-arxiv", add_cross_attention=False)
+        >>> model = BigBirdPegasusForCausalLM.from_pretrained(
+        ...     "google/bigbird-pegasus-large-arxiv", add_cross_attention=False
+        ... )
         >>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
         >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
         >>> outputs = model(**inputs)

src/transformers/models/blenderbot/modeling_blenderbot.py

@@ -1130,7 +1130,9 @@ class BlenderbotModel(BlenderbotPreTrainedModel):
         >>> model = BlenderbotModel.from_pretrained("facebook/blenderbot-400M-distill")
         >>> tokenizer = BlenderbotTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
 
-        >>> input_ids = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="pt").input_ids  # Batch size 1
+        >>> input_ids = tokenizer(
+        ...     "Studies have been shown that owning a dog is good for you", return_tensors="pt"
+        >>> ).input_ids  # Batch size 1
         >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids  # Batch size 1
         >>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
 
@@ -1506,8 +1508,8 @@ class BlenderbotForCausalLM(BlenderbotPreTrainedModel):
         ```python
         >>> from transformers import BlenderbotTokenizer, BlenderbotForCausalLM
 
-        >>> tokenizer = BlenderbotTokenizer.from_pretrained('facebook/bart-large')
-        >>> model = BlenderbotForCausalLM.from_pretrained('facebook/bart-large', add_cross_attention=False)
+        >>> tokenizer = BlenderbotTokenizer.from_pretrained("facebook/bart-large")
+        >>> model = BlenderbotForCausalLM.from_pretrained("facebook/bart-large", add_cross_attention=False)
         >>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
         >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
         >>> outputs = model(**inputs)

src/transformers/models/blenderbot/modeling_flax_blenderbot.py

@@ -982,11 +982,11 @@ class FlaxBlenderbotPreTrainedModel(FlaxPreTrainedModel):
         ```python
         >>> from transformers import BlenderbotTokenizer, FlaxBlenderbotForConditionalGeneration
 
-        >>> model = FlaxBlenderbotForConditionalGeneration.from_pretrained('facebook/blenderbot-400M-distill')
-        >>> tokenizer = BlenderbotTokenizer.from_pretrained('facebook/blenderbot-400M-distill')
+        >>> model = FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-400M-distill")
+        >>> tokenizer = BlenderbotTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
 
         >>> text = "My friends are cool but they eat too many carbs."
-        >>> inputs = tokenizer(text, max_length=1024, return_tensors='jax')
+        >>> inputs = tokenizer(text, max_length=1024, return_tensors="jax")
         >>> encoder_outputs = model.encode(**inputs)
         ```"""
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
@@ -1050,11 +1050,11 @@ class FlaxBlenderbotPreTrainedModel(FlaxPreTrainedModel):
         ```python
         >>> from transformers import BlenderbotTokenizer, FlaxBlenderbotForConditionalGeneration
 
-        >>> model = FlaxBlenderbotForConditionalGeneration.from_pretrained('facebook/blenderbot-400M-distill')
-        >>> tokenizer = BlenderbotTokenizer.from_pretrained('facebook/blenderbot-400M-distill')
+        >>> model = FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-400M-distill")
+        >>> tokenizer = BlenderbotTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
 
         >>> text = "My friends are cool but they eat too many carbs."
-        >>> inputs = tokenizer(text, max_length=1024, return_tensors='jax')
+        >>> inputs = tokenizer(text, max_length=1024, return_tensors="jax")
         >>> encoder_outputs = model.encode(**inputs)
 
         >>> decoder_start_token_id = model.config.decoder_start_token_id
@@ -1319,11 +1319,11 @@ class FlaxBlenderbotForConditionalGeneration(FlaxBlenderbotPreTrainedModel):
         ```python
         >>> from transformers import BlenderbotTokenizer, FlaxBlenderbotForConditionalGeneration
 
-        >>> model = FlaxBlenderbotForConditionalGeneration.from_pretrained('facebook/blenderbot-400M-distill')
-        >>> tokenizer = BlenderbotTokenizer.from_pretrained('facebook/blenderbot-400M-distill')
+        >>> model = FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-400M-distill")
+        >>> tokenizer = BlenderbotTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
 
         >>> text = "My friends are cool but they eat too many carbs."
-        >>> inputs = tokenizer(text, max_length=1024, return_tensors='jax')
+        >>> inputs = tokenizer(text, max_length=1024, return_tensors="jax")
         >>> encoder_outputs = model.encode(**inputs)
 
         >>> decoder_start_token_id = model.config.decoder_start_token_id

src/transformers/models/blenderbot_small/modeling_blenderbot_small.py

@@ -1113,7 +1113,9 @@ class BlenderbotSmallModel(BlenderbotSmallPreTrainedModel):
         >>> model = BlenderbotSmallModel.from_pretrained("facebook/blenderbot_small-90M")
         >>> tokenizer = BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot_small-90M")
 
-        >>> input_ids = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="pt").input_ids  # Batch size 1
+        >>> input_ids = tokenizer(
+        ...     "Studies have been shown that owning a dog is good for you", return_tensors="pt"
+        >>> ).input_ids  # Batch size 1
         >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids  # Batch size 1
         >>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
 
@@ -1477,8 +1479,8 @@ class BlenderbotSmallForCausalLM(BlenderbotSmallPreTrainedModel):
         ```python
         >>> from transformers import BlenderbotSmallTokenizer, BlenderbotSmallForCausalLM
 
-        >>> tokenizer = BlenderbotSmallTokenizer.from_pretrained('facebook/bart-large')
-        >>> model = BlenderbotSmallForCausalLM.from_pretrained('facebook/bart-large', add_cross_attention=False)
+        >>> tokenizer = BlenderbotSmallTokenizer.from_pretrained("facebook/bart-large")
+        >>> model = BlenderbotSmallForCausalLM.from_pretrained("facebook/bart-large", add_cross_attention=False)
         >>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
         >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
         >>> outputs = model(**inputs)

src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py

@@ -994,11 +994,11 @@ class FlaxBlenderbotSmallPreTrainedModel(FlaxPreTrainedModel):
         ```python
         >>> from transformers import BlenderbotSmallTokenizer, FlaxBlenderbotSmallForConditionalGeneration
 
-        >>> model = FlaxBlenderbotSmallForConditionalGeneration.from_pretrained('facebook/blenderbot_small-90M')
-        >>> tokenizer = BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot_small-90M')
+        >>> model = FlaxBlenderbotSmallForConditionalGeneration.from_pretrained("facebook/blenderbot_small-90M")
+        >>> tokenizer = BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot_small-90M")
 
         >>> text = "My friends are cool but they eat too many carbs."
-        >>> inputs = tokenizer(text, max_length=1024, return_tensors='np')
+        >>> inputs = tokenizer(text, max_length=1024, return_tensors="np")
         >>> encoder_outputs = model.encode(**inputs)
         ```"""
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
@@ -1062,11 +1062,11 @@ class FlaxBlenderbotSmallPreTrainedModel(FlaxPreTrainedModel):
         ```python
         >>> from transformers import BlenderbotSmallTokenizer, FlaxBlenderbotSmallForConditionalGeneration
 
-        >>> model = FlaxBlenderbotSmallForConditionalGeneration.from_pretrained('facebook/blenderbot_small-90M')
-        >>> tokenizer = BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot_small-90M')
+        >>> model = FlaxBlenderbotSmallForConditionalGeneration.from_pretrained("facebook/blenderbot_small-90M")
+        >>> tokenizer = BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot_small-90M")
 
         >>> text = "My friends are cool but they eat too many carbs."
-        >>> inputs = tokenizer(text, max_length=1024, return_tensors='np')
+        >>> inputs = tokenizer(text, max_length=1024, return_tensors="np")
         >>> encoder_outputs = model.encode(**inputs)
 
         >>> decoder_start_token_id = model.config.decoder_start_token_id
@@ -1331,11 +1331,11 @@ class FlaxBlenderbotSmallForConditionalGeneration(FlaxBlenderbotSmallPreTrainedM
         ```python
         >>> from transformers import BlenderbotSmallTokenizer, FlaxBlenderbotSmallForConditionalGeneration
 
-        >>> model = FlaxBlenderbotSmallForConditionalGeneration.from_pretrained('facebook/blenderbot_small-90M')
-        >>> tokenizer = BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot_small-90M')
+        >>> model = FlaxBlenderbotSmallForConditionalGeneration.from_pretrained("facebook/blenderbot_small-90M")
+        >>> tokenizer = BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot_small-90M")
 
         >>> text = "My friends are cool but they eat too many carbs."
-        >>> inputs = tokenizer(text, max_length=1024, return_tensors='np')
+        >>> inputs = tokenizer(text, max_length=1024, return_tensors="np")
         >>> encoder_outputs = model.encode(**inputs)
 
         >>> decoder_start_token_id = model.config.decoder_start_token_id

src/transformers/models/clip/modeling_clip.py

@@ -978,7 +978,9 @@ class CLIPModel(CLIPPreTrainedModel):
         >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
         >>> image = Image.open(requests.get(url, stream=True).raw)
 
-        >>> inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True)
+        >>> inputs = processor(
+        ...     text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True
+        ... )
 
         >>> outputs = model(**inputs)
         >>> logits_per_image = outputs.logits_per_image  # this is the image-text similarity score

src/transformers/models/clip/modeling_flax_clip.py

@@ -1128,7 +1128,9 @@ FLAX_CLIP_MODEL_DOCSTRING = """
     >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
     >>> image = Image.open(requests.get(url, stream=True).raw)
 
-    >>> inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="np", padding=True)
+    >>> inputs = processor(
+    ...     text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="np", padding=True
+    ... )
 
     >>> outputs = model(**inputs)
     >>> logits_per_image = outputs.logits_per_image  # this is the image-text similarity score

src/transformers/models/clip/modeling_tf_clip.py

@@ -1469,7 +1469,9 @@ class TFCLIPModel(TFCLIPPreTrainedModel):
         >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
         >>> image = Image.open(requests.get(url, stream=True).raw)
 
-        >>> inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="tf", padding=True)
+        >>> inputs = processor(
+        ...     text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="tf", padding=True
+        ... )
 
         >>> outputs = model(**inputs)
         >>> logits_per_image = outputs.logits_per_image  # this is the image-text similarity score

src/transformers/models/convbert/configuration_convbert.py

@@ -78,6 +78,7 @@ class ConvBertConfig(PretrainedConfig):
 
     ```python
     >>> from transformers import ConvBertModel, ConvBertConfig
+
     >>> # Initializing a ConvBERT convbert-base-uncased style configuration
     >>> configuration = ConvBertConfig()
     >>> # Initializing a model from the convbert-base-uncased style configuration

src/transformers/models/deberta/modeling_deberta.py

@@ -90,10 +90,10 @@ class XSoftmax(torch.autograd.Function):
     >>> from transformers.models.deberta.modeling_deberta import XSoftmax
 
     >>> # Make a tensor
-    >>> x = torch.randn([4,20,100])
+    >>> x = torch.randn([4, 20, 100])
 
     >>> # Create a mask
-    >>> mask = (x>0).int()
+    >>> mask = (x > 0).int()
 
     >>> # Specify the dimension to apply softmax
     >>> dim = -1

src/transformers/models/deberta_v2/modeling_deberta_v2.py

@@ -91,10 +91,10 @@ class XSoftmax(torch.autograd.Function):
     >>> from transformers.models.deberta_v2.modeling_deberta_v2 import XSoftmax
 
     >>> # Make a tensor
-    >>> x = torch.randn([4,20,100])
+    >>> x = torch.randn([4, 20, 100])
 
     >>> # Create a mask
-    >>> mask = (x>0).int()
+    >>> mask = (x > 0).int()
 
     >>> # Specify the dimension to apply softmax
     >>> dim = -1

src/transformers/models/deit/modeling_deit.py

@@ -492,11 +492,11 @@ class DeiTModel(DeiTPreTrainedModel):
         >>> from PIL import Image
         >>> import requests
 
-        >>> url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
         >>> image = Image.open(requests.get(url, stream=True).raw)
 
-        >>> feature_extractor = DeiTFeatureExtractor.from_pretrained('facebook/deit-base-distilled-patch16-224')
-        >>> model = DeiTModel.from_pretrained('facebook/deit-base-distilled-patch16-224', add_pooling_layer=False)
+        >>> feature_extractor = DeiTFeatureExtractor.from_pretrained("facebook/deit-base-distilled-patch16-224")
+        >>> model = DeiTModel.from_pretrained("facebook/deit-base-distilled-patch16-224", add_pooling_layer=False)
 
         >>> inputs = feature_extractor(images=image, return_tensors="pt")
         >>> outputs = model(**inputs)
@@ -604,13 +604,13 @@ class DeiTForImageClassification(DeiTPreTrainedModel):
         >>> from PIL import Image
         >>> import requests
 
-        >>> url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
         >>> image = Image.open(requests.get(url, stream=True).raw)
 
         >>> # note: we are loading a DeiTForImageClassificationWithTeacher from the hub here,
         >>> # so the head will be randomly initialized, hence the predictions will be random
-        >>> feature_extractor = DeiTFeatureExtractor.from_pretrained('facebook/deit-base-distilled-patch16-224')
-        >>> model = DeiTForImageClassification.from_pretrained('facebook/deit-base-distilled-patch16-224')
+        >>> feature_extractor = DeiTFeatureExtractor.from_pretrained("facebook/deit-base-distilled-patch16-224")
+        >>> model = DeiTForImageClassification.from_pretrained("facebook/deit-base-distilled-patch16-224")
 
         >>> inputs = feature_extractor(images=image, return_tensors="pt")
         >>> outputs = model(**inputs)
@@ -737,11 +737,11 @@ class DeiTForImageClassificationWithTeacher(DeiTPreTrainedModel):
         >>> from PIL import Image
         >>> import requests
 
-        >>> url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
         >>> image = Image.open(requests.get(url, stream=True).raw)
 
-        >>> feature_extractor = DeiTFeatureExtractor.from_pretrained('facebook/deit-base-distilled-patch16-224')
-        >>> model = DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224')
+        >>> feature_extractor = DeiTFeatureExtractor.from_pretrained("facebook/deit-base-distilled-patch16-224")
+        >>> model = DeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224")
 
         >>> inputs = feature_extractor(images=image, return_tensors="pt")
         >>> outputs = model(**inputs)

src/transformers/models/detr/modeling_detr.py

@@ -1224,11 +1224,11 @@ class DetrModel(DetrPreTrainedModel):
         >>> from PIL import Image
         >>> import requests
 
-        >>> url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
         >>> image = Image.open(requests.get(url, stream=True).raw)
 
-        >>> feature_extractor = DetrFeatureExtractor.from_pretrained('facebook/detr-resnet-50')
-        >>> model = DetrModel.from_pretrained('facebook/detr-resnet-50')
+        >>> feature_extractor = DetrFeatureExtractor.from_pretrained("facebook/detr-resnet-50")
+        >>> model = DetrModel.from_pretrained("facebook/detr-resnet-50")
         >>> inputs = feature_extractor(images=image, return_tensors="pt")
         >>> outputs = model(**inputs)
         >>> last_hidden_states = outputs.last_hidden_state
@@ -1381,11 +1381,11 @@ class DetrForObjectDetection(DetrPreTrainedModel):
         >>> from PIL import Image
         >>> import requests
 
-        >>> url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
         >>> image = Image.open(requests.get(url, stream=True).raw)
 
-        >>> feature_extractor = DetrFeatureExtractor.from_pretrained('facebook/detr-resnet-50')
-        >>> model = DetrForObjectDetection.from_pretrained('facebook/detr-resnet-50')
+        >>> feature_extractor = DetrFeatureExtractor.from_pretrained("facebook/detr-resnet-50")
+        >>> model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
 
         >>> inputs = feature_extractor(images=image, return_tensors="pt")
         >>> outputs = model(**inputs)
@@ -1537,11 +1537,11 @@ class DetrForSegmentation(DetrPreTrainedModel):
         >>> from PIL import Image
         >>> import requests
 
-        >>> url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
         >>> image = Image.open(requests.get(url, stream=True).raw)
 
-        >>> feature_extractor = DetrFeatureExtractor.from_pretrained('facebook/detr-resnet-50-panoptic')
-        >>> model = DetrForSegmentation.from_pretrained('facebook/detr-resnet-50-panoptic')
+        >>> feature_extractor = DetrFeatureExtractor.from_pretrained("facebook/detr-resnet-50-panoptic")
+        >>> model = DetrForSegmentation.from_pretrained("facebook/detr-resnet-50-panoptic")
 
         >>> inputs = feature_extractor(images=image, return_tensors="pt")
         >>> outputs = model(**inputs)