class_docstr: This is the configuration class to store the configuration of a [`BlipTextModel`]. It is used to instantiate a BLIP
  text 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 `BlipText` used by the [base
  architectures](https://huggingface.co/Salesforce/blip-vqa-base).
  
  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 30524):
          Vocabulary size of the `Blip` text model. Defines the number of different tokens that can be represented by
          the `inputs_ids` passed when calling [`BlipModel`].
      hidden_size (`int`, *optional*, defaults to 768):
          Dimensionality of the encoder layers and the pooler layer.
      encoder_hidden_size (`int`, *optional*, defaults to 768):
          Dimensionality of the encoder layers from the vision model.
      intermediate_size (`int`, *optional*, defaults to 3072):
          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 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).
      hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
          The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
          `"relu"`, `"selu"` and `"gelu_new"` ``"gelu"` are supported.
      layer_norm_eps (`float`, *optional*, defaults to 1e-12):
          The epsilon used by the layer normalization layers.
      hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
          The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
      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.
      bos_token_id (`int`, *optional*, defaults to 30522):
          The id of the `beginning-of-sequence` token.
      eos_token_id (`int`, *optional*, defaults to 2):
          The id of the `end-of-sequence` token.
      pad_token_id (`int`, *optional*, defaults to 0):
          The id of the `padding` token.
      sep_token_id (`int`, *optional*, defaults to 102):
          The id of the `separator` token.
      is_decoder (`bool`, *optional*, defaults to `True`):
          Whether the model is used as a decoder.
      use_cache (`bool`, *optional*, defaults to `True`):
          Whether or not the model should return the last key/values attentions (not used by all models).
  
  Example:
  
  ```python
  >>> from transformers import BlipTextConfig, BlipTextModel
  
  >>> # Initializing a BlipTextConfig with Salesforce/blip-vqa-base style configuration
  >>> configuration = BlipTextConfig()
  
  >>> # Initializing a BlipTextModel (with random weights) from the Salesforce/blip-vqa-base style configuration
  >>> model = BlipTextModel(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
code: def __init__(
      self,
      vocab_size=30524,
      hidden_size=768,
      encoder_hidden_size=768,
      intermediate_size=3072,
      projection_dim=768,
      num_hidden_layers=12,
      num_attention_heads=8,
      max_position_embeddings=512,
      hidden_act="gelu",
      layer_norm_eps=1e-12,
      hidden_dropout_prob=0.0,
      attention_probs_dropout_prob=0.0,
      initializer_range=0.02,
      bos_token_id=30522,
      eos_token_id=2,
      pad_token_id=0,
      sep_token_id=102,
      is_decoder=True,
      use_cache=True,
      **kwargs,
  ):
      super().__init__(
          pad_token_id=pad_token_id,
          bos_token_id=bos_token_id,
          eos_token_id=eos_token_id,
          sep_token_id=sep_token_id,
          **kwargs,
      )
  
      self.vocab_size = vocab_size
      self.hidden_size = hidden_size
      self.encoder_hidden_size = encoder_hidden_size
      self.intermediate_size = intermediate_size
      self.projection_dim = projection_dim
      self.hidden_dropout_prob = hidden_dropout_prob
      self.num_hidden_layers = num_hidden_layers
      self.num_attention_heads = num_attention_heads
      self.max_position_embeddings = max_position_embeddings
      self.layer_norm_eps = layer_norm_eps
      self.hidden_act = hidden_act
      self.initializer_range = initializer_range
      self.attention_probs_dropout_prob = attention_probs_dropout_prob
      self.is_decoder = is_decoder
      self.use_cache = use_cache
  
file_path: transformers/src/transformers/models/blip/configuration_blip.py
imports: import os
  from typing import Union
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
label_desc: Use image node_id 1 to create a new BlipTextConfig object from inherited base classes: PretrainedConfig with example: obj = BlipTextConfig(vocab_size, hidden_size, encoder_hidden_size, intermediate_size, projection_dim, num_hidden_layers, num_attention_heads, max_position_embeddings, hidden_act, layer_norm_eps, hidden_dropout_prob, attention_probs_dropout_prob, initializer_range, bos_token_id, eos_token_id, pad_token_id, sep_token_id, is_decoder, use_cache)