sgZdZddlmZddlmZej eZGddeZy)zFNet model configuration)PretrainedConfig)loggingcFeZdZdZdZ dfd ZxZS) FNetConfigak This is the configuration class to store the configuration of a [`FNetModel`]. It is used to instantiate an FNet 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 FNet [google/fnet-base](https://huggingface.co/google/fnet-base) 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 32000): Vocabulary size of the FNet model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`FNetModel`] or [`TFFNetModel`]. 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 Transformer encoder. intermediate_size (`int`, *optional*, defaults to 3072): Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"gelu_new"`): 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 probability for all fully connected layers in the embeddings, encoder, and pooler. 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). type_vocab_size (`int`, *optional*, defaults to 4): The vocabulary size of the `token_type_ids` passed when calling [`FNetModel`] or [`TFFNetModel`]. 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. use_tpu_fourier_optimizations (`bool`, *optional*, defaults to `False`): Determines whether to use TPU optimized FFTs. If `True`, the model will favor axis-wise FFTs transforms. Set to `False` for GPU/CPU hardware, in which case n-dimensional FFTs are used. tpu_short_seq_length (`int`, *optional*, defaults to 512): The sequence length that is expected by the model when using TPUs. This will be used to initialize the DFT matrix only when *use_tpu_fourier_optimizations* is set to `True` and the input sequence is shorter than or equal to 4096 tokens. Example: ```python >>> from transformers import FNetConfig, FNetModel >>> # Initializing a FNet fnet-base style configuration >>> configuration = FNetConfig() >>> # Initializing a model (with random weights) from the fnet-base style configuration >>> model = FNetModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```fnetc t|d| ||d|||_||_||_||_||_||_||_| |_ ||_ | |_ | |_ | |_ y)N) pad_token_id bos_token_id eos_token_id)super__init__ vocab_sizemax_position_embeddings hidden_sizenum_hidden_layersintermediate_size hidden_acthidden_dropout_probinitializer_rangetype_vocab_sizelayer_norm_epsuse_tpu_fourier_optimizationstpu_short_seq_length)selfrrrrrrrrrrrrr r r kwargs __class__s ^/var/www/html/venv/lib/python3.12/site-packages/transformers/models/fnet/configuration_fnet.pyrzFNetConfig.__init__Ts~& sl\hslrs$'>$&!2!2$#6 !2.,-J*$8!)i}i i gelu_newg?g{Gz?g-q=Fr"r)__name__ __module__ __qualname____doc__ model_typer __classcell__)rs@rrrsI7 rJ #&+ ! 9 9rrN) r)configuration_utilsrutilsr get_loggerr&loggerrr rrr0s33   H %\9!\9r