class_docstr: This is the configuration class to store the configuration of a [`ChineseCLIPModel`]. It is used to instantiate an
  ChineseCLIP model according to the specified arguments, defining the model architecture. Instantiating a
  configuration with the defaults will yield a similar configuration to that of the ChineseCLIP
  [OFA-Sys/chinese-clip-vit-base-patch16](https:
      //huggingface.co/OFA-Sys/chinese-clip-vit-base-patch16) architecture.
  
  Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
  documentation from [`PretrainedConfig`] for more information.
  
  
  Args:
      hidden_size (`int`, *optional*, defaults to 768):
          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):
          The size (resolution) of each patch.
      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-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 1.0):
          A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
          testing).
  Example:
  ```python
  >>> from transformers import ChineseCLIPVisionConfig, ChineseCLIPVisionModel
  
  >>> # Initializing a ChineseCLIPVisionConfig with OFA-Sys/chinese-clip-vit-base-patch16 style configuration
  >>> configuration = ChineseCLIPVisionConfig()
  
  >>> # Initializing a ChineseCLIPVisionModel (with random weights) from the OFA-Sys/chinese-clip-vit-base-patch16 style configuration
  >>> model = ChineseCLIPVisionModel(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
code: def __init__(
      self,
      hidden_size=768,
      intermediate_size=3072,
      projection_dim=512,
      num_hidden_layers=12,
      num_attention_heads=12,
      num_channels=3,
      image_size=224,
      patch_size=32,
      hidden_act="quick_gelu",
      layer_norm_eps=1e-5,
      attention_dropout=0.0,
      initializer_range=0.02,
      initializer_factor=1.0,
      **kwargs,
  ):
      super().__init__(**kwargs)
  
      self.hidden_size = hidden_size
      self.intermediate_size = intermediate_size
      self.projection_dim = projection_dim
      self.num_hidden_layers = num_hidden_layers
      self.num_attention_heads = num_attention_heads
      self.num_channels = num_channels
      self.patch_size = patch_size
      self.image_size = image_size
      self.initializer_range = initializer_range
      self.initializer_factor = initializer_factor
      self.attention_dropout = attention_dropout
      self.layer_norm_eps = layer_norm_eps
      self.hidden_act = hidden_act
  
imports: import os
  from collections import OrderedDict
  from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
label_desc: Use image node_id 1 to create a new ChineseCLIPVisionConfig object from inherited base classes: PretrainedConfig with example: obj = ChineseCLIPVisionConfig(hidden_size, intermediate_size, projection_dim, num_hidden_layers, num_attention_heads, num_channels, image_size, patch_size, hidden_act, layer_norm_eps, attention_dropout, initializer_range, initializer_factor)