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amyeroberts merged 2 commits into from
Jan 20, 2023
Merged

Update modeling doc strings FE -> IP #21106

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ea28fb3
Update docs examples FE -> IP
amyeroberts Jan 13, 2023
3ba8ed1
Remove _IMAGE_PROCESSOR_FOR_DOC
amyeroberts Jan 19, 2023
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4 changes: 2 additions & 2 deletions src/transformers/models/altclip/modeling_altclip.py
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Expand Up @@ -98,7 +98,7 @@
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
[`CLIPFeatureExtractor`]. See [`CLIPFeatureExtractor.__call__`] for details.
[`CLIPImageProcessor`]. 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.
Expand Down Expand Up @@ -133,7 +133,7 @@
[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
[`CLIPFeatureExtractor`]. See [`CLIPFeatureExtractor.__call__`] for details.
[`CLIPImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details.
return_loss (`bool`, *optional*):
Whether or not to return the contrastive loss.
output_attentions (`bool`, *optional*):
Expand Down
6 changes: 3 additions & 3 deletions src/transformers/models/beit/modeling_beit.py
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Expand Up @@ -49,7 +49,7 @@

# General docstring
_CONFIG_FOR_DOC = "BeitConfig"
_FEAT_EXTRACTOR_FOR_DOC = "BeitImageProcessor"
_IMAGE_PROCESSOR_FOR_DOC = "BeitImageProcessor"
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# Base docstring
_CHECKPOINT_FOR_DOC = "microsoft/beit-base-patch16-224-pt22k"
Expand Down Expand Up @@ -646,7 +646,7 @@ class PreTrainedModel

@add_start_docstrings_to_model_forward(BEIT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
processor_class=_IMAGE_PROCESSOR_FOR_DOC,
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checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BeitModelOutputWithPooling,
config_class=_CONFIG_FOR_DOC,
Expand Down Expand Up @@ -844,7 +844,7 @@ def __init__(self, config: BeitConfig) -> None:

@add_start_docstrings_to_model_forward(BEIT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
processor_class=_IMAGE_PROCESSOR_FOR_DOC,
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checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=ImageClassifierOutput,
config_class=_CONFIG_FOR_DOC,
Expand Down
10 changes: 5 additions & 5 deletions src/transformers/models/bit/modeling_bit.py
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Expand Up @@ -46,7 +46,7 @@

# General docstring
_CONFIG_FOR_DOC = "BitConfig"
_FEAT_EXTRACTOR_FOR_DOC = "AutoFeatureExtractor"
_IMAGE_PROCESSOR_FOR_DOC = "AutoImageProcessor"
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# Base docstring
_CHECKPOINT_FOR_DOC = "google/bit-50"
Expand Down Expand Up @@ -688,8 +688,8 @@ def _set_gradient_checkpointing(self, module, value=False):
BIT_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoFeatureExtractor`]. See
[`AutoFeatureExtractor.__call__`] for details.
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`AutoImageProcessor.__call__`] for details.

output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
Expand Down Expand Up @@ -723,7 +723,7 @@ def __init__(self, config):

@add_start_docstrings_to_model_forward(BIT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
processor_class=_IMAGE_PROCESSOR_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPoolingAndNoAttention,
config_class=_CONFIG_FOR_DOC,
Expand Down Expand Up @@ -782,7 +782,7 @@ def __init__(self, config):

@add_start_docstrings_to_model_forward(BIT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
processor_class=_IMAGE_PROCESSOR_FOR_DOC,
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=ImageClassifierOutputWithNoAttention,
config_class=_CONFIG_FOR_DOC,
Expand Down
4 changes: 2 additions & 2 deletions src/transformers/models/chinese_clip/modeling_chinese_clip.py
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Expand Up @@ -810,7 +810,7 @@ def _set_gradient_checkpointing(self, module, value=False):
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
[`ChineseCLIPFeatureExtractor`]. See [`ChineseCLIPFeatureExtractor.__call__`] for details.
[`ChineseCLIPImageProcessor`]. See [`ChineseCLIPImageProcessor.__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.
Expand Down Expand Up @@ -853,7 +853,7 @@ def _set_gradient_checkpointing(self, module, value=False):
[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
[`ChineseCLIPFeatureExtractor`]. See [`ChineseCLIPFeatureExtractor.__call__`] for details.
[`ChineseCLIPImageProcessor`]. See [`ChineseCLIPImageProcessor.__call__`] for details.
return_loss (`bool`, *optional*):
Whether or not to return the contrastive loss.
output_attentions (`bool`, *optional*):
Expand Down
4 changes: 2 additions & 2 deletions src/transformers/models/clip/modeling_clip.py
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Expand Up @@ -521,7 +521,7 @@ def _set_gradient_checkpointing(self, module, value=False):
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
[`CLIPFeatureExtractor`]. See [`CLIPFeatureExtractor.__call__`] for details.
[`CLIPImageProcessor`]. 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.
Expand Down Expand Up @@ -556,7 +556,7 @@ def _set_gradient_checkpointing(self, module, value=False):
[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
[`CLIPFeatureExtractor`]. See [`CLIPFeatureExtractor.__call__`] for details.
[`CLIPImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details.
return_loss (`bool`, *optional*):
Whether or not to return the contrastive loss.
output_attentions (`bool`, *optional*):
Expand Down
6 changes: 3 additions & 3 deletions src/transformers/models/clip/modeling_flax_clip.py
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Expand Up @@ -108,7 +108,7 @@
Args:
pixel_values (`numpy.ndarray` 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
[`CLIPFeatureExtractor`]. See [`CLIPFeatureExtractor.__call__`] for details.
[`CLIPImageProcessor`]. 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.
Expand Down Expand Up @@ -143,7 +143,7 @@
[What are position IDs?](../glossary#position-ids)
pixel_values (`numpy.ndarray` 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
[`CLIPFeatureExtractor`]. See [`CLIPFeatureExtractor.__call__`] for details.
[`CLIPImageProcessor`]. 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.
Expand Down Expand Up @@ -893,7 +893,7 @@ def get_image_features(
Args:
pixel_values (`numpy.ndarray` 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 [`CLIPFeatureExtractor`]. See [`CLIPFeatureExtractor.__call__`] for details.
using [`CLIPImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details.

Returns:
image_features (`jnp.ndarray` of shape `(batch_size, output_dim`): The image embeddings obtained by
Expand Down
8 changes: 4 additions & 4 deletions src/transformers/models/clip/modeling_tf_clip.py
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Expand Up @@ -993,8 +993,8 @@ class TFCLIPPreTrainedModel(TFPreTrainedModel):
CLIP_VISION_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`CLIPFeatureExtractor`]. See
[`CLIPFeatureExtractor.__call__`] for details. output_attentions (`bool`, *optional*): Whether or not to
Pixel values. Pixel values can be obtained using [`CLIPImageProcessor`]. 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. This argument can be used only in eager mode, in graph mode the value in the config will be used
instead.
Expand All @@ -1020,8 +1020,8 @@ class TFCLIPPreTrainedModel(TFPreTrainedModel):

[What are input IDs?](../glossary#input-ids)
pixel_values (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` `Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`CLIPFeatureExtractor`]. See
[`CLIPFeatureExtractor.__call__`] for details.
Pixel values. Pixel values can be obtained using [`CLIPImageProcessor`]. See
[`CLIPImageProcessor.__call__`] for details.
attention_mask (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

Expand Down
4 changes: 2 additions & 2 deletions src/transformers/models/clipseg/modeling_clipseg.py
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Expand Up @@ -530,7 +530,7 @@ def _set_gradient_checkpointing(self, module, value=False):
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
[`CLIPFeatureExtractor`]. See [`CLIPFeatureExtractor.__call__`] for details.
[`CLIPImageProcessor`]. 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.
Expand Down Expand Up @@ -565,7 +565,7 @@ def _set_gradient_checkpointing(self, module, value=False):
[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
[`CLIPFeatureExtractor`]. See [`CLIPFeatureExtractor.__call__`] for details.
[`CLIPImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details.
return_loss (`bool`, *optional*):
Whether or not to return the contrastive loss.
output_attentions (`bool`, *optional*):
Expand Down
6 changes: 3 additions & 3 deletions src/transformers/models/convnext/modeling_convnext.py
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Expand Up @@ -44,7 +44,7 @@

# General docstring
_CONFIG_FOR_DOC = "ConvNextConfig"
_FEAT_EXTRACTOR_FOR_DOC = "ConvNextImageProcessor"
_IMAGE_PROCESSOR_FOR_DOC = "ConvNextImageProcessor"
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# Base docstring
_CHECKPOINT_FOR_DOC = "facebook/convnext-tiny-224"
Expand Down Expand Up @@ -346,7 +346,7 @@ def __init__(self, config):

@add_start_docstrings_to_model_forward(CONVNEXT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
processor_class=_IMAGE_PROCESSOR_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPoolingAndNoAttention,
config_class=_CONFIG_FOR_DOC,
Expand Down Expand Up @@ -414,7 +414,7 @@ def __init__(self, config):

@add_start_docstrings_to_model_forward(CONVNEXT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
processor_class=_IMAGE_PROCESSOR_FOR_DOC,
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=ImageClassifierOutputWithNoAttention,
config_class=_CONFIG_FOR_DOC,
Expand Down
6 changes: 3 additions & 3 deletions src/transformers/models/cvt/modeling_cvt.py
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Expand Up @@ -35,7 +35,7 @@

# General docstring
_CONFIG_FOR_DOC = "CvtConfig"
_FEAT_EXTRACTOR_FOR_DOC = "AutoImageProcessor"
_IMAGE_PROCESSOR_FOR_DOC = "AutoImageProcessor"
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# Base docstring
_CHECKPOINT_FOR_DOC = "microsoft/cvt-13"
Expand Down Expand Up @@ -604,7 +604,7 @@ class PreTrainedModel

@add_start_docstrings_to_model_forward(CVT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
processor_class=_IMAGE_PROCESSOR_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithCLSToken,
config_class=_CONFIG_FOR_DOC,
Expand Down Expand Up @@ -667,7 +667,7 @@ def __init__(self, config):

@add_start_docstrings_to_model_forward(CVT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
processor_class=_IMAGE_PROCESSOR_FOR_DOC,
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=ImageClassifierOutputWithNoAttention,
config_class=_CONFIG_FOR_DOC,
Expand Down
6 changes: 3 additions & 3 deletions src/transformers/models/data2vec/modeling_data2vec_vision.py
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Expand Up @@ -48,7 +48,7 @@

# General docstring
_CONFIG_FOR_DOC = "Data2VecVisionConfig"
_FEAT_EXTRACTOR_FOR_DOC = "BeitImageProcessor"
_IMAGE_PROCESSOR_FOR_DOC = "BeitImageProcessor"
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# Base docstring
_CHECKPOINT_FOR_DOC = "facebook/data2vec-vision-base"
Expand Down Expand Up @@ -660,7 +660,7 @@ class PreTrainedModel

@add_start_docstrings_to_model_forward(DATA2VEC_VISION_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
processor_class=_IMAGE_PROCESSOR_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=Data2VecVisionModelOutputWithPooling,
config_class=_CONFIG_FOR_DOC,
Expand Down Expand Up @@ -760,7 +760,7 @@ def __init__(self, config: Data2VecVisionConfig) -> None:

@add_start_docstrings_to_model_forward(DATA2VEC_VISION_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
processor_class=_IMAGE_PROCESSOR_FOR_DOC,
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=ImageClassifierOutput,
config_class=_CONFIG_FOR_DOC,
Expand Down
6 changes: 3 additions & 3 deletions src/transformers/models/data2vec/modeling_tf_data2vec_vision.py
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Expand Up @@ -53,7 +53,7 @@

# General docstring
_CONFIG_FOR_DOC = "Data2VecVisionConfig"
_FEAT_EXTRACTOR_FOR_DOC = "BeitImageProcessor"
_IMAGE_PROCESSOR_FOR_DOC = "BeitImageProcessor"
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# Base docstring
_CHECKPOINT_FOR_DOC = "facebook/data2vec-vision-base"
Expand Down Expand Up @@ -894,7 +894,7 @@ def get_input_embeddings(self):
@unpack_inputs
@add_start_docstrings_to_model_forward(DATA2VEC_VISION_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
processor_class=_IMAGE_PROCESSOR_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFData2VecVisionModelOutputWithPooling,
config_class=_CONFIG_FOR_DOC,
Expand Down Expand Up @@ -960,7 +960,7 @@ def __init__(self, config: Data2VecVisionConfig, *inputs, **kwargs):
@unpack_inputs
@add_start_docstrings_to_model_forward(DATA2VEC_VISION_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
processor_class=_IMAGE_PROCESSOR_FOR_DOC,
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=TFSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
Expand Down
6 changes: 3 additions & 3 deletions src/transformers/models/deit/modeling_deit.py
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Expand Up @@ -44,7 +44,7 @@

# General docstring
_CONFIG_FOR_DOC = "DeiTConfig"
_FEAT_EXTRACTOR_FOR_DOC = "DeiTImageProcessor"
_IMAGE_PROCESSOR_FOR_DOC = "DeiTImageProcessor"
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# Base docstring
_CHECKPOINT_FOR_DOC = "facebook/deit-base-distilled-patch16-224"
Expand Down Expand Up @@ -483,7 +483,7 @@ class PreTrainedModel

@add_start_docstrings_to_model_forward(DEIT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
processor_class=_IMAGE_PROCESSOR_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPooling,
config_class=_CONFIG_FOR_DOC,
Expand Down Expand Up @@ -854,7 +854,7 @@ def __init__(self, config: DeiTConfig) -> None:

@add_start_docstrings_to_model_forward(DEIT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
processor_class=_IMAGE_PROCESSOR_FOR_DOC,
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=DeiTForImageClassificationWithTeacherOutput,
config_class=_CONFIG_FOR_DOC,
Expand Down
6 changes: 3 additions & 3 deletions src/transformers/models/deit/modeling_tf_deit.py
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Expand Up @@ -52,7 +52,7 @@

# General docstring
_CONFIG_FOR_DOC = "DeiTConfig"
_FEAT_EXTRACTOR_FOR_DOC = "DeiTImageProcessor"
_IMAGE_PROCESSOR_FOR_DOC = "DeiTImageProcessor"
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# Base docstring
_CHECKPOINT_FOR_DOC = "facebook/deit-base-distilled-patch16-224"
Expand Down Expand Up @@ -651,7 +651,7 @@ def __init__(
@unpack_inputs
@add_start_docstrings_to_model_forward(DEIT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
processor_class=_IMAGE_PROCESSOR_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFBaseModelOutputWithPooling,
config_class=_CONFIG_FOR_DOC,
Expand Down Expand Up @@ -1009,7 +1009,7 @@ def __init__(self, config: DeiTConfig) -> None:
@unpack_inputs
@add_start_docstrings_to_model_forward(DEIT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
processor_class=_IMAGE_PROCESSOR_FOR_DOC,
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=TFDeiTForImageClassificationWithTeacherOutput,
config_class=_CONFIG_FOR_DOC,
Expand Down
6 changes: 3 additions & 3 deletions src/transformers/models/dinat/modeling_dinat.py
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Expand Up @@ -57,7 +57,7 @@ def natten2dav(*args, **kwargs):

# General docstring
_CONFIG_FOR_DOC = "DinatConfig"
_FEAT_EXTRACTOR_FOR_DOC = "AutoImageProcessor"
_IMAGE_PROCESSOR_FOR_DOC = "AutoImageProcessor"
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# Base docstring
_CHECKPOINT_FOR_DOC = "shi-labs/dinat-mini-in1k-224"
Expand Down Expand Up @@ -730,7 +730,7 @@ class PreTrainedModel

@add_start_docstrings_to_model_forward(DINAT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
processor_class=_IMAGE_PROCESSOR_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=DinatModelOutput,
config_class=_CONFIG_FOR_DOC,
Expand Down Expand Up @@ -810,7 +810,7 @@ def __init__(self, config):

@add_start_docstrings_to_model_forward(DINAT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
processor_class=_FEAT_EXTRACTOR_FOR_DOC,
processor_class=_IMAGE_PROCESSOR_FOR_DOC,
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=DinatImageClassifierOutput,
config_class=_CONFIG_FOR_DOC,
Expand Down
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