Add doc tests for Albert and Bigbird (#16774)

* Add doctest BERT

* make fixup

* fix typo

* change checkpoints

* make fixup

* define doctest output value, update doctest for mobilebert

* solve fix-copies

* update QA target start index and end index

* change checkpoint for docs and reuse defined variable

* Update src/transformers/models/bert/modeling_tf_bert.py

Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>

* Apply suggestions from code review

Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>

* Apply suggestions from code review

Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>

* make fixup

* Add Doctest for Albert and Bigbird

* make fixup

* overwrite examples for Albert and Bigbird

* Apply suggestions from code review

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* update longer examples for Bigbird

* using examples from squad_v2

* print out example text

* change name token-classification-big-bird checkpoint to random

Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

src/transformers/models/albert/modeling_albert.py

@@ -801,9 +801,8 @@ class AlbertForPreTraining(AlbertPreTrainedModel):
         >>> tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
         >>> model = AlbertForPreTraining.from_pretrained("albert-base-v2")
 
-        >>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(
+        >>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)
-        ...     0
+        >>> # Batch size 1
-        >>> )  # Batch size 1
         >>> outputs = model(input_ids)
 
         >>> prediction_logits = outputs.prediction_logits
@@ -914,12 +913,7 @@ class AlbertForMaskedLM(AlbertPreTrainedModel):
         return self.albert.embeddings.word_embeddings
 
     @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
+    @replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC)
-        processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=MaskedLMOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
     def forward(
         self,
         input_ids=None,
@@ -938,6 +932,37 @@ class AlbertForMaskedLM(AlbertPreTrainedModel):
             Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
             config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
             loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> import torch
+        >>> from transformers import AlbertTokenizer, AlbertForMaskedLM
+
+        >>> tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
+        >>> model = AlbertForMaskedLM.from_pretrained("albert-base-v2")
+
+        >>> # add mask_token
+        >>> inputs = tokenizer("The capital of [MASK] is Paris.", return_tensors="pt")
+        >>> with torch.no_grad():
+        ...     logits = model(**inputs).logits
+
+        >>> # retrieve index of [MASK]
+        >>> mask_token_index = (inputs.input_ids == tokenizer.mask_token_id)[0].nonzero(as_tuple=True)[0]
+        >>> predicted_token_id = logits[0, mask_token_index].argmax(axis=-1)
+        >>> tokenizer.decode(predicted_token_id)
+        'france'
+        ```
+
+        ```python
+        >>> labels = tokenizer("The capital of France is Paris.", return_tensors="pt")["input_ids"]
+        >>> labels = torch.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)
+        >>> outputs = model(**inputs, labels=labels)
+        >>> round(outputs.loss.item(), 2)
+        0.81
+        ```
         """
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
@@ -996,9 +1021,11 @@ class AlbertForSequenceClassification(AlbertPreTrainedModel):
     @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint=_CHECKPOINT_FOR_DOC,
+        checkpoint="textattack/albert-base-v2-imdb",
         output_type=SequenceClassifierOutput,
         config_class=_CONFIG_FOR_DOC,
+        expected_output="'LABEL_1'",
+        expected_loss=0.12,
     )
     def forward(
         self,
@@ -1103,9 +1130,12 @@ class AlbertForTokenClassification(AlbertPreTrainedModel):
     @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint=_CHECKPOINT_FOR_DOC,
+        checkpoint="vumichien/tiny-albert",
         output_type=TokenClassifierOutput,
         config_class=_CONFIG_FOR_DOC,
+        expected_output="['LABEL_1', 'LABEL_1', 'LABEL_1', 'LABEL_0', 'LABEL_1', 'LABEL_0', 'LABEL_1', 'LABEL_1', "
+        "'LABEL_0', 'LABEL_1', 'LABEL_0', 'LABEL_0', 'LABEL_1', 'LABEL_1']",
+        expected_loss=0.66,
     )
     def forward(
         self,
@@ -1184,9 +1214,13 @@ class AlbertForQuestionAnswering(AlbertPreTrainedModel):
     @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint=_CHECKPOINT_FOR_DOC,
+        checkpoint="twmkn9/albert-base-v2-squad2",
         output_type=QuestionAnsweringModelOutput,
         config_class=_CONFIG_FOR_DOC,
+        qa_target_start_index=12,
+        qa_target_end_index=13,
+        expected_output="'a nice puppet'",
+        expected_loss=7.36,
     )
     def forward(
         self,

src/transformers/models/albert/modeling_tf_albert.py

@@ -865,9 +865,8 @@ class TFAlbertForPreTraining(TFAlbertPreTrainedModel, TFAlbertPreTrainingLoss):
         >>> tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
         >>> model = TFAlbertForPreTraining.from_pretrained("albert-base-v2")
 
-        >>> input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[
+        >>> input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :]
-        ...     None, :
+        >>> # Batch size 1
-        >>> ]  # Batch size 1
         >>> outputs = model(input_ids)
 
         >>> prediction_logits = outputs.prediction_logits
@@ -954,12 +953,7 @@ class TFAlbertForMaskedLM(TFAlbertPreTrainedModel, TFMaskedLanguageModelingLoss)
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
+    @replace_return_docstrings(output_type=TFMaskedLMOutput, config_class=_CONFIG_FOR_DOC)
-        processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=TFMaskedLMOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
     def call(
         self,
         input_ids: Optional[TFModelInputType] = None,
@@ -979,6 +973,36 @@ class TFAlbertForMaskedLM(TFAlbertPreTrainedModel, TFMaskedLanguageModelingLoss)
             Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
             config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
             loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> import tensorflow as tf
+        >>> from transformers import AlbertTokenizer, TFAlbertForMaskedLM
+
+        >>> tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
+        >>> model = TFAlbertForMaskedLM.from_pretrained("albert-base-v2")
+
+        >>> # add mask_token
+        >>> inputs = tokenizer(f"The capital of [MASK] is Paris.", return_tensors="tf")
+        >>> logits = model(**inputs).logits
+
+        >>> # retrieve index of [MASK]
+        >>> mask_token_index = tf.where(inputs.input_ids == tokenizer.mask_token_id)[0][1]
+        >>> predicted_token_id = tf.math.argmax(logits[0, mask_token_index], axis=-1)
+        >>> tokenizer.decode(predicted_token_id)
+        'france'
+        ```
+
+        ```python
+        >>> labels = tokenizer("The capital of France is Paris.", return_tensors="tf")["input_ids"]
+        >>> labels = tf.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)
+        >>> outputs = model(**inputs, labels=labels)
+        >>> round(float(outputs.loss), 2)
+        0.81
+        ```
         """
         outputs = self.albert(
             input_ids=input_ids,
@@ -1043,9 +1067,11 @@ class TFAlbertForSequenceClassification(TFAlbertPreTrainedModel, TFSequenceClass
     @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint=_CHECKPOINT_FOR_DOC,
+        checkpoint="vumichien/albert-base-v2-imdb",
         output_type=TFSequenceClassifierOutput,
         config_class=_CONFIG_FOR_DOC,
+        expected_output="'LABEL_1'",
+        expected_loss=0.12,
     )
     def call(
         self,
@@ -1136,9 +1162,12 @@ class TFAlbertForTokenClassification(TFAlbertPreTrainedModel, TFTokenClassificat
     @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint=_CHECKPOINT_FOR_DOC,
+        checkpoint="vumichien/tiny-albert",
         output_type=TFTokenClassifierOutput,
         config_class=_CONFIG_FOR_DOC,
+        expected_output="['LABEL_1', 'LABEL_1', 'LABEL_1', 'LABEL_0', 'LABEL_1', 'LABEL_0', 'LABEL_1', 'LABEL_1', "
+        "'LABEL_0', 'LABEL_1', 'LABEL_0', 'LABEL_0', 'LABEL_1', 'LABEL_1']",
+        expected_loss=0.66,
     )
     def call(
         self,
@@ -1220,9 +1249,13 @@ class TFAlbertForQuestionAnswering(TFAlbertPreTrainedModel, TFQuestionAnsweringL
     @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint=_CHECKPOINT_FOR_DOC,
+        checkpoint="vumichien/albert-base-v2-squad2",
         output_type=TFQuestionAnsweringModelOutput,
         config_class=_CONFIG_FOR_DOC,
+        qa_target_start_index=12,
+        qa_target_end_index=13,
+        expected_output="'a nice puppet'",
+        expected_loss=7.36,
     )
     def call(
         self,

src/transformers/models/big_bird/modeling_big_bird.py

@@ -2392,12 +2392,7 @@ class BigBirdForMaskedLM(BigBirdPreTrainedModel):
         self.cls.predictions.decoder = new_embeddings
 
     @add_start_docstrings_to_model_forward(BIG_BIRD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
+    @replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC)
-        processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=MaskedLMOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
     def forward(
         self,
         input_ids: torch.LongTensor = None,
@@ -2418,6 +2413,49 @@ class BigBirdForMaskedLM(BigBirdPreTrainedModel):
             Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
             config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
             loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> import torch
+        >>> from transformers import BigBirdTokenizer, BigBirdForMaskedLM
+        >>> from datasets import load_dataset
+
+        >>> tokenizer = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")
+        >>> model = BigBirdForMaskedLM.from_pretrained("google/bigbird-roberta-base")
+        >>> squad_ds = load_dataset("squad_v2", split="train")  # doctest: +IGNORE_RESULT
+
+        >>> # select random long article
+        >>> LONG_ARTICLE_TARGET = squad_ds[81514]["context"]
+        >>> # select random sentence
+        >>> LONG_ARTICLE_TARGET[332:398]
+        'the highest values are very close to the theoretical maximum value'
+
+        >>> # add mask_token
+        >>> LONG_ARTICLE_TO_MASK = LONG_ARTICLE_TARGET.replace("maximum", "[MASK]")
+        >>> inputs = tokenizer(LONG_ARTICLE_TO_MASK, return_tensors="pt")
+        >>> # long article input
+        >>> list(inputs["input_ids"].shape)
+        [1, 919]
+
+        >>> with torch.no_grad():
+        ...     logits = model(**inputs).logits
+        >>> # retrieve index of [MASK]
+        >>> mask_token_index = (inputs.input_ids == tokenizer.mask_token_id)[0].nonzero(as_tuple=True)[0]
+        >>> predicted_token_id = logits[0, mask_token_index].argmax(axis=-1)
+        >>> tokenizer.decode(predicted_token_id)
+        'maximum'
+        ```
+
+        ```python
+        >>> labels = tokenizer(LONG_ARTICLE_TARGET, return_tensors="pt")["input_ids"]
+        >>> labels = torch.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)
+        >>> outputs = model(**inputs, labels=labels)
+        >>> round(outputs.loss.item(), 2)
+        1.08
+        ```
         """
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
@@ -2496,7 +2534,12 @@ class BigBirdForCausalLM(BigBirdPreTrainedModel):
         self.cls.predictions.decoder = new_embeddings
 
     @add_start_docstrings_to_model_forward(BIG_BIRD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
+    @add_code_sample_docstrings(
+        processor_class=_TOKENIZER_FOR_DOC,
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=CausalLMOutputWithCrossAttentions,
+        config_class=_CONFIG_FOR_DOC,
+    )
     def forward(
         self,
         input_ids: torch.LongTensor = None,
@@ -2536,25 +2579,7 @@ class BigBirdForCausalLM(BigBirdPreTrainedModel):
         use_cache (`bool`, *optional*):
             If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
             `past_key_values`).
-
+        """
-        Returns:
-
-        Example:
-
-        ```python
-        >>> from transformers import BigBirdTokenizer, BigBirdForCausalLM, BigBirdConfig
-        >>> import torch
-
-        >>> 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)
-
-        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
-        >>> outputs = model(**inputs)
-
-        >>> prediction_logits = outputs.logits
-        ```"""
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         outputs = self.bert(
@@ -2662,12 +2687,7 @@ class BigBirdForSequenceClassification(BigBirdPreTrainedModel):
         self.post_init()
 
     @add_start_docstrings_to_model_forward(BIG_BIRD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
+    @replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
-        processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=SequenceClassifierOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
     def forward(
         self,
         input_ids: torch.LongTensor = None,
@@ -2686,6 +2706,43 @@ class BigBirdForSequenceClassification(BigBirdPreTrainedModel):
             Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
             config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
             `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> import torch
+        >>> from transformers import BigBirdTokenizer, BigBirdForSequenceClassification
+        >>> from datasets import load_dataset
+
+        >>> tokenizer = BigBirdTokenizer.from_pretrained("l-yohai/bigbird-roberta-base-mnli")
+        >>> model = BigBirdForSequenceClassification.from_pretrained("l-yohai/bigbird-roberta-base-mnli")
+        >>> squad_ds = load_dataset("squad_v2", split="train")  # doctest: +IGNORE_RESULT
+
+        >>> LONG_ARTICLE = squad_ds[81514]["context"]
+        >>> inputs = tokenizer(LONG_ARTICLE, return_tensors="pt")
+        >>> # long input article
+        >>> list(inputs["input_ids"].shape)
+        [1, 919]
+
+        >>> with torch.no_grad():
+        ...     logits = model(**inputs).logits
+        >>> predicted_class_id = logits.argmax().item()
+        >>> model.config.id2label[predicted_class_id]
+        'LABEL_0'
+        ```
+
+        ```python
+        >>> num_labels = len(model.config.id2label)
+        >>> model = BigBirdForSequenceClassification.from_pretrained(
+        ...     "l-yohai/bigbird-roberta-base-mnli", num_labels=num_labels
+        ... )
+        >>> labels = torch.tensor(1)
+        >>> loss = model(**inputs, labels=labels).loss
+        >>> round(loss.item(), 2)
+        1.13
+        ```
         """
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
@@ -2858,9 +2915,12 @@ class BigBirdForTokenClassification(BigBirdPreTrainedModel):
     @add_start_docstrings_to_model_forward(BIG_BIRD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint=_CHECKPOINT_FOR_DOC,
+        checkpoint="vumichien/token-classification-bigbird-roberta-base-random",
         output_type=TokenClassifierOutput,
         config_class=_CONFIG_FOR_DOC,
+        expected_output="['LABEL_1', 'LABEL_1', 'LABEL_1', 'LABEL_1', 'LABEL_1', 'LABEL_1', 'LABEL_1', 'LABEL_1', "
+        "'LABEL_1', 'LABEL_1', 'LABEL_1', 'LABEL_1']",
+        expected_loss=0.54,
     )
     def forward(
         self,
@@ -2955,12 +3015,7 @@ class BigBirdForQuestionAnswering(BigBirdPreTrainedModel):
         self.post_init()
 
     @add_start_docstrings_to_model_forward(BIG_BIRD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
+    @replace_return_docstrings(output_type=BigBirdForQuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC)
-        processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint="google/bigbird-base-trivia-itc",
-        output_type=BigBirdForQuestionAnsweringModelOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
     def forward(
         self,
         input_ids: torch.LongTensor = None,
@@ -2985,6 +3040,48 @@ class BigBirdForQuestionAnswering(BigBirdPreTrainedModel):
             Labels for position (index) of the end of the labelled span for computing the token classification loss.
             Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
             are not taken into account for computing the loss.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> import torch
+        >>> from transformers import BigBirdTokenizer, BigBirdForQuestionAnswering
+        >>> from datasets import load_dataset
+
+        >>> tokenizer = BigBirdTokenizer.from_pretrained("abhinavkulkarni/bigbird-roberta-base-finetuned-squad")
+        >>> model = BigBirdForQuestionAnswering.from_pretrained("abhinavkulkarni/bigbird-roberta-base-finetuned-squad")
+        >>> squad_ds = load_dataset("squad_v2", split="train")  # doctest: +IGNORE_RESULT
+
+        >>> # select random article and question
+        >>> LONG_ARTICLE = squad_ds[81514]["context"]
+        >>> QUESTION = squad_ds[81514]["question"]
+        >>> QUESTION
+        'During daytime how high can the temperatures reach?'
+
+        >>> inputs = tokenizer(QUESTION, LONG_ARTICLE, return_tensors="pt")
+        >>> # long article and question input
+        >>> list(inputs["input_ids"].shape)
+        [1, 929]
+
+        >>> with torch.no_grad():
+        ...     outputs = model(**inputs)
+
+        >>> answer_start_index = outputs.start_logits.argmax()
+        >>> answer_end_index = outputs.end_logits.argmax()
+        >>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
+        >>> tokenizer.decode(predict_answer_tokens)
+        '80 °C (176 °F) or more'
+        ```
+
+        ```python
+        >>> target_start_index, target_end_index = torch.tensor([130]), torch.tensor([132])
+        >>> outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
+        >>> loss = outputs.loss
+        >>> round(outputs.loss.item(), 2)
+        7.63
+        ```
         """
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 

utils/documentation_tests.txt

@@ -6,11 +6,14 @@ docs/source/en/model_doc/t5v1.1.mdx
 docs/source/en/model_doc/byt5.mdx
 docs/source/en/model_doc/tapex.mdx
 src/transformers/generation_utils.py
+src/transformers/models/albert/modeling_albert.py
+src/transformers/models/albert/modeling_tf_albert.py
 src/transformers/models/bart/modeling_bart.py
 src/transformers/models/beit/modeling_beit.py
 src/transformers/models/bert/modeling_bert.py
 src/transformers/models/bert/modeling_tf_bert.py
 src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py
+src/transformers/models/big_bird/modeling_big_bird.py
 src/transformers/models/blenderbot/modeling_blenderbot.py
 src/transformers/models/blenderbot_small/modeling_blenderbot_small.py
 src/transformers/models/convnext/modeling_convnext.py