
    Z j                     d    S r SSKJr  SSKJr  SSKJr  \" SS9\ " S S	\5      5       5       rS	/rg
)zCANINE model configuration    )strict   )PreTrainedConfig)auto_docstringzgoogle/canine-s)
checkpointc                   R   \ rS rSr% SrSrSr\\S'   Sr	\\S'   Sr
\\S'   S	r\\S
'   Sr\\S'   Sr\\-  \S'   Sr\\-  \S'   Sr\\S'   Sr\\S'   Sr\\S'   Sr\\S'   Sr\S-  \S'   Sr\S-  \S'   Sr\\\   -  S-  \S'   Sr\\S '   Sr\\S!'   S"r\\S#'   Sr\\S$'   S%r\\S&'   S'rg)(CanineConfig   a  
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
```caninei   hidden_size   num_hidden_layersnum_attention_headsi   intermediate_sizegelu
hidden_actg?hidden_dropout_probattention_probs_dropout_probi @  max_position_embeddings   type_vocab_sizeg{Gz?initializer_rangeg-q=layer_norm_epsr   Npad_token_idi   bos_token_idi  eos_token_id   downsampling_rateupsampling_kernel_size   num_hash_functionsnum_hash_buckets   local_transformer_stride )__name__
__module____qualname____firstlineno____doc__
model_typer   int__annotations__r   r   r   r   strr   floatr   r   r   r   r   r   r   r   listr   r   r!   r"   r$   __static_attributes__r%       ڀ/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/canine/configuration_canine.pyr	   r	      s    < JKs!!!s!J'**03 %#+3#(S(OS#u#!NE! L#* %L#*%+1L#S	/D(1s"#C#!c!$'c'r2   r	   N)	r*   huggingface_hub.dataclassesr   configuration_utilsr   utilsr   r	   __all__r%   r2   r3   <module>r8      sH    ! . 3 # ,-3(# 3(  .3(l 
r2   