codingutf8 2021 iz beltagy matthew e peters arman cohan 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 led model configuration from typing import list union from configurationutils import pretrainedconfig from utils import logging logger logging getloggername ledpretrainedconfigarchivemap allenailedbase16384 https huggingface coallenailedbase16384resolvemainconfig json see all led models at https huggingface comodels filterled class ledconfigpretrainedconfig r this is the configuration class to store the configuration of a ledmodel it is used to instantiate an led 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 led allenailedbase16384https huggingface coallenailedbase16384 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 50265 vocabulary size of the led model defines the number of different tokens that can be represented by the inputsids passed when calling ledmodel or tfledmodel dmodel int optional defaults to 1024 dimensionality of the layers and the pooler layer encoderlayers int optional defaults to 12 number of encoder layers decoderlayers int optional defaults to 12 number of decoder layers encoderattentionheads int optional defaults to 16 number of attention heads for each attention layer in the transformer encoder decoderattentionheads int optional defaults to 16 number of attention heads for each attention layer in the transformer decoder decoderffndim int optional defaults to 4096 dimensionality of the intermediate often named feedforward layer in decoder encoderffndim int optional defaults to 4096 dimensionality of the intermediate often named feedforward layer in decoder activationfunction str or function optional defaults to gelu the nonlinear activation function function or string in the encoder and pooler if string gelu relu silu and gelunew are supported dropout float optional defaults to 0 1 the dropout probability for all fully connected layers in the embeddings encoder and pooler attentiondropout float optional defaults to 0 0 the dropout ratio for the attention probabilities activationdropout float optional defaults to 0 0 the dropout ratio for activations inside the fully connected layer classifierdropout float optional defaults to 0 0 the dropout ratio for classifier maxencoderpositionembeddings int optional defaults to 16384 the maximum sequence length that the encoder might ever be used with maxdecoderpositionembeddings int optional defaults to 16384 the maximum sequence length that the decoder might ever be used with initstd float optional defaults to 0 02 the standard deviation of the truncatednormalinitializer for initializing all weight matrices encoderlayerdrop float optional defaults to 0 0 the layerdrop probability for the encoder see the layerdrop papersee https arxiv orgabs1909 11556 for more details decoderlayerdrop float optional defaults to 0 0 the layerdrop probability for the decoder see the layerdrop papersee https arxiv orgabs1909 11556 for more details usecache bool optional defaults to true whether or not the model should return the last keyvalues attentions not used by all models example python from transformers import ledmodel ledconfig initializing a led allenailedbase16384 style configuration configuration ledconfig initializing a model from the allenailedbase16384 style configuration model ledmodelconfiguration accessing the model configuration configuration model config modeltype led attributemap numattentionheads encoderattentionheads hiddensize dmodel attentionprobsdropoutprob attentiondropout initializerrange initstd def init self vocabsize50265 maxencoderpositionembeddings16384 maxdecoderpositionembeddings1024 encoderlayers12 encoderffndim4096 encoderattentionheads16 decoderlayers12 decoderffndim4096 decoderattentionheads16 encoderlayerdrop0 0 decoderlayerdrop0 0 usecachetrue isencoderdecodertrue activationfunctiongelu dmodel1024 dropout0 1 attentiondropout0 0 activationdropout0 0 initstd0 02 decoderstarttokenid2 classifierdropout0 0 padtokenid1 bostokenid0 eostokenid2 attentionwindow unionlistint int 512 kwargs self vocabsize vocabsize self maxencoderpositionembeddings maxencoderpositionembeddings self maxdecoderpositionembeddings maxdecoderpositionembeddings self dmodel dmodel self encoderffndim encoderffndim self encoderlayers encoderlayers self encoderattentionheads encoderattentionheads self decoderffndim decoderffndim self decoderlayers decoderlayers self decoderattentionheads decoderattentionheads self dropout dropout self attentiondropout attentiondropout self activationdropout activationdropout self activationfunction activationfunction self initstd initstd self encoderlayerdrop encoderlayerdrop self decoderlayerdrop decoderlayerdrop self classifierdropout classifierdropout self usecache usecache self numhiddenlayers encoderlayers self attentionwindow attentionwindow super init padtokenidpadtokenid bostokenidbostokenid eostokenideostokenid isencoderdecoderisencoderdecoder decoderstarttokeniddecoderstarttokenid kwargs coding utf 8 2021 iz beltagy matthew e peters arman cohan 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 led model configuration see all led models at https huggingface co models filter led this is the configuration class to store the configuration of a ledmodel it is used to instantiate an led 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 led allenai led base 16384 https huggingface co allenai led base 16384 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 50265 vocabulary size of the led model defines the number of different tokens that can be represented by the inputs_ids passed when calling ledmodel or tfledmodel d_model int optional defaults to 1024 dimensionality of the layers and the pooler layer encoder_layers int optional defaults to 12 number of encoder layers decoder_layers int optional defaults to 12 number of decoder layers encoder_attention_heads int optional defaults to 16 number of attention heads for each attention layer in the transformer encoder decoder_attention_heads int optional defaults to 16 number of attention heads for each attention layer in the transformer decoder decoder_ffn_dim int optional defaults to 4096 dimensionality of the intermediate often named feed forward layer in decoder encoder_ffn_dim int optional defaults to 4096 dimensionality of the intermediate often named feed forward layer in decoder activation_function str or function optional defaults to gelu the non linear activation function function or string in the encoder and pooler if string gelu relu silu and gelu_new are supported dropout float optional defaults to 0 1 the dropout probability for all fully connected layers in the embeddings encoder and pooler attention_dropout float optional defaults to 0 0 the dropout ratio for the attention probabilities activation_dropout float optional defaults to 0 0 the dropout ratio for activations inside the fully connected layer classifier_dropout float optional defaults to 0 0 the dropout ratio for classifier max_encoder_position_embeddings int optional defaults to 16384 the maximum sequence length that the encoder might ever be used with max_decoder_position_embeddings int optional defaults to 16384 the maximum sequence length that the decoder might ever be used with init_std float optional defaults to 0 02 the standard deviation of the truncated_normal_initializer for initializing all weight matrices encoder_layerdrop float optional defaults to 0 0 the layerdrop probability for the encoder see the layerdrop paper see https arxiv org abs 1909 11556 for more details decoder_layerdrop float optional defaults to 0 0 the layerdrop probability for the decoder see the layerdrop paper see https arxiv org abs 1909 11556 for more details use_cache bool optional defaults to true whether or not the model should return the last key values attentions not used by all models example python from transformers import ledmodel ledconfig initializing a led allenai led base 16384 style configuration configuration ledconfig initializing a model from the allenai led base 16384 style configuration model ledmodel configuration accessing the model configuration configuration model config
from typing import List, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)

LED_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/config.json",
    
}


class LEDConfig(PretrainedConfig):
    r

    model_type = "led"
    attribute_map = {
        "num_attention_heads": "encoder_attention_heads",
        "hidden_size": "d_model",
        "attention_probs_dropout_prob": "attention_dropout",
        "initializer_range": "init_std",
    }

    def __init__(
        self,
        vocab_size=50265,
        max_encoder_position_embeddings=16384,
        max_decoder_position_embeddings=1024,
        encoder_layers=12,
        encoder_ffn_dim=4096,
        encoder_attention_heads=16,
        decoder_layers=12,
        decoder_ffn_dim=4096,
        decoder_attention_heads=16,
        encoder_layerdrop=0.0,
        decoder_layerdrop=0.0,
        use_cache=True,
        is_encoder_decoder=True,
        activation_function="gelu",
        d_model=1024,
        dropout=0.1,
        attention_dropout=0.0,
        activation_dropout=0.0,
        init_std=0.02,
        decoder_start_token_id=2,
        classifier_dropout=0.0,
        pad_token_id=1,
        bos_token_id=0,
        eos_token_id=2,
        attention_window: Union[List[int], int] = 512,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.max_encoder_position_embeddings = max_encoder_position_embeddings
        self.max_decoder_position_embeddings = max_decoder_position_embeddings
        self.d_model = d_model
        self.encoder_ffn_dim = encoder_ffn_dim
        self.encoder_layers = encoder_layers
        self.encoder_attention_heads = encoder_attention_heads
        self.decoder_ffn_dim = decoder_ffn_dim
        self.decoder_layers = decoder_layers
        self.decoder_attention_heads = decoder_attention_heads
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.activation_dropout = activation_dropout
        self.activation_function = activation_function
        self.init_std = init_std
        self.encoder_layerdrop = encoder_layerdrop
        self.decoder_layerdrop = decoder_layerdrop
        self.classifier_dropout = classifier_dropout
        self.use_cache = use_cache
        self.num_hidden_layers = encoder_layers
        self.attention_window = attention_window

        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            is_encoder_decoder=is_encoder_decoder,
            decoder_start_token_id=decoder_start_token_id,
            **kwargs,
        )