codingutf8 2020 the squeezebert s 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 squeezebert model configuration from collections import ordereddict from typing import mapping from configurationutils import pretrainedconfig from onnx import onnxconfig from utils import logging logger logging getloggername squeezebertpretrainedconfigarchivemap squeezebertsqueezebertuncased https huggingface cosqueezebertsqueezebertuncasedresolvemainconfig json squeezebertsqueezebertmnli https huggingface cosqueezebertsqueezebertmnliresolvemainconfig json squeezebertsqueezebertmnliheadless https huggingface cosqueezebertsqueezebertmnliheadlessresolvemainconfig json class squeezebertconfigpretrainedconfig r this is the configuration class to store the configuration of a squeezebertmodel it is used to instantiate a squeezebert 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 squeezebert squeezebertsqueezebertuncasedhttps huggingface cosqueezebertsqueezebertuncased 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 30522 vocabulary size of the squeezebert model defines the number of different tokens that can be represented by the inputsids passed when calling squeezebertmodel hiddensize int optional defaults to 768 dimensionality 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 dimensionality of the intermediate often named feedforward layer in the transformer encoder hiddenact str or callable 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 hiddendropoutprob float optional defaults to 0 1 the dropout probability 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 2 the vocabulary size of the tokentypeids passed when calling bertmodel or tfbertmodel initializerrange float optional defaults to 0 02 the standard deviation of the truncatednormalinitializer for initializing all weight matrices layernormeps float optional defaults to 1e12 padtokenid int optional defaults to 0 the id of the token in the word embedding to use as padding embeddingsize int optional defaults to 768 the dimension of the word embedding vectors qgroups int optional defaults to 4 the number of groups in q layer kgroups int optional defaults to 4 the number of groups in k layer vgroups int optional defaults to 4 the number of groups in v layer postattentiongroups int optional defaults to 1 the number of groups in the first feed forward network layer intermediategroups int optional defaults to 4 the number of groups in the second feed forward network layer outputgroups int optional defaults to 4 the number of groups in the third feed forward network layer examples python from transformers import squeezebertconfig squeezebertmodel initializing a squeezebert configuration configuration squeezebertconfig initializing a model with random weights from the configuration above model squeezebertmodelconfiguration accessing the model configuration configuration model config attributes pretrainedconfigarchivemap dictstr str a dictionary containing all the available pretrained checkpoints copied from transformers models bert configurationbert bertonxxconfig with bertsqueezebert coding utf 8 2020 the squeezebert s 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 squeezebert model configuration this is the configuration class to store the configuration of a squeezebertmodel it is used to instantiate a squeezebert 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 squeezebert squeezebert squeezebert uncased https huggingface co squeezebert squeezebert uncased 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 30522 vocabulary size of the squeezebert model defines the number of different tokens that can be represented by the inputs_ids passed when calling squeezebertmodel hidden_size int optional defaults to 768 dimensionality 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 dimensionality of the intermediate often named feed forward layer in the transformer encoder hidden_act str or callable 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 hidden_dropout_prob float optional defaults to 0 1 the dropout probability 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 2 the vocabulary size of the token_type_ids passed when calling bertmodel or tfbertmodel 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 pad_token_id int optional defaults to 0 the id of the token in the word embedding to use as padding embedding_size int optional defaults to 768 the dimension of the word embedding vectors q_groups int optional defaults to 4 the number of groups in q layer k_groups int optional defaults to 4 the number of groups in k layer v_groups int optional defaults to 4 the number of groups in v layer post_attention_groups int optional defaults to 1 the number of groups in the first feed forward network layer intermediate_groups int optional defaults to 4 the number of groups in the second feed forward network layer output_groups int optional defaults to 4 the number of groups in the third feed forward network layer examples python from transformers import squeezebertconfig squeezebertmodel initializing a squeezebert configuration configuration squeezebertconfig initializing a model with random weights from the configuration above model squeezebertmodel configuration accessing the model configuration configuration model config attributes pretrained_config_archive_map dict str str a dictionary containing all the available pre trained checkpoints copied from transformers models bert configuration_bert bertonxxconfig with bert squeezebert
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


logger = logging.get_logger(__name__)

SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "squeezebert/squeezebert-uncased": (
        "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"
    ),
    "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json",
    "squeezebert/squeezebert-mnli-headless": (
        "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"
    ),
}


class SqueezeBertConfig(PretrainedConfig):
    r

    pretrained_config_archive_map = SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
    model_type = "squeezebert"

    def __init__(
        self,
        vocab_size=30522,
        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=2,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        pad_token_id=0,
        embedding_size=768,
        q_groups=4,
        k_groups=4,
        v_groups=4,
        post_attention_groups=1,
        intermediate_groups=4,
        output_groups=4,
        **kwargs,
    ):
        super().__init__(pad_token_id=pad_token_id, **kwargs)

        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.hidden_act = hidden_act
        self.intermediate_size = intermediate_size
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.type_vocab_size = type_vocab_size
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.embedding_size = embedding_size
        self.q_groups = q_groups
        self.k_groups = k_groups
        self.v_groups = v_groups
        self.post_attention_groups = post_attention_groups
        self.intermediate_groups = intermediate_groups
        self.output_groups = output_groups



class SqueezeBertOnnxConfig(OnnxConfig):
    @property
    def inputs(self) -> Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
            dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
        else:
            dynamic_axis = {0: "batch", 1: "sequence"}
        return OrderedDict(
            [
                ("input_ids", dynamic_axis),
                ("attention_mask", dynamic_axis),
                ("token_type_ids", dynamic_axis),
            ]
        )