[Docs] More general docstrings (#14028)

* up * finish * up * up * finish

[Docs] More general docstrings (#14028)
* up * finish * up * up * finish
f5af8736 · Patrick von Platen · GitHub · 47489a69 · f5af8736 · f5af8736
Unverified Commit f5af8736 authored Oct 16, 2021 by Patrick von Platen Committed by GitHub Oct 16, 2021
14 changed files
--- a/src/transformers/models/roformer/modeling_roformer.py
+++ b/src/transformers/models/roformer/modeling_roformer.py
@@ -839,7 +839,7 @@ class RoFormerModel(RoFormerPreTrainedModel):

    @add_start_docstrings_to_model_forward(ROFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=BaseModelOutputWithPastAndCrossAttentions,
        config_class=_CONFIG_FOR_DOC,
@@ -987,7 +987,7 @@ class RoFormerForMaskedLM(RoFormerPreTrainedModel):

    @add_start_docstrings_to_model_forward(ROFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=MaskedLMOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -1246,7 +1246,7 @@ class RoFormerForSequenceClassification(RoFormerPreTrainedModel):

    @add_start_docstrings_to_model_forward(ROFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=SequenceClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -1328,7 +1328,7 @@ class RoFormerForMultipleChoice(RoFormerPreTrainedModel):
        ROFORMER_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
    )
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=MultipleChoiceModelOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -1418,7 +1418,7 @@ class RoFormerForTokenClassification(RoFormerPreTrainedModel):

    @add_start_docstrings_to_model_forward(ROFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TokenClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -1505,7 +1505,7 @@ class RoFormerForQuestionAnswering(RoFormerPreTrainedModel):

    @add_start_docstrings_to_model_forward(ROFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=QuestionAnsweringModelOutput,
        config_class=_CONFIG_FOR_DOC,

--- a/src/transformers/models/roformer/modeling_tf_roformer.py
+++ b/src/transformers/models/roformer/modeling_tf_roformer.py
@@ -814,7 +814,7 @@ class TFRoFormerModel(TFRoFormerPreTrainedModel):

    @add_start_docstrings_to_model_forward(ROFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFBaseModelOutputWithPooling,
        config_class=_CONFIG_FOR_DOC,
@@ -886,7 +886,7 @@ class TFRoFormerForMaskedLM(TFRoFormerPreTrainedModel, TFMaskedLanguageModelingL

    @add_start_docstrings_to_model_forward(ROFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFMaskedLMOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -978,7 +978,7 @@ class TFRoFormerForCausalLM(TFRoFormerPreTrainedModel, TFCausalLanguageModelingL
        return self.mlm.predictions

    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFCausalLMOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -1103,7 +1103,7 @@ class TFRoFormerForSequenceClassification(TFRoFormerPreTrainedModel, TFSequenceC

    @add_start_docstrings_to_model_forward(ROFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFSequenceClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -1208,7 +1208,7 @@ class TFRoFormerForMultipleChoice(TFRoFormerPreTrainedModel, TFMultipleChoiceLos
        ROFORMER_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
    )
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFMultipleChoiceModelOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -1344,7 +1344,7 @@ class TFRoFormerForTokenClassification(TFRoFormerPreTrainedModel, TFTokenClassif

    @add_start_docstrings_to_model_forward(ROFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFTokenClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -1437,7 +1437,7 @@ class TFRoFormerForQuestionAnswering(TFRoFormerPreTrainedModel, TFQuestionAnswer

    @add_start_docstrings_to_model_forward(ROFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFQuestionAnsweringModelOutput,
        config_class=_CONFIG_FOR_DOC,

--- a/src/transformers/models/speech_to_text/modeling_speech_to_text.py
+++ b/src/transformers/models/speech_to_text/modeling_speech_to_text.py
@@ -1138,7 +1138,7 @@ class Speech2TextModel(Speech2TextPreTrainedModel):

    @add_start_docstrings_to_model_forward(SPEECH_TO_TEXT_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=Seq2SeqModelOutput,
        config_class=_CONFIG_FOR_DOC,

--- a/src/transformers/models/splinter/modeling_splinter.py
+++ b/src/transformers/models/splinter/modeling_splinter.py
@@ -635,7 +635,7 @@ class SplinterModel(SplinterPreTrainedModel):

    @add_start_docstrings_to_model_forward(SPLINTER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=BaseModelOutputWithPastAndCrossAttentions,
        config_class=_CONFIG_FOR_DOC,
@@ -836,7 +836,7 @@ class SplinterForQuestionAnswering(SplinterPreTrainedModel):

    @add_start_docstrings_to_model_forward(SPLINTER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=QuestionAnsweringModelOutput,
        config_class=_CONFIG_FOR_DOC,

--- a/src/transformers/models/squeezebert/modeling_squeezebert.py
+++ b/src/transformers/models/squeezebert/modeling_squeezebert.py
@@ -571,7 +571,7 @@ class SqueezeBertModel(SqueezeBertPreTrainedModel):

    @add_start_docstrings_to_model_forward(SQUEEZEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=BaseModelOutputWithPooling,
        config_class=_CONFIG_FOR_DOC,
@@ -664,7 +664,7 @@ class SqueezeBertForMaskedLM(SqueezeBertPreTrainedModel):

    @add_start_docstrings_to_model_forward(SQUEEZEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=MaskedLMOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -743,7 +743,7 @@ class SqueezeBertForSequenceClassification(SqueezeBertPreTrainedModel):

    @add_start_docstrings_to_model_forward(SQUEEZEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=SequenceClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -842,7 +842,7 @@ class SqueezeBertForMultipleChoice(SqueezeBertPreTrainedModel):
        SQUEEZEBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
    )
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=MultipleChoiceModelOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -934,7 +934,7 @@ class SqueezeBertForTokenClassification(SqueezeBertPreTrainedModel):

    @add_start_docstrings_to_model_forward(SQUEEZEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TokenClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -1021,7 +1021,7 @@ class SqueezeBertForQuestionAnswering(SqueezeBertPreTrainedModel):

    @add_start_docstrings_to_model_forward(SQUEEZEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=QuestionAnsweringModelOutput,
        config_class=_CONFIG_FOR_DOC,

--- a/src/transformers/models/transfo_xl/modeling_tf_transfo_xl.py
+++ b/src/transformers/models/transfo_xl/modeling_tf_transfo_xl.py
@@ -883,7 +883,7 @@ class TFTransfoXLModel(TFTransfoXLPreTrainedModel):

    @add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFTransfoXLModelOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -975,7 +975,7 @@ class TFTransfoXLLMHeadModel(TFTransfoXLPreTrainedModel):

    @add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFTransfoXLLMHeadModelOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -1091,7 +1091,7 @@ class TFTransfoXLForSequenceClassification(TFTransfoXLPreTrainedModel, TFSequenc

    @add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFTransfoXLSequenceClassifierOutputWithPast,
        config_class=_CONFIG_FOR_DOC,

--- a/src/transformers/models/transfo_xl/modeling_transfo_xl.py
+++ b/src/transformers/models/transfo_xl/modeling_transfo_xl.py
@@ -871,7 +871,7 @@ class TransfoXLModel(TransfoXLPreTrainedModel):

    @add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TransfoXLModelOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -1052,7 +1052,7 @@ class TransfoXLLMHeadModel(TransfoXLPreTrainedModel):

    @add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TransfoXLLMHeadModelOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -1174,7 +1174,7 @@ class TransfoXLForSequenceClassification(TransfoXLPreTrainedModel):

    @add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TransfoXLSequenceClassifierOutputWithPast,
        config_class=_CONFIG_FOR_DOC,

--- a/src/transformers/models/wav2vec2/modeling_wav2vec2.py
+++ b/src/transformers/models/wav2vec2/modeling_wav2vec2.py
@@ -29,6 +29,7 @@ from ...activations import ACT2FN
 from ...deepspeed import is_deepspeed_zero3_enabled
 from ...file_utils import (
    ModelOutput,
+    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    replace_return_docstrings,
@@ -43,6 +44,7 @@ logger = logging.get_logger(__name__)

 _CONFIG_FOR_DOC = "Wav2Vec2Config"
 _CHECKPOINT_FOR_DOC = "facebook/wav2vec2-base-960h"
+_PROCESSOR_FOR_DOC = "Wav2Vec2Processor"

 WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "facebook/wav2vec2-base-960h",
@@ -1118,7 +1120,13 @@ class Wav2Vec2Model(Wav2Vec2PreTrainedModel):
        return hidden_states

    @add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC)
+    @add_code_sample_docstrings(
+        processor_class=_PROCESSOR_FOR_DOC,
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=Wav2Vec2BaseModelOutput,
+        config_class=_CONFIG_FOR_DOC,
+        modality="audio",
+    )
    def forward(
        self,
        input_values,
@@ -1128,30 +1136,6 @@ class Wav2Vec2Model(Wav2Vec2PreTrainedModel):
        output_hidden_states=None,
        return_dict=None,
    ):
-        """
-
-        Returns:
-
-        Example::
-
-            >>> from transformers import Wav2Vec2Processor, Wav2Vec2Model
-            >>> from datasets import load_dataset
-            >>> import soundfile as sf
-
-            >>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
-            >>> model = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")
-
-            >>> def map_to_array(batch):
-            >>>     speech, _ = sf.read(batch["file"])
-            >>>     batch["speech"] = speech
-            >>>     return batch
-
-            >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
-            >>> ds = ds.map(map_to_array)
-
-            >>> input_values = processor(ds["speech"][0], return_tensors="pt").input_values  # Batch size 1
-            >>> hidden_states = model(input_values).last_hidden_state
-        """
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
@@ -1502,7 +1486,12 @@ class Wav2Vec2ForCTC(Wav2Vec2PreTrainedModel):
        self.wav2vec2.feature_extractor._freeze_parameters()

    @add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=CausalLMOutput, config_class=_CONFIG_FOR_DOC)
+    @add_code_sample_docstrings(
+        processor_class=_PROCESSOR_FOR_DOC,
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=CausalLMOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
    def forward(
        self,
        input_values,
@@ -1518,41 +1507,6 @@ class Wav2Vec2ForCTC(Wav2Vec2PreTrainedModel):
            the sequence length of the output logits. Indices are selected in ``[-100, 0, ..., config.vocab_size -
            1]``. All labels set to ``-100`` are ignored (masked), the loss is only computed for labels in ``[0, ...,
            config.vocab_size - 1]``.
-
-        Returns:
-
-        Example::
-
-            >>> import torch
-            >>> from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
-            >>> from datasets import load_dataset
-            >>> import soundfile as sf
-
-            >>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
-            >>> model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
-
-            >>> def map_to_array(batch):
-            >>>     speech, _ = sf.read(batch["file"])
-            >>>     batch["speech"] = speech
-            >>>     return batch
-
-            >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
-            >>> ds = ds.map(map_to_array)
-
-            >>> input_values = processor(ds["speech"][0], return_tensors="pt").input_values  # Batch size 1
-            >>> logits = model(input_values).logits
-            >>> predicted_ids = torch.argmax(logits, dim=-1)
-
-            >>> transcription = processor.decode(predicted_ids[0])
-
-            >>> # compute loss
-            >>> target_transcription = "A MAN SAID TO THE UNIVERSE SIR I EXIST"
-
-            >>> # wrap processor as target processor to encode labels
-            >>> with processor.as_target_processor():
-            >>>     labels = processor(target_transcription, return_tensors="pt").input_ids
-
-            >>> loss = model(input_values, labels=labels).loss
        """

        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
@@ -1647,7 +1601,13 @@ class Wav2Vec2ForSequenceClassification(Wav2Vec2PreTrainedModel):
            param.requires_grad = False

    @add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
+    @add_code_sample_docstrings(
+        processor_class="Wav2Vec2FeatureExtractor",
+        checkpoint="superb/wav2vec2-base-superb-ks",
+        output_type=SequenceClassifierOutput,
+        config_class=_CONFIG_FOR_DOC,
+        modality="audio",
+    )
    def forward(
        self,
        input_values,
@@ -1662,29 +1622,6 @@ class Wav2Vec2ForSequenceClassification(Wav2Vec2PreTrainedModel):
            Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
            config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
            If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-
-        Returns:
-
-        Example::
-
-            >>> import torch
-            >>> from transformers import Wav2Vec2FeatureExtractor, Wav2Vec2ForSequenceClassification
-            >>> from datasets import load_dataset
-
-            >>> processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/wav2vec2-base-superb-ks")
-            >>> model = Wav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-base-superb-ks")
-
-            >>> ds = load_dataset("anton-l/superb_dummy", "ks", split="test")
-
-            >>> input_values = processor(ds["speech"][4], return_tensors="pt").input_values  # Batch size 1
-            >>> logits = model(input_values).logits
-            >>> predicted_class_ids = torch.argmax(logits, dim=-1)
-
-            >>> # compute loss
-            >>> target_label = "down"
-            >>> labels = torch.tensor([model.config.label2id[target_label]])
-
-            >>> loss = model(input_values, labels=labels).loss
        """

        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

--- a/src/transformers/models/xlm/modeling_tf_xlm.py
+++ b/src/transformers/models/xlm/modeling_tf_xlm.py
@@ -703,7 +703,7 @@ class TFXLMModel(TFXLMPreTrainedModel):

    @add_start_docstrings_to_model_forward(XLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFBaseModelOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -856,7 +856,7 @@ class TFXLMWithLMHeadModel(TFXLMPreTrainedModel):

    @add_start_docstrings_to_model_forward(XLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFXLMWithLMHeadModelOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -946,7 +946,7 @@ class TFXLMForSequenceClassification(TFXLMPreTrainedModel, TFSequenceClassificat

    @add_start_docstrings_to_model_forward(XLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFSequenceClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -1072,7 +1072,7 @@ class TFXLMForMultipleChoice(TFXLMPreTrainedModel, TFMultipleChoiceLoss):

    @add_start_docstrings_to_model_forward(XLM_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFMultipleChoiceModelOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -1222,7 +1222,7 @@ class TFXLMForTokenClassification(TFXLMPreTrainedModel, TFTokenClassificationLos

    @add_start_docstrings_to_model_forward(XLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFTokenClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -1327,7 +1327,7 @@ class TFXLMForQuestionAnsweringSimple(TFXLMPreTrainedModel, TFQuestionAnsweringL

    @add_start_docstrings_to_model_forward(XLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFQuestionAnsweringModelOutput,
        config_class=_CONFIG_FOR_DOC,

--- a/src/transformers/models/xlm/modeling_xlm.py
+++ b/src/transformers/models/xlm/modeling_xlm.py
@@ -488,7 +488,7 @@ class XLMModel(XLMPreTrainedModel):

    @add_start_docstrings_to_model_forward(XLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=BaseModelOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -710,7 +710,7 @@ class XLMWithLMHeadModel(XLMPreTrainedModel):

    @add_start_docstrings_to_model_forward(XLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=MaskedLMOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -789,7 +789,7 @@ class XLMForSequenceClassification(XLMPreTrainedModel):

    @add_start_docstrings_to_model_forward(XLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=SequenceClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -889,7 +889,7 @@ class XLMForQuestionAnsweringSimple(XLMPreTrainedModel):

    @add_start_docstrings_to_model_forward(XLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=QuestionAnsweringModelOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -1112,7 +1112,7 @@ class XLMForTokenClassification(XLMPreTrainedModel):

    @add_start_docstrings_to_model_forward(XLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TokenClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -1205,7 +1205,7 @@ class XLMForMultipleChoice(XLMPreTrainedModel):

    @add_start_docstrings_to_model_forward(XLM_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=MultipleChoiceModelOutput,
        config_class=_CONFIG_FOR_DOC,

--- a/src/transformers/models/xlnet/modeling_tf_xlnet.py
+++ b/src/transformers/models/xlnet/modeling_tf_xlnet.py
@@ -1160,7 +1160,7 @@ class TFXLNetModel(TFXLNetPreTrainedModel):

    @add_start_docstrings_to_model_forward(XLNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFXLNetModelOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -1429,7 +1429,7 @@ class TFXLNetForSequenceClassification(TFXLNetPreTrainedModel, TFSequenceClassif

    @add_start_docstrings_to_model_forward(XLNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFXLNetForSequenceClassificationOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -1555,7 +1555,7 @@ class TFXLNetForMultipleChoice(TFXLNetPreTrainedModel, TFMultipleChoiceLoss):

    @add_start_docstrings_to_model_forward(XLNET_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFXLNetForMultipleChoiceOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -1704,7 +1704,7 @@ class TFXLNetForTokenClassification(TFXLNetPreTrainedModel, TFTokenClassificatio

    @add_start_docstrings_to_model_forward(XLNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFXLNetForTokenClassificationOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -1811,7 +1811,7 @@ class TFXLNetForQuestionAnsweringSimple(TFXLNetPreTrainedModel, TFQuestionAnswer

    @add_start_docstrings_to_model_forward(XLNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFXLNetForQuestionAnsweringSimpleOutput,
        config_class=_CONFIG_FOR_DOC,

--- a/src/transformers/models/xlnet/modeling_xlnet.py
+++ b/src/transformers/models/xlnet/modeling_xlnet.py
@@ -1069,7 +1069,7 @@ class XLNetModel(XLNetPreTrainedModel):

    @add_start_docstrings_to_model_forward(XLNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=XLNetModelOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -1497,7 +1497,7 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):

    @add_start_docstrings_to_model_forward(XLNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=XLNetForSequenceClassificationOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -1604,7 +1604,7 @@ class XLNetForTokenClassification(XLNetPreTrainedModel):

    @add_start_docstrings_to_model_forward(XLNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=XLNetForTokenClassificationOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -1701,7 +1701,7 @@ class XLNetForMultipleChoice(XLNetPreTrainedModel):

    @add_start_docstrings_to_model_forward(XLNET_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=XLNetForMultipleChoiceOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -1804,7 +1804,7 @@ class XLNetForQuestionAnsweringSimple(XLNetPreTrainedModel):

    @add_start_docstrings_to_model_forward(XLNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=XLNetForQuestionAnsweringSimpleOutput,
        config_class=_CONFIG_FOR_DOC,

--- a/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_tf_{{cookiecutter.lowercase_modelname}}.py
+++ b/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_tf_{{cookiecutter.lowercase_modelname}}.py
@@ -945,7 +945,7 @@ class TF{{cookiecutter.camelcase_modelname}}Model(TF{{cookiecutter.camelcase_mod

    @add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFBaseModelOutputWithPoolingAndCrossAttentions,
        config_class=_CONFIG_FOR_DOC,
@@ -1068,7 +1068,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForMaskedLM(TF{{cookiecutter.camelca

    @add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFMaskedLMOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -1177,7 +1177,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForCausalLM(TF{{cookiecutter.camelca
        }

    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFCausalLMOutputWithCrossAttentions,
        config_class=_CONFIG_FOR_DOC,
@@ -1344,7 +1344,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification(TF{{cookie

    @add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFSequenceClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -1450,7 +1450,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice(TF{{cookiecutter.c

    @add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFMultipleChoiceModelOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -1593,7 +1593,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForTokenClassification(TF{{cookiecut

    @add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFTokenClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -1689,7 +1689,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering(TF{{cookiecutte

    @add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFQuestionAnsweringModelOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -2941,7 +2941,7 @@ class TF{{cookiecutter.camelcase_modelname}}Model(TF{{cookiecutter.camelcase_mod

    @add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFSeq2SeqModelOutput,
        config_class=_CONFIG_FOR_DOC,

--- a/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_{{cookiecutter.lowercase_modelname}}.py
+++ b/templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_{{cookiecutter.lowercase_modelname}}.py
@@ -795,7 +795,7 @@ class {{cookiecutter.camelcase_modelname}}Model({{cookiecutter.camelcase_modelna

    @add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=BaseModelOutputWithPastAndCrossAttentions,
        config_class=_CONFIG_FOR_DOC,
@@ -953,7 +953,7 @@ class {{cookiecutter.camelcase_modelname}}ForMaskedLM({{cookiecutter.camelcase_m

    @add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=MaskedLMOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -1221,7 +1221,7 @@ class {{cookiecutter.camelcase_modelname}}ForSequenceClassification({{cookiecutt

    @add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=SequenceClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -1301,7 +1301,7 @@ class {{cookiecutter.camelcase_modelname}}ForMultipleChoice({{cookiecutter.camel

    @add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=MultipleChoiceModelOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -1391,7 +1391,7 @@ class {{cookiecutter.camelcase_modelname}}ForTokenClassification({{cookiecutter.

    @add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TokenClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -1478,7 +1478,7 @@ class {{cookiecutter.camelcase_modelname}}ForQuestionAnswering({{cookiecutter.ca

    @add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=QuestionAnsweringModelOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -2646,7 +2646,7 @@ class {{cookiecutter.camelcase_modelname}}Model({{cookiecutter.camelcase_modelna

    @add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=Seq2SeqModelOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -2921,7 +2921,7 @@ class {{cookiecutter.camelcase_modelname}}ForSequenceClassification({{cookiecutt

    @add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=Seq2SeqSequenceClassifierOutput,
        config_class=_CONFIG_FOR_DOC,
@@ -3022,7 +3022,7 @@ class {{cookiecutter.camelcase_modelname}}ForQuestionAnswering({{cookiecutter.ca

    @add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
+        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=Seq2SeqQuestionAnsweringModelOutput,
        config_class=_CONFIG_FOR_DOC,