fix more doctests (#22292)

* fix more doctests

* fix style

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

src/transformers/models/bart/tokenization_bart.py

@@ -105,12 +105,14 @@ class BartTokenizer(PreTrainedTokenizer):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import BartTokenizer
+
     >>> tokenizer = BartTokenizer.from_pretrained("facebook/bart-base")
-    >>> tokenizer("Hello world")['input_ids']
+    >>> tokenizer("Hello world")["input_ids"]
     [0, 31414, 232, 2]
-    >>> tokenizer(" Hello world")['input_ids']
+
+    >>> tokenizer(" Hello world")["input_ids"]
     [0, 20920, 232, 2]
     ```
 

src/transformers/models/bart/tokenization_bart_fast.py

@@ -75,12 +75,14 @@ class BartTokenizerFast(PreTrainedTokenizerFast):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import BartTokenizerFast
+
     >>> tokenizer = BartTokenizerFast.from_pretrained("facebook/bart-base")
-    >>> tokenizer("Hello world")['input_ids']
+    >>> tokenizer("Hello world")["input_ids"]
     [0, 31414, 232, 2]
-    >>> tokenizer(" Hello world")['input_ids']
+
+    >>> tokenizer(" Hello world")["input_ids"]
     [0, 20920, 232, 2]
     ```
 

src/transformers/models/blenderbot/tokenization_blenderbot.py

@@ -96,13 +96,15 @@ class BlenderbotTokenizer(PreTrainedTokenizer):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import BlenderbotTokenizer
+
     >>> tokenizer = BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B")
     >>> tokenizer.add_prefix_space = False
-    >>> tokenizer("Hello world")['input_ids']
+    >>> tokenizer("Hello world")["input_ids"]
     [47, 921, 86, 1085, 2]
-    >>> tokenizer(" Hello world")['input_ids']
+
+    >>> tokenizer(" Hello world")["input_ids"]
     [6950, 1085, 2]
     ```
 

src/transformers/models/blenderbot/tokenization_blenderbot_fast.py

@@ -55,12 +55,14 @@ class BlenderbotTokenizerFast(PreTrainedTokenizerFast):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import BlenderbotTokenizerFast
+
     >>> tokenizer = BlenderbotTokenizerFast.from_pretrained("facebook/blenderbot-3B")
-    >>> tokenizer("Hello world")['input_ids']
+    >>> tokenizer("Hello world")["input_ids"]
     [6950, 1085, 2]
-    >>> tokenizer(" Hello world")['input_ids']
+
+    >>> tokenizer(" Hello world")["input_ids"]
     [6950, 1085, 2]
     ```
 

src/transformers/models/bloom/tokenization_bloom_fast.py

@@ -54,13 +54,15 @@ class BloomTokenizerFast(PreTrainedTokenizerFast):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import BloomTokenizerFast
+
     >>> tokenizer = BloomTokenizerFast.from_pretrained("bigscience/bloom")
-    >>> tokenizer("Hello world")['input_ids']
-    [15496, 995]
-    >>> tokenizer(" Hello world")['input_ids']
-    [18435, 995]
+    >>> tokenizer("Hello world")["input_ids"]
+    [59414, 8876]
+
+    >>> tokenizer(" Hello world")["input_ids"]
+    [86153, 8876]
     ```
 
     You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer, but since

src/transformers/models/codegen/tokenization_codegen.py

@@ -102,12 +102,14 @@ class CodeGenTokenizer(PreTrainedTokenizer):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import CodeGenTokenizer
+
     >>> tokenizer = CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono")
-    >>> tokenizer("Hello world")['input_ids']
+    >>> tokenizer("Hello world")["input_ids"]
     [15496, 995]
-    >>> tokenizer(" Hello world")['input_ids']
+
+    >>> tokenizer(" Hello world")["input_ids"]
     [18435, 995]
     ```
 

src/transformers/models/codegen/tokenization_codegen_fast.py

@@ -68,12 +68,14 @@ class CodeGenTokenizerFast(PreTrainedTokenizerFast):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import CodeGenTokenizerFast
+
     >>> tokenizer = CodeGenTokenizerFast.from_pretrained("Salesforce/codegen-350M-mono")
-    >>> tokenizer("Hello world")['input_ids']
+    >>> tokenizer("Hello world")["input_ids"]
     [15496, 995]
-    >>> tokenizer(" Hello world")['input_ids']
+
+    >>> tokenizer(" Hello world")["input_ids"]
     [18435, 995]
     ```
 

src/transformers/models/deberta/tokenization_deberta.py

@@ -116,13 +116,15 @@ class DebertaTokenizer(PreTrainedTokenizer):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import DebertaTokenizer
+
     >>> tokenizer = DebertaTokenizer.from_pretrained("microsoft/deberta-base")
-    >>> tokenizer("Hello world")['input_ids']
-    [15496, 995]
-    >>> tokenizer(" Hello world")['input_ids']
-    [18435, 995]
+    >>> tokenizer("Hello world")["input_ids"]
+    [1, 31414, 232, 2]
+
+    >>> tokenizer(" Hello world")["input_ids"]
+    [1, 20920, 232, 2]
     ```
 
     You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you

src/transformers/models/deberta/tokenization_deberta_fast.py

@@ -79,13 +79,15 @@ class DebertaTokenizerFast(PreTrainedTokenizerFast):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import DebertaTokenizerFast
+
     >>> tokenizer = DebertaTokenizerFast.from_pretrained("microsoft/deberta-base")
-    >>> tokenizer("Hello world")['input_ids']
-    [15496, 995]
-    >>> tokenizer(" Hello world")['input_ids']
-    [18435, 995]
+    >>> tokenizer("Hello world")["input_ids"]
+    [1, 31414, 232, 2]
+
+    >>> tokenizer(" Hello world")["input_ids"]
+    [1, 20920, 232, 2]
     ```
 
     You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer, but since

src/transformers/models/gpt2/tokenization_gpt2.py

@@ -108,12 +108,14 @@ class GPT2Tokenizer(PreTrainedTokenizer):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import GPT2Tokenizer
+
     >>> tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
-    >>> tokenizer("Hello world")['input_ids']
+    >>> tokenizer("Hello world")["input_ids"]
     [15496, 995]
-    >>> tokenizer(" Hello world")['input_ids']
+
+    >>> tokenizer(" Hello world")["input_ids"]
     [18435, 995]
     ```
 

src/transformers/models/gpt2/tokenization_gpt2_fast.py

@@ -75,12 +75,14 @@ class GPT2TokenizerFast(PreTrainedTokenizerFast):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import GPT2TokenizerFast
+
     >>> tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")
-    >>> tokenizer("Hello world")['input_ids']
+    >>> tokenizer("Hello world")["input_ids"]
     [15496, 995]
-    >>> tokenizer(" Hello world")['input_ids']
+
+    >>> tokenizer(" Hello world")["input_ids"]
     [18435, 995]
     ```
 

src/transformers/models/gpt_neox/tokenization_gpt_neox_fast.py

@@ -49,12 +49,14 @@ class GPTNeoXTokenizerFast(PreTrainedTokenizerFast):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import GPTNeoXTokenizerFast
+
     >>> tokenizer = GPTNeoXTokenizerFast.from_pretrained("gpt2")
-    >>> tokenizer("Hello world")['input_ids']
+    >>> tokenizer("Hello world")["input_ids"]
     [15496, 995]
-    >>> tokenizer(" Hello world")['input_ids']
+
+    >>> tokenizer(" Hello world")["input_ids"]
     [18435, 995]
     ```
 

src/transformers/models/gpt_sw3/tokenization_gpt_sw3.py

@@ -47,10 +47,11 @@ class GPTSw3Tokenizer(PreTrainedTokenizer):
     this superclass for more information regarding those methods.
 
     Example usage:
-    ```
+    ```python
     >>> from transformers import GPTSw3Tokenizer
+
     >>> tokenizer = GPTSw3Tokenizer.from_pretrained("AI-Sweden/gpt-sw3-126m")
-    >>> tokenizer("Svenska är kul!")['input_ids']
+    >>> tokenizer("Svenska är kul!")["input_ids"]
     [1814, 377, 3617, 63504]
     ```
 

src/transformers/models/jukebox/tokenization_jukebox.py

@@ -68,13 +68,13 @@ class JukeboxTokenizer(PreTrainedTokenizer):
     as the conditioning of the model can be done on the three different queries. If None is provided, defaults values will be used.:
 
     Depending on the number of genres on which the model should be conditioned (`n_genres`).
-    ```
+    ```python
     >>> from transformers import JukeboxTokenizer
+
     >>> tokenizer = JukeboxTokenizer.from_pretrained("openai/jukebox-1b-lyrics")
-    >>> tokenizer("Alan Jackson", "Country Rock", "old town road")['input_ids']
+    >>> tokenizer("Alan Jackson", "Country Rock", "old town road")["input_ids"]
     [tensor([[   0,    0,    0, 6785,  546,   41,   38,   30,   76,   46,   41,   49,
                40,   76,   44,   41,   27,   30]]), tensor([[  0,   0,   0, 145,   0]]), tensor([[  0,   0,   0, 145,   0]])]
-
     ```
 
     You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you

src/transformers/models/led/tokenization_led.py

@@ -97,12 +97,14 @@ class LEDTokenizer(PreTrainedTokenizer):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import LEDTokenizer
+
     >>> tokenizer = LEDTokenizer.from_pretrained("allenai/led-base-16384")
-    >>> tokenizer("Hello world")['input_ids']
+    >>> tokenizer("Hello world")["input_ids"]
     [0, 31414, 232, 2]
-    >>> tokenizer(" Hello world")['input_ids']
+
+    >>> tokenizer(" Hello world")["input_ids"]
     [0, 20920, 232, 2]
     ```
 

src/transformers/models/led/tokenization_led_fast.py

@@ -55,12 +55,14 @@ class LEDTokenizerFast(PreTrainedTokenizerFast):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import LEDTokenizerFast
+
     >>> tokenizer = LEDTokenizerFast.from_pretrained("allenai/led-base-16384")
-    >>> tokenizer("Hello world")['input_ids']
+    >>> tokenizer("Hello world")["input_ids"]
     [0, 31414, 232, 2]
-    >>> tokenizer(" Hello world")['input_ids']
+
+    >>> tokenizer(" Hello world")["input_ids"]
     [0, 20920, 232, 2]
     ```
 

src/transformers/models/longformer/tokenization_longformer.py

@@ -120,12 +120,14 @@ class LongformerTokenizer(PreTrainedTokenizer):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import LongformerTokenizer
+
     >>> tokenizer = LongformerTokenizer.from_pretrained("allenai/longformer-base-4096")
-    >>> tokenizer("Hello world")['input_ids']
+    >>> tokenizer("Hello world")["input_ids"]
     [0, 31414, 232, 2]
-    >>> tokenizer(" Hello world")['input_ids']
+
+    >>> tokenizer(" Hello world")["input_ids"]
     [0, 20920, 232, 2]
     ```
 

src/transformers/models/longformer/tokenization_longformer_fast.py

@@ -96,12 +96,14 @@ class LongformerTokenizerFast(PreTrainedTokenizerFast):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import LongformerTokenizerFast
+
     >>> tokenizer = LongformerTokenizerFast.from_pretrained("allenai/longformer-base-4096")
-    >>> tokenizer("Hello world")['input_ids']
+    >>> tokenizer("Hello world")["input_ids"]
     [0, 31414, 232, 2]
-    >>> tokenizer(" Hello world")['input_ids']
+
+    >>> tokenizer(" Hello world")["input_ids"]
     [0, 20920, 232, 2]
     ```
 

src/transformers/models/luke/tokenization_luke.py

@@ -197,12 +197,14 @@ class LukeTokenizer(PreTrainedTokenizer):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import LukeTokenizer
+
     >>> tokenizer = LukeTokenizer.from_pretrained("studio-ousia/luke-base")
-    >>> tokenizer("Hello world")['input_ids']
+    >>> tokenizer("Hello world")["input_ids"]
     [0, 31414, 232, 2]
-    >>> tokenizer(" Hello world")['input_ids']
+
+    >>> tokenizer(" Hello world")["input_ids"]
     [0, 20920, 232, 2]
     ```
 

src/transformers/models/mvp/tokenization_mvp.py

@@ -93,12 +93,14 @@ class MvpTokenizer(PreTrainedTokenizer):
     This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
     be encoded differently whether it is at the beginning of the sentence (without space) or not:
 
-    ```
+    ```python
     >>> from transformers import MvpTokenizer
+
     >>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp")
-    >>> tokenizer("Hello world")['input_ids']
+    >>> tokenizer("Hello world")["input_ids"]
     [0, 31414, 232, 2]
-    >>> tokenizer(" Hello world")['input_ids']
+
+    >>> tokenizer(" Hello world")["input_ids"]
     [0, 20920, 232, 2]
     ```