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[docstring] Fix docstring for ChineseCLIP (#26880)

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* Remove ChineseCLIPImageProcessor, ChineseCLIPTextConfig, ChineseCLIPVisionConfig from check_docstrings

* Run fix_and_overwrite for ChineseCLIPImageProcessor, ChineseCLIPTextConfig, ChineseCLIPVisionConfig

* Replace <fill_type> and <fill_docstring> in configuration_chinese_clip.py, image_processing_chinese_clip.py with type and docstring values

---------

Co-authored-by: vignesh-raghunathan <vignesh_raghunathan@intuit.com>
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Sparty 2023-10-19 04:52:14 -04:00 committed by GitHub
parent 574a538455
commit 816c2237c1
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3 changed files with 13 additions and 7 deletions
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13
src/transformers/models/chinese_clip/configuration_chinese_clip.py
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@ -75,8 +75,13 @@ class ChineseCLIPTextConfig(PretrainedConfig):
The vocabulary size of the `token_type_ids` passed when calling [`ChineseCLIPModel`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
initializer_factor (`float`, *optional*, defaults to 1.0):
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
testing).
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
pad_token_id (`int`, *optional*, defaults to 0):
Padding token id.
position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
@ -177,10 +182,14 @@ class ChineseCLIPVisionConfig(PretrainedConfig):
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
projection_dim (`int`, *optional*, defaults to 512):
Dimentionality of text and vision projection layers.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 32):
@ -188,13 +197,13 @@ class ChineseCLIPVisionConfig(PretrainedConfig):
hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported.
layer_norm_eps (`float`, *optional*, defaults to 1e-5):
layer_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the layer normalization layers.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
initializer_factor (`float``, *optional*, defaults to 1):
initializer_factor (`float``, *optional*, defaults to 1.0):
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
testing).
Example:

4
src/transformers/models/chinese_clip/image_processing_chinese_clip.py
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@ -59,7 +59,7 @@ class ChineseCLIPImageProcessor(BaseImageProcessor):
Size of the image after resizing. The shortest edge of the image is resized to size["shortest_edge"], with
the longest edge resized to keep the input aspect ratio. Can be overridden by `size` in the `preprocess`
method.
resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
Resampling filter to use if resizing the image. Can be overridden by `resample` in the `preprocess` method.
do_center_crop (`bool`, *optional*, defaults to `True`):
Whether to center crop the image to the specified `crop_size`. Can be overridden by `do_center_crop` in the
@ -73,7 +73,7 @@ class ChineseCLIPImageProcessor(BaseImageProcessor):
rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
Scale factor to use if rescaling the image. Can be overridden by `rescale_factor` in the `preprocess`
method.
do_normalize:
do_normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the image. Can be overridden by `do_normalize` in the `preprocess` method.
image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
Mean to use if normalizing the image. This is a float or list of floats the length of the number of

3
utils/check_docstrings.py
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@ -121,10 +121,7 @@ OBJECTS_TO_IGNORE = [
"CamembertTokenizerFast",
"CanineModel",
"CanineTokenizer",
"ChineseCLIPImageProcessor",
"ChineseCLIPTextConfig",
"ChineseCLIPTextModel",
"ChineseCLIPVisionConfig",
"ClapTextConfig",
"CodeGenConfig",
"CodeGenTokenizer",