class_docstr: This is the configuration class to store the configuration of a [`CanineModel`]. It is used to instantiate an
  CANINE 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 CANINE
  [google/canine-s](https://huggingface.co/google/canine-s) 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):
          Dimension of the encoder layers and the pooler layer.
      num_hidden_layers (`int`, *optional*, defaults to 12):
          Number of hidden layers in the deep Transformer encoder.
      num_attention_heads (`int`, *optional*, defaults to 12):
          Number of attention heads for each attention layer in the Transformer encoders.
      intermediate_size (`int`, *optional*, defaults to 3072):
          Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoders.
      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"` are supported.
      hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
          The dropout probabilitiy for all fully connected layers in the embeddings, encoders, 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 16384):
          The maximum sequence length that this model might ever be used with.
      type_vocab_size (`int`, *optional*, defaults to 16):
          The vocabulary size of the `token_type_ids` passed when calling [`CanineModel`].
      initializer_range (`float`, *optional*, defaults to 0.02):
          The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
      layer_norm_eps (`float`, *optional*, defaults to 1e-12):
          The epsilon used by the layer normalization layers.
      downsampling_rate (`int`, *optional*, defaults to 4):
          The rate at which to downsample the original character sequence length before applying the deep Transformer
          encoder.
      upsampling_kernel_size (`int`, *optional*, defaults to 4):
          The kernel size (i.e. the number of characters in each window) of the convolutional projection layer when
          projecting back from `hidden_size`*2 to `hidden_size`.
      num_hash_functions (`int`, *optional*, defaults to 8):
          The number of hash functions to use. Each hash function has its own embedding matrix.
      num_hash_buckets (`int`, *optional*, defaults to 16384):
          The number of hash buckets to use.
      local_transformer_stride (`int`, *optional*, defaults to 128):
          The stride of the local attention of the first shallow Transformer encoder. Defaults to 128 for good
          TPU/XLA memory alignment.
  
  Example:
  
  ```python
  >>> from transformers import CanineConfig, CanineModel
  
  >>> # Initializing a CANINE google/canine-s style configuration
  >>> configuration = CanineConfig()
  
  >>> # Initializing a model (with random weights) from the google/canine-s style configuration
  >>> model = CanineModel(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
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code: def __init__(
      self,
      hidden_size=768,
      num_hidden_layers=12,
      num_attention_heads=12,
      intermediate_size=3072,
      hidden_act="gelu",
      hidden_dropout_prob=0.1,
      attention_probs_dropout_prob=0.1,
      max_position_embeddings=16384,
      type_vocab_size=16,
      initializer_range=0.02,
      layer_norm_eps=1e-12,
      use_cache=True,
      is_encoder_decoder=False,
      pad_token_id=0,
      bos_token_id=0xE000,
      eos_token_id=0xE001,
      downsampling_rate=4,
      upsampling_kernel_size=4,
      num_hash_functions=8,
      num_hash_buckets=16384,
      local_transformer_stride=128,  # Good TPU/XLA memory alignment.
      **kwargs,
  ):
      super().__init__(
          pad_token_id=pad_token_id,
          bos_token_id=bos_token_id,
          eos_token_id=eos_token_id,
          **kwargs,
      )
  
      self.max_position_embeddings = max_position_embeddings
      self.hidden_size = hidden_size
      self.num_hidden_layers = num_hidden_layers
      self.num_attention_heads = num_attention_heads
      self.intermediate_size = intermediate_size
      self.hidden_act = hidden_act
      self.hidden_dropout_prob = hidden_dropout_prob
      self.attention_probs_dropout_prob = attention_probs_dropout_prob
      self.initializer_range = initializer_range
      self.type_vocab_size = type_vocab_size
      self.layer_norm_eps = layer_norm_eps
      self.use_cache = use_cache
  
      # Character config:
      self.downsampling_rate = downsampling_rate
      self.upsampling_kernel_size = upsampling_kernel_size
      self.num_hash_functions = num_hash_functions
      self.num_hash_buckets = num_hash_buckets
      self.local_transformer_stride = local_transformer_stride
  
imports: from ...configuration_utils import PretrainedConfig
  from ...utils import logging
label_desc: Use image node_id 1 to create a new CanineConfig object from inherited base classes: PretrainedConfig with example: obj = CanineConfig(hidden_size, num_hidden_layers, num_attention_heads, intermediate_size, hidden_act, hidden_dropout_prob, attention_probs_dropout_prob, max_position_embeddings, type_vocab_size, initializer_range, layer_norm_eps, use_cache, is_encoder_decoder, pad_token_id, bos_token_id, eos_token_id, downsampling_rate, upsampling_kernel_size, num_hash_functions, num_hash_buckets, local_transformer_stride)