class_docstr: This is the configuration class to store the configuration of a [`ChineseCLIPModel`]. It is used to instantiate a
  Chinese CLIP 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 Chinese CLIP
  [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:
      vocab_size (`int`, *optional*, defaults to 30522):
          Vocabulary size of the CHINESE_CLIP model. Defines the number of different tokens that can be represented
          by the `inputs_ids` passed when calling [`ChineseCLIPModel`].
      hidden_size (`int`, *optional*, defaults to 768):
          Dimensionality of the encoder layers and the pooler layer.
      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.
      intermediate_size (`int`, *optional*, defaults to 3072):
          Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
      hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
          The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
          `"relu"`, `"silu"` and `"gelu_new"` are supported.
      hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
          The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
      attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
          The dropout ratio for the attention probabilities.
      max_position_embeddings (`int`, *optional*, defaults to 512):
          The maximum sequence length that this model might ever be used with. Typically set this to something large
          just in case (e.g., 512 or 1024 or 2048).
      type_vocab_size (`int`, *optional*, defaults to 2):
          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
          [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
          For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
          with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
      use_cache (`bool`, *optional*, defaults to `True`):
          Whether or not the model should return the last key/values attentions (not used by all models). Only
          relevant if `config.is_decoder=True`.
  
  Example:
  
  ```python
  >>> from transformers import ChineseCLIPTextConfig, ChineseCLIPTextModel
  
  >>> # Initializing a ChineseCLIPTextConfig with OFA-Sys/chinese-clip-vit-base-patch16 style configuration
  >>> configuration = ChineseCLIPTextConfig()
  
  >>> # Initializing a ChineseCLIPTextModel (with random weights) from the OFA-Sys/chinese-clip-vit-base-patch16 style configuration
  >>> model = ChineseCLIPTextModel(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
code: def from_pretrained(
      cls,
      pretrained_model_name_or_path: Union[str, os.PathLike],
      **kwargs,
  ) -> "PretrainedConfig":
      cls._set_token_in_kwargs(kwargs)
  
      config_dict, kwargs = cls.get_config_dict(
          pretrained_model_name_or_path, **kwargs
      )
  
      # get the vision config dict if we are loading from ChineseCLIPConfig
      if config_dict.get("model_type") == "chinese_clip":
          config_dict = config_dict["text_config"]
  
      if (
          "model_type" in config_dict
          and hasattr(cls, "model_type")
          and config_dict["model_type"] != cls.model_type
      ):
          logger.warning(
              f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
              f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
          )
  
      return cls.from_dict(config_dict, **kwargs)
  
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 2 for calling the ChineseCLIPTextConfig obj's underlying member method code with example usage: obj.from_pretrained(cls, pretrained_model_name_or_path) and returns: cls