o ZŽh¿ã@s@dZddlmZddlmZe e¡ZGdd„deƒZdgZ dS)zFNet model configurationé)ÚPretrainedConfig)ÚloggingcsDeZdZdZdZ         d‡fdd„ Z‡ZS)Ú 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 ```Zfneté}éé é Úgelu_newçš™™™™™¹?ééç{®Gáz”?çê-™—q=Frééc sftƒjd| ||dœ|¤Ž||_||_||_||_||_||_||_| |_ ||_ | |_ | |_ | |_ dS)N)Ú pad_token_idÚ bos_token_idÚ eos_token_id©)ÚsuperÚ__init__Ú vocab_sizeÚmax_position_embeddingsÚ hidden_sizeÚnum_hidden_layersÚintermediate_sizeÚ hidden_actÚhidden_dropout_probÚinitializer_rangeÚtype_vocab_sizeÚlayer_norm_epsÚuse_tpu_fourier_optimizationsÚtpu_short_seq_length)Úselfrrrrrrrrrr r!r"rrrÚkwargs©Ú __class__rúZ/var/www/auris/lib/python3.10/site-packages/transformers/models/fnet/configuration_fnet.pyrTs zFNetConfig.__init__)rrrrr r r r r rFr rrr)Ú__name__Ú __module__Ú __qualname__Ú__doc__Z model_typerÚ __classcell__rrr%r'rs&9ðrN) r+Zconfiguration_utilsrÚutilsrZ get_loggerr(ÚloggerrÚ__all__rrrr'Ús     _