codingutf8 2021 google ai and the huggingface inc team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache orglicenseslicense2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license fnet model configuration from configurationutils import pretrainedconfig from utils import logging logger logging getloggername fnetpretrainedconfigarchivemap googlefnetbase https huggingface cogooglefnetbaseresolvemainconfig json googlefnetlarge https huggingface cogooglefnetlargeresolvemainconfig json see all fnet models at https huggingface comodels filterfnet class fnetconfigpretrainedconfig r 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 googlefnetbasehttps huggingface cogooglefnetbase architecture configuration objects inherit from pretrainedconfig and can be used to control the model outputs read the documentation from pretrainedconfig for more information args vocabsize int optional defaults to 32000 vocabulary size of the fnet model defines the number of different tokens that can be represented by the inputsids passed when calling fnetmodel or tffnetmodel hiddensize int optional defaults to 768 dimension of the encoder layers and the pooler layer numhiddenlayers int optional defaults to 12 number of hidden layers in the transformer encoder intermediatesize int optional defaults to 3072 dimension of the intermediate i e feedforward layer in the transformer encoder hiddenact str or function optional defaults to gelunew the nonlinear activation function function or string in the encoder and pooler if string gelu relu selu and gelunew are supported hiddendropoutprob float optional defaults to 0 1 the dropout probabilitiy for all fully connected layers in the embeddings encoder and pooler maxpositionembeddings 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 typevocabsize int optional defaults to 4 the vocabulary size of the tokentypeids passed when calling fnetmodel or tffnetmodel initializerrange float optional defaults to 0 02 the standard deviation of the truncatednormalinitializer for initializing all weight matrices layernormeps float optional defaults to 1e12 the epsilon used by the layer normalization layers usetpufourieroptimizations bool optional defaults to false determines whether to use tpu optimized ffts if true the model will favor axiswise ffts transforms set to false for gpucpu hardware in which case ndimensional ffts are used tpushortseqlength 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 usetpufourieroptimizations 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 fnetbase style configuration configuration fnetconfig initializing a model with random weights from the fnetbase style configuration model fnetmodelconfiguration accessing the model configuration configuration model config modeltype fnet def init self vocabsize32000 hiddensize768 numhiddenlayers12 intermediatesize3072 hiddenactgelunew hiddendropoutprob0 1 maxpositionembeddings512 typevocabsize4 initializerrange0 02 layernormeps1e12 usetpufourieroptimizationsfalse tpushortseqlength512 padtokenid3 bostokenid1 eostokenid2 kwargs super initpadtokenidpadtokenid bostokenidbostokenid eostokenideostokenid kwargs self vocabsize vocabsize self maxpositionembeddings maxpositionembeddings self hiddensize hiddensize self numhiddenlayers numhiddenlayers self intermediatesize intermediatesize self hiddenact hiddenact self hiddendropoutprob hiddendropoutprob self initializerrange initializerrange self typevocabsize typevocabsize self layernormeps layernormeps self usetpufourieroptimizations usetpufourieroptimizations self tpushortseqlength tpushortseqlength coding utf 8 2021 google ai and the huggingface inc team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license fnet model configuration see all fnet models at https huggingface co models filter fnet 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 probabilitiy 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
from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)

FNET_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
    "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/config.json",
    
}


class FNetConfig(PretrainedConfig):
    r

    model_type = "fnet"

    def __init__(
        self,
        vocab_size=32000,
        hidden_size=768,
        num_hidden_layers=12,
        intermediate_size=3072,
        hidden_act="gelu_new",
        hidden_dropout_prob=0.1,
        max_position_embeddings=512,
        type_vocab_size=4,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        use_tpu_fourier_optimizations=False,
        tpu_short_seq_length=512,
        pad_token_id=3,
        bos_token_id=1,
        eos_token_id=2,
        **kwargs,
    ):
        super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)

        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        self.hidden_dropout_prob = hidden_dropout_prob
        self.initializer_range = initializer_range
        self.type_vocab_size = type_vocab_size
        self.layer_norm_eps = layer_norm_eps
        self.use_tpu_fourier_optimizations = use_tpu_fourier_optimizations
        self.tpu_short_seq_length = tpu_short_seq_length