codingutf8 2023 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 mra model configuration from configurationutils import pretrainedconfig from utils import logging logger logging getloggername mrapretrainedconfigarchivemap uwmadisonmrabase5124 https huggingface couwmadisonmrabase5124resolvemainconfig json class mraconfigpretrainedconfig r this is the configuration class to store the configuration of a mramodel it is used to instantiate an mra 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 mra uwmadisonmrabase5124https huggingface couwmadisonmrabase5124 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 mra model defines the number of different tokens that can be represented by the inputsids passed when calling mramodel 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 numattentionheads int optional defaults to 12 number of attention heads for each attention layer 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 gelu 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 attentionprobsdropoutprob float optional defaults to 0 1 the dropout ratio for the attention probabilities 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 1 the vocabulary size of the tokentypeids passed when calling mramodel initializerrange float optional defaults to 0 02 the standard deviation of the truncatednormalinitializer for initializing all weight matrices layernormeps float optional defaults to 1e5 the epsilon used by the layer normalization layers positionembeddingtype str optional defaults to absolute type of position embedding choose one of absolute relativekey relativekeyquery blockperrow int optional defaults to 4 used to set the budget for the high resolution scale approxmode str optional defaults to full controls whether both low and high resolution approximations are used set to full for both low and high resolution and sparse for only low resolution initialpriorfirstnblocks int optional defaults to 0 the initial number of blocks for which high resolution is used initialpriordiagonalnblocks int optional defaults to 0 the number of diagonal blocks for which high resolution is used example python from transformers import mraconfig mramodel initializing a mra uwmadisonmrabase5124 style configuration configuration mraconfig initializing a model with random weights from the uwmadisonmrabase5124 style configuration model mramodelconfiguration accessing the model configuration configuration model config modeltype mra def init self vocabsize50265 hiddensize768 numhiddenlayers12 numattentionheads12 intermediatesize3072 hiddenactgelu hiddendropoutprob0 1 attentionprobsdropoutprob0 1 maxpositionembeddings512 typevocabsize1 initializerrange0 02 layernormeps1e5 positionembeddingtypeabsolute blockperrow4 approxmodefull initialpriorfirstnblocks0 initialpriordiagonalnblocks0 padtokenid1 bostokenid0 eostokenid2 kwargs super initpadtokenidpadtokenid bostokenidbostokenid eostokenideostokenid kwargs self vocabsize vocabsize self maxpositionembeddings maxpositionembeddings self hiddensize hiddensize self numhiddenlayers numhiddenlayers self numattentionheads numattentionheads self intermediatesize intermediatesize self hiddenact hiddenact self hiddendropoutprob hiddendropoutprob self attentionprobsdropoutprob attentionprobsdropoutprob self initializerrange initializerrange self typevocabsize typevocabsize self layernormeps layernormeps self positionembeddingtype positionembeddingtype self blockperrow blockperrow self approxmode approxmode self initialpriorfirstnblocks initialpriorfirstnblocks self initialpriordiagonalnblocks initialpriordiagonalnblocks coding utf 8 2023 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 mra model configuration this is the configuration class to store the configuration of a mramodel it is used to instantiate an mra 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 mra uw madison mra base 512 4 https huggingface co uw madison mra base 512 4 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 mra model defines the number of different tokens that can be represented by the inputs_ids passed when calling mramodel 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 num_attention_heads int optional defaults to 12 number of attention heads for each attention layer 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 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 attention_probs_dropout_prob float optional defaults to 0 1 the dropout ratio for the attention probabilities 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 1 the vocabulary size of the token_type_ids passed when calling mramodel 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 5 the epsilon used by the layer normalization layers position_embedding_type str optional defaults to absolute type of position embedding choose one of absolute relative_key relative_key_query block_per_row int optional defaults to 4 used to set the budget for the high resolution scale approx_mode str optional defaults to full controls whether both low and high resolution approximations are used set to full for both low and high resolution and sparse for only low resolution initial_prior_first_n_blocks int optional defaults to 0 the initial number of blocks for which high resolution is used initial_prior_diagonal_n_blocks int optional defaults to 0 the number of diagonal blocks for which high resolution is used example python from transformers import mraconfig mramodel initializing a mra uw madison mra base 512 4 style configuration configuration mraconfig initializing a model with random weights from the uw madison mra base 512 4 style configuration model mramodel configuration accessing the model configuration configuration model config
from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)

MRA_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "uw-madison/mra-base-512-4": "https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json",
}


class MraConfig(PretrainedConfig):
    r

    model_type = "mra"

    def __init__(
        self,
        vocab_size=50265,
        hidden_size=768,
        num_hidden_layers=12,
        num_attention_heads=12,
        intermediate_size=3072,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=512,
        type_vocab_size=1,
        initializer_range=0.02,
        layer_norm_eps=1e-5,
        position_embedding_type="absolute",
        block_per_row=4,
        approx_mode="full",
        initial_prior_first_n_blocks=0,
        initial_prior_diagonal_n_blocks=0,
        pad_token_id=1,
        bos_token_id=0,
        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.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.initializer_range = initializer_range
        self.type_vocab_size = type_vocab_size
        self.layer_norm_eps = layer_norm_eps
        self.position_embedding_type = position_embedding_type
        self.block_per_row = block_per_row
        self.approx_mode = approx_mode
        self.initial_prior_first_n_blocks = initial_prior_first_n_blocks
        self.initial_prior_diagonal_n_blocks = initial_prior_diagonal_n_blocks