class_docstr: This is the configuration class to store the configuration of a [`FalconModel`]. It is used to instantiate a Falcon
  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
  [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) 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 65024):
          Vocabulary size of the Falcon model. Defines the number of different tokens that can be represented by the
          `inputs_ids` passed when calling [`FalconModel`]
      hidden_size (`int`, *optional*, defaults to 4544):
          Dimension of the hidden representations.
      num_hidden_layers (`int`, *optional*, defaults to 32):
          Number of hidden layers in the Transformer decoder.
      num_attention_heads (`int`, *optional*, defaults to 71):
          Number of attention heads for each attention layer in the Transformer encoder.
      layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
          The epsilon used by the layer normalization layers.
      initializer_range (`float`, *optional*, defaults to 0.02):
          The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
      use_cache (`bool`, *optional*, defaults to `True`):
          Whether the model should return the last key/values attentions (not used by all models). Only relevant if
          `config.is_decoder=True`.
      hidden_dropout (`float`, *optional*, defaults to 0.0):
          The dropout probability for MLP layers.
      attention_dropout (`float`, *optional*, defaults to 0.0):
          The dropout probability for attention layers.
      num_kv_heads (`int`, *optional*):
          Number of key-value heads to use per attention layer. If unset, defaults to the same value as
          `num_attention_heads`.
      alibi (`bool`, *optional*, defaults to `False`):
          Whether to use ALiBi positional biases during self-attention.
      new_decoder_architecture (`bool`, *optional*, defaults to `False`):
          Whether to use the new (Falcon-40B) decoder architecture. If `True`, the `multi_query` and `parallel_attn`
          arguments are ignored, as the new decoder always uses parallel attention.
      multi_query (`bool`, *optional*, defaults to `True`):
          Whether to use multi-query attention in the decoder. Ignored when `new_decoder_architecture` is `True`.
      parallel_attn (`bool`, *optional*, defaults to `True`):
          Whether to compute attention in parallel with the feedforward layer. If False, they are consecutive
          instead, as in the original Transformer architecture. Ignored when `new_decoder_architecture` is `True`.
      bias (`bool`, *optional*, defaults to `False`):
          Whether to use bias on Linear layers.
      max_position_embeddings (`int`, *optional*, defaults to 2048):
          The maximum sequence length that this model might ever be used with, when `alibi` is `False`. Pretrained
          Falcon models with RoPE support up to 2048 tokens.
      rope_theta (`float`, *optional*, defaults to 10000.0):
          The base period of the RoPE embeddings.
      rope_scaling (`Dict`, *optional*):
          Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
          strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
          `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
          `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
          these scaling strategies behave:
          https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
          experimental feature, subject to breaking API changes in future versions.
      bos_token_id (`int`, *optional*, defaults to 11):
          The id of the "beginning-of-sequence" token.
      eos_token_id (`int`, *optional*, defaults to 11):
          The id of the "end-of-sequence" token.
  
  Example:
  
  ```python
  >>> from transformers import FalconModel, FalconConfig
  
  >>> # Initializing a small (2-layer) Falcon configuration
  >>> configuration = FalconConfig(num_hidden_layers=2)
  
  >>> # Initializing a model from the small configuration
  >>> model = FalconModel(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
docstr: Validate the `rope_scaling` configuration.
docstr_tok: ["Validate","the","`","rope_scaling","`","configuration","."]
code: def _rope_scaling_validation(self):
      """
      Validate the `rope_scaling` configuration.
      """
      if self.rope_scaling is None:
          return
  
      if self.alibi:
          raise ValueError(
              "`rope_scaling` is not supported when `alibi` is `True`."
          )
  
      if (
          not isinstance(self.rope_scaling, dict)
          or len(self.rope_scaling) != 2
      ):
          raise ValueError(
              "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
              f"got {self.rope_scaling}"
          )
      rope_scaling_type = self.rope_scaling.get("type", None)
      rope_scaling_factor = self.rope_scaling.get("factor", None)
      if rope_scaling_type is None or rope_scaling_type not in [
          "linear",
          "dynamic",
      ]:
          raise ValueError(
              f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
          )
      if (
          rope_scaling_factor is None
          or not isinstance(rope_scaling_factor, float)
          or rope_scaling_factor <= 1.0
      ):
          raise ValueError(
              f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}"
          )
  
imports: from ...configuration_utils import PretrainedConfig
  from ...utils import logging
label_desc: Use image node_id 4 for calling the FalconConfig obj's underlying member method code with example usage: obj._rope_scaling_validation() without return types