Models docstring (#21225)

* Clean all models * Style * Last to remove * address review comments * Address review comments
huggingface · Jan 23, 2023 · fd5cdae · fd5cdae
1 parent 9e86c4e
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diff --git a/src/transformers/modeling_flax_utils.py b/src/transformers/modeling_flax_utils.py
@@ -1124,10 +1124,9 @@ def overwrite_call_docstring(model_class, docstring):
     model_class.__call__ = add_start_docstrings_to_model_forward(docstring)(model_class.__call__)
 
 
-def append_call_sample_docstring(model_class, tokenizer_class, checkpoint, output_type, config_class, mask=None):
+def append_call_sample_docstring(model_class, checkpoint, output_type, config_class, mask=None):
     model_class.__call__ = copy_func(model_class.__call__)
     model_class.__call__ = add_code_sample_docstrings(
-        processor_class=tokenizer_class,
         checkpoint=checkpoint,
         output_type=output_type,
         config_class=config_class,

diff --git a/src/transformers/models/albert/modeling_albert.py b/src/transformers/models/albert/modeling_albert.py
@@ -50,7 +50,6 @@
 
 _CHECKPOINT_FOR_DOC = "albert-base-v2"
 _CONFIG_FOR_DOC = "AlbertConfig"
-_TOKENIZER_FOR_DOC = "AlbertTokenizer"
 
 
 ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
@@ -582,7 +581,7 @@ class AlbertForPreTrainingOutput(ModelOutput):
         input_ids (`torch.LongTensor` of shape `({0})`):
             Indices of input sequence tokens in the vocabulary.
 
-            Indices can be obtained using [`AlbertTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
             [`PreTrainedTokenizer.encode`] for details.
 
             [What are input IDs?](../glossary#input-ids)
@@ -677,7 +676,6 @@ def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None:
 
     @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,
         output_type=BaseModelOutputWithPooling,
         config_class=_CONFIG_FOR_DOC,
@@ -818,10 +816,10 @@ def forward(
         Example:
 
         ```python
-        >>> from transformers import AlbertTokenizer, AlbertForPreTraining
+        >>> from transformers import AutoTokenizer, AlbertForPreTraining
         >>> import torch
 
-        >>> tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
+        >>> tokenizer = AutoTokenizer.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(0)
@@ -967,9 +965,9 @@ def forward(
 
         ```python
         >>> import torch
-        >>> from transformers import AlbertTokenizer, AlbertForMaskedLM
+        >>> from transformers import AutoTokenizer, AlbertForMaskedLM
 
-        >>> tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
+        >>> tokenizer = AutoTokenizer.from_pretrained("albert-base-v2")
         >>> model = AlbertForMaskedLM.from_pretrained("albert-base-v2")
 
         >>> # add mask_token
@@ -1048,7 +1046,6 @@ def __init__(self, config: AlbertConfig):
 
     @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
-        processor_class=_TOKENIZER_FOR_DOC,
         checkpoint="textattack/albert-base-v2-imdb",
         output_type=SequenceClassifierOutput,
         config_class=_CONFIG_FOR_DOC,
@@ -1157,15 +1154,9 @@ def __init__(self, config: AlbertConfig):
 
     @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
-        processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint="vumichien/tiny-albert",
+        checkpoint=_CHECKPOINT_FOR_DOC,
         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,
@@ -1243,7 +1234,6 @@ def __init__(self, config: AlbertConfig):
 
     @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
-        processor_class=_TOKENIZER_FOR_DOC,
         checkpoint="twmkn9/albert-base-v2-squad2",
         output_type=QuestionAnsweringModelOutput,
         config_class=_CONFIG_FOR_DOC,
@@ -1347,7 +1337,6 @@ def __init__(self, config: AlbertConfig):
 
     @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
     @add_code_sample_docstrings(
-        processor_class=_TOKENIZER_FOR_DOC,
         checkpoint=_CHECKPOINT_FOR_DOC,
         output_type=MultipleChoiceModelOutput,
         config_class=_CONFIG_FOR_DOC,

diff --git a/src/transformers/models/albert/modeling_flax_albert.py b/src/transformers/models/albert/modeling_flax_albert.py
@@ -50,7 +50,6 @@
 
 _CHECKPOINT_FOR_DOC = "albert-base-v2"
 _CONFIG_FOR_DOC = "AlbertConfig"
-_TOKENIZER_FOR_DOC = "AlbertTokenizer"
 
 
 @flax.struct.dataclass
@@ -122,7 +121,7 @@ class FlaxAlbertForPreTrainingOutput(ModelOutput):
         input_ids (`numpy.ndarray` of shape `({0})`):
             Indices of input sequence tokens in the vocabulary.
 
-            Indices can be obtained using [`AlbertTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
             [`PreTrainedTokenizer.__call__`] for details.
 
             [What are input IDs?](../glossary#input-ids)
@@ -680,9 +679,7 @@ class FlaxAlbertModel(FlaxAlbertPreTrainedModel):
     module_class = FlaxAlbertModule
 
 
-append_call_sample_docstring(
-    FlaxAlbertModel, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutputWithPooling, _CONFIG_FOR_DOC
-)
+append_call_sample_docstring(FlaxAlbertModel, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutputWithPooling, _CONFIG_FOR_DOC)
 
 
 class FlaxAlbertForPreTrainingModule(nn.Module):
@@ -757,9 +754,9 @@ class FlaxAlbertForPreTraining(FlaxAlbertPreTrainedModel):
     Example:
 
     ```python
-    >>> from transformers import AlbertTokenizer, FlaxAlbertForPreTraining
+    >>> from transformers import AutoTokenizer, FlaxAlbertForPreTraining
 
-    >>> tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
+    >>> tokenizer = AutoTokenizer.from_pretrained("albert-base-v2")
     >>> model = FlaxAlbertForPreTraining.from_pretrained("albert-base-v2")
 
     >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="np")
@@ -834,9 +831,7 @@ class FlaxAlbertForMaskedLM(FlaxAlbertPreTrainedModel):
     module_class = FlaxAlbertForMaskedLMModule
 
 
-append_call_sample_docstring(
-    FlaxAlbertForMaskedLM, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxMaskedLMOutput, _CONFIG_FOR_DOC
-)
+append_call_sample_docstring(FlaxAlbertForMaskedLM, _CHECKPOINT_FOR_DOC, FlaxMaskedLMOutput, _CONFIG_FOR_DOC)
 
 
 class FlaxAlbertForSequenceClassificationModule(nn.Module):
@@ -906,7 +901,6 @@ class FlaxAlbertForSequenceClassification(FlaxAlbertPreTrainedModel):
 
 append_call_sample_docstring(
     FlaxAlbertForSequenceClassification,
-    _TOKENIZER_FOR_DOC,
     _CHECKPOINT_FOR_DOC,
     FlaxSequenceClassifierOutput,
     _CONFIG_FOR_DOC,
@@ -983,7 +977,6 @@ class FlaxAlbertForMultipleChoice(FlaxAlbertPreTrainedModel):
 )
 append_call_sample_docstring(
     FlaxAlbertForMultipleChoice,
-    _TOKENIZER_FOR_DOC,
     _CHECKPOINT_FOR_DOC,
     FlaxMultipleChoiceModelOutput,
     _CONFIG_FOR_DOC,
@@ -1054,7 +1047,6 @@ class FlaxAlbertForTokenClassification(FlaxAlbertPreTrainedModel):
 
 append_call_sample_docstring(
     FlaxAlbertForTokenClassification,
-    _TOKENIZER_FOR_DOC,
     _CHECKPOINT_FOR_DOC,
     FlaxTokenClassifierOutput,
     _CONFIG_FOR_DOC,
@@ -1123,7 +1115,6 @@ class FlaxAlbertForQuestionAnswering(FlaxAlbertPreTrainedModel):
 
 append_call_sample_docstring(
     FlaxAlbertForQuestionAnswering,
-    _TOKENIZER_FOR_DOC,
     _CHECKPOINT_FOR_DOC,
     FlaxQuestionAnsweringModelOutput,
     _CONFIG_FOR_DOC,

diff --git a/src/transformers/models/albert/modeling_tf_albert.py b/src/transformers/models/albert/modeling_tf_albert.py
@@ -61,7 +61,6 @@
 
 _CHECKPOINT_FOR_DOC = "albert-base-v2"
 _CONFIG_FOR_DOC = "AlbertConfig"
-_TOKENIZER_FOR_DOC = "AlbertTokenizer"
 
 TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
     "albert-base-v1",
@@ -738,7 +737,7 @@ class TFAlbertForPreTrainingOutput(ModelOutput):
         input_ids (`Numpy array` or `tf.Tensor` of shape `({0})`):
             Indices of input sequence tokens in the vocabulary.
 
-            Indices can be obtained using [`AlbertTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
             [`PreTrainedTokenizer.encode`] for details.
 
             [What are input IDs?](../glossary#input-ids)
@@ -802,7 +801,6 @@ def __init__(self, config: AlbertConfig, *inputs, **kwargs):
     @unpack_inputs
     @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,
         output_type=TFBaseModelOutputWithPooling,
         config_class=_CONFIG_FOR_DOC,
@@ -895,9 +893,9 @@ def call(
 
         ```python
         >>> import tensorflow as tf
-        >>> from transformers import AlbertTokenizer, TFAlbertForPreTraining
+        >>> from transformers import AutoTokenizer, TFAlbertForPreTraining
 
-        >>> tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
+        >>> tokenizer = AutoTokenizer.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))[None, :]
@@ -1015,9 +1013,9 @@ def call(
 
         ```python
         >>> import tensorflow as tf
-        >>> from transformers import AlbertTokenizer, TFAlbertForMaskedLM
+        >>> from transformers import AutoTokenizer, TFAlbertForMaskedLM
 
-        >>> tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
+        >>> tokenizer = AutoTokenizer.from_pretrained("albert-base-v2")
         >>> model = TFAlbertForMaskedLM.from_pretrained("albert-base-v2")
 
         >>> # add mask_token
@@ -1101,7 +1099,6 @@ def __init__(self, config: AlbertConfig, *inputs, **kwargs):
     @unpack_inputs
     @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
-        processor_class=_TOKENIZER_FOR_DOC,
         checkpoint="vumichien/albert-base-v2-imdb",
         output_type=TFSequenceClassifierOutput,
         config_class=_CONFIG_FOR_DOC,
@@ -1196,15 +1193,9 @@ def __init__(self, config: AlbertConfig, *inputs, **kwargs):
     @unpack_inputs
     @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
-        processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint="vumichien/tiny-albert",
+        checkpoint=_CHECKPOINT_FOR_DOC,
         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,
@@ -1285,7 +1276,6 @@ def __init__(self, config: AlbertConfig, *inputs, **kwargs):
     @unpack_inputs
     @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
-        processor_class=_TOKENIZER_FOR_DOC,
         checkpoint="vumichien/albert-base-v2-squad2",
         output_type=TFQuestionAnsweringModelOutput,
         config_class=_CONFIG_FOR_DOC,
@@ -1400,7 +1390,6 @@ def dummy_inputs(self):
     @unpack_inputs
     @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
     @add_code_sample_docstrings(
-        processor_class=_TOKENIZER_FOR_DOC,
         checkpoint=_CHECKPOINT_FOR_DOC,
         output_type=TFMultipleChoiceModelOutput,
         config_class=_CONFIG_FOR_DOC,

diff --git a/src/transformers/models/altclip/modeling_altclip.py b/src/transformers/models/altclip/modeling_altclip.py
@@ -37,7 +37,6 @@
 
 logger = logging.get_logger(__name__)
 
-_TOKENIZER_FOR_DOC = "XLMRobertaTokenizer"
 _CHECKPOINT_FOR_DOC = "BAAI/AltCLIP"
 _CONFIG_FOR_DOC = "AltCLIPConfig"
 
@@ -68,7 +67,7 @@
             Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
             it.
 
-            Indices can be obtained using [`XLMRobertaTokenizerFast`]. See [`PreTrainedTokenizer.encode`] and
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
             [`PreTrainedTokenizer.__call__`] for details.
 
             [What are input IDs?](../glossary#input-ids)
@@ -98,7 +97,7 @@
     Args:
         pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
             Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
-            [`CLIPImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details.
+            [`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details.
         output_attentions (`bool`, *optional*):
             Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
             tensors for more detail.
@@ -115,7 +114,7 @@
             Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
             it.
 
-            Indices can be obtained using [`XLMRobertaTokenizerFast`]. See [`PreTrainedTokenizer.encode`] and
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
             [`PreTrainedTokenizer.__call__`] for details.
 
             [What are input IDs?](../glossary#input-ids)
@@ -133,7 +132,7 @@
             [What are position IDs?](../glossary#position-ids)
         pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
             Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
-            [`CLIPImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details.
+            [`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details.
         return_loss (`bool`, *optional*):
             Whether or not to return the contrastive loss.
         output_attentions (`bool`, *optional*):
@@ -1181,10 +1180,10 @@ def forward(
         ```python
         >>> from PIL import Image
         >>> import requests
-        >>> from transformers import AltCLIPProcessor, AltCLIPVisionModel
+        >>> from transformers import AutoProcessor, AltCLIPVisionModel
 
         >>> model = AltCLIPVisionModel.from_pretrained("BAAI/AltCLIP")
-        >>> processor = AltCLIPProcessor.from_pretrained("BAAI/AltCLIP")
+        >>> processor = AutoProcessor.from_pretrained("BAAI/AltCLIP")
 
         >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
         >>> image = Image.open(requests.get(url, stream=True).raw)
@@ -1422,10 +1421,10 @@ def forward(
         Examples:
 
         ```python
-        >>> from transformers import AltCLIPProcessor, AltCLIPTextModel
+        >>> from transformers import AutoProcessor, AltCLIPTextModel
 
         >>> model = AltCLIPTextModel.from_pretrained("BAAI/AltCLIP")
-        >>> processor = AltCLIPProcessor.from_pretrained("BAAI/AltCLIP")
+        >>> processor = AutoProcessor.from_pretrained("BAAI/AltCLIP")
 
         >>> texts = ["it's a cat", "it's a dog"]
 
@@ -1526,10 +1525,10 @@ def get_text_features(
         Examples:
 
         ```python
-        >>> from transformers import AltCLIPProcessor, AltCLIPModel
+        >>> from transformers import AutoProcessor, AltCLIPModel
 
         >>> model = AltCLIPModel.from_pretrained("BAAI/AltCLIP")
-        >>> processor = AltCLIPProcessor.from_pretrained("BAAI/AltCLIP")
+        >>> processor = AutoProcessor.from_pretrained("BAAI/AltCLIP")
         >>> inputs = processor(text=["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")
         >>> text_features = model.get_text_features(**inputs)
         ```"""
@@ -1572,10 +1571,10 @@ def get_image_features(
         ```python
         >>> from PIL import Image
         >>> import requests
-        >>> from transformers import AltCLIPProcessor, AltCLIPModel
+        >>> from transformers import AutoProcessor, AltCLIPModel
 
         >>> model = AltCLIPModel.from_pretrained("BAAI/AltCLIP")
-        >>> processor = AltCLIPProcessor.from_pretrained("BAAI/AltCLIP")
+        >>> processor = AutoProcessor.from_pretrained("BAAI/AltCLIP")
         >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
         >>> image = Image.open(requests.get(url, stream=True).raw)
         >>> inputs = processor(images=image, return_tensors="pt")
@@ -1622,10 +1621,10 @@ def forward(
         ```python
         >>> from PIL import Image
         >>> import requests
-        >>> from transformers import AltCLIPProcessor, AltCLIPModel
+        >>> from transformers import AutoProcessor, AltCLIPModel
 
         >>> model = AltCLIPModel.from_pretrained("BAAI/AltCLIP")
-        >>> processor = AltCLIPProcessor.from_pretrained("BAAI/AltCLIP")
+        >>> processor = AutoProcessor.from_pretrained("BAAI/AltCLIP")
         >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
         >>> image = Image.open(requests.get(url, stream=True).raw)
         >>> inputs = processor(