class_docstr: This is the configuration class to store the configuration of an [`OwlViTTextModel`]. It is used to instantiate an
  OwlViT text encoder according to the specified arguments, defining the model architecture. Instantiating a
  configuration with the defaults will yield a similar configuration to that of the OwlViT
  [google/owlvit-base-patch32](https://huggingface.co/google/owlvit-base-patch32) 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 49408):
          Vocabulary size of the OWL-ViT text model. Defines the number of different tokens that can be represented
          by the `inputs_ids` passed when calling [`OwlViTTextModel`].
      hidden_size (`int`, *optional*, defaults to 512):
          Dimensionality of the encoder layers and the pooler layer.
      intermediate_size (`int`, *optional*, defaults to 2048):
          Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
      num_hidden_layers (`int`, *optional*, defaults to 12):
          Number of hidden layers in the Transformer encoder.
      num_attention_heads (`int`, *optional*, defaults to 8):
          Number of attention heads for each attention layer in the Transformer encoder.
      max_position_embeddings (`int`, *optional*, defaults to 16):
          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).
      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).
      pad_token_id (`int`, *optional*, defaults to 0):
          The id of the padding token in the input sequences.
      bos_token_id (`int`, *optional*, defaults to 49406):
          The id of the beginning-of-sequence token in the input sequences.
      eos_token_id (`int`, *optional*, defaults to 49407):
          The id of the end-of-sequence token in the input sequences.
  
  Example:
  
  ```python
  >>> from transformers import OwlViTTextConfig, OwlViTTextModel
  
  >>> # Initializing a OwlViTTextModel with google/owlvit-base-patch32 style configuration
  >>> configuration = OwlViTTextConfig()
  
  >>> # Initializing a OwlViTTextConfig from the google/owlvit-base-patch32 style configuration
  >>> model = OwlViTTextModel(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
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code: def __init__(
      self,
      vocab_size=49408,
      hidden_size=512,
      intermediate_size=2048,
      num_hidden_layers=12,
      num_attention_heads=8,
      max_position_embeddings=16,
      hidden_act="quick_gelu",
      layer_norm_eps=1e-5,
      attention_dropout=0.0,
      initializer_range=0.02,
      initializer_factor=1.0,
      pad_token_id=0,
      bos_token_id=49406,
      eos_token_id=49407,
      **kwargs,
  ):
      super().__init__(
          pad_token_id=pad_token_id,
          bos_token_id=bos_token_id,
          eos_token_id=eos_token_id,
          **kwargs,
      )
  
      self.vocab_size = vocab_size
      self.hidden_size = hidden_size
      self.intermediate_size = intermediate_size
      self.num_hidden_layers = num_hidden_layers
      self.num_attention_heads = num_attention_heads
      self.max_position_embeddings = max_position_embeddings
      self.hidden_act = hidden_act
      self.layer_norm_eps = layer_norm_eps
      self.attention_dropout = attention_dropout
      self.initializer_range = initializer_range
      self.initializer_factor = initializer_factor
  
imports: import os
  from collections import OrderedDict
  from typing import TYPE_CHECKING, Any, Dict, 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 OwlViTTextConfig object from inherited base classes: PretrainedConfig with example: obj = OwlViTTextConfig(vocab_size, hidden_size, intermediate_size, num_hidden_layers, num_attention_heads, max_position_embeddings, hidden_act, layer_norm_eps, attention_dropout, initializer_range, initializer_factor, pad_token_id, bos_token_id, eos_token_id)