codingutf8 2020 microsoft 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 debertav2 model configuration from collections import ordereddict from typing import typechecking any mapping optional union from configurationutils import pretrainedconfig from onnx import onnxconfig from utils import logging if typechecking from import featureextractionmixin pretrainedtokenizerbase tensortype logger logging getloggername debertav2pretrainedconfigarchivemap microsoftdebertav2xlarge https huggingface comicrosoftdebertav2xlargeresolvemainconfig json microsoftdebertav2xxlarge https huggingface comicrosoftdebertav2xxlargeresolvemainconfig json microsoftdebertav2xlargemnli https huggingface comicrosoftdebertav2xlargemnliresolvemainconfig json microsoftdebertav2xxlargemnli https huggingface comicrosoftdebertav2xxlargemnliresolvemainconfig json class debertav2configpretrainedconfig r this is the configuration class to store the configuration of a debertav2model it is used to instantiate a debertav2 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 deberta microsoftdebertav2xlargehttps huggingface comicrosoftdebertav2xlarge architecture configuration objects inherit from pretrainedconfig and can be used to control the model outputs read the documentation from pretrainedconfig for more information arguments vocabsize int optional defaults to 128100 vocabulary size of the debertav2 model defines the number of different tokens that can be represented by the inputsids passed when calling debertav2model hiddensize int optional defaults to 1536 dimensionality of the encoder layers and the pooler layer numhiddenlayers int optional defaults to 24 number of hidden layers in the transformer encoder numattentionheads int optional defaults to 24 number of attention heads for each attention layer in the transformer encoder intermediatesize int optional defaults to 6144 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 gelu tanh gelufast mish linear sigmoid 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 0 the vocabulary size of the tokentypeids passed when calling debertamodel or tfdebertamodel initializerrange float optional defaults to 0 02 the standard deviation of the truncatednormalinitializer for initializing all weight matrices layernormeps float optional defaults to 1e7 the epsilon used by the layer normalization layers relativeattention bool optional defaults to true whether use relative position encoding maxrelativepositions int optional defaults to 1 the range of relative positions maxpositionembeddings maxpositionembeddings use the same value as maxpositionembeddings padtokenid int optional defaults to 0 the value used to pad inputids positionbiasedinput bool optional defaults to false whether add absolute position embedding to content embedding posatttype liststr optional the type of relative position attention it can be a combination of p2c c2p e g p2c p2c c2p p2c c2p layernormeps float optional defaults to 1e12 the epsilon used by the layer normalization layers example python from transformers import debertav2config debertav2model initializing a debertav2 microsoftdebertav2xlarge style configuration configuration debertav2config initializing a model with random weights from the microsoftdebertav2xlarge style configuration model debertav2modelconfiguration accessing the model configuration configuration model config modeltype debertav2 def init self vocabsize128100 hiddensize1536 numhiddenlayers24 numattentionheads24 intermediatesize6144 hiddenactgelu hiddendropoutprob0 1 attentionprobsdropoutprob0 1 maxpositionembeddings512 typevocabsize0 initializerrange0 02 layernormeps1e7 relativeattentionfalse maxrelativepositions1 padtokenid0 positionbiasedinputtrue posatttypenone poolerdropout0 poolerhiddenactgelu kwargs super initkwargs self hiddensize hiddensize self numhiddenlayers numhiddenlayers self numattentionheads numattentionheads self intermediatesize intermediatesize self hiddenact hiddenact self hiddendropoutprob hiddendropoutprob self attentionprobsdropoutprob attentionprobsdropoutprob self maxpositionembeddings maxpositionembeddings self typevocabsize typevocabsize self initializerrange initializerrange self relativeattention relativeattention self maxrelativepositions maxrelativepositions self padtokenid padtokenid self positionbiasedinput positionbiasedinput backwards compatibility if isinstanceposatttype str posatttype x strip for x in posatttype lower split self posatttype posatttype self vocabsize vocabsize self layernormeps layernormeps self poolerhiddensize kwargs getpoolerhiddensize hiddensize self poolerdropout poolerdropout self poolerhiddenact poolerhiddenact class debertav2onnxconfigonnxconfig property def inputsself mappingstr mappingint str if self task multiplechoice dynamicaxis 0 batch 1 choice 2 sequence else dynamicaxis 0 batch 1 sequence if self config typevocabsize 0 return ordereddict inputids dynamicaxis attentionmask dynamicaxis tokentypeids dynamicaxis else return ordereddictinputids dynamicaxis attentionmask dynamicaxis property def defaultonnxopsetself int return 12 def generatedummyinputs self preprocessor unionpretrainedtokenizerbase featureextractionmixin batchsize int 1 seqlength int 1 numchoices int 1 ispair bool false framework optionaltensortype none numchannels int 3 imagewidth int 40 imageheight int 40 tokenizer pretrainedtokenizerbase none mappingstr any dummyinputs super generatedummyinputspreprocessorpreprocessor frameworkframework if self config typevocabsize 0 and tokentypeids in dummyinputs del dummyinputstokentypeids return dummyinputs coding utf 8 2020 microsoft 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 deberta v2 model configuration this is the configuration class to store the configuration of a debertav2model it is used to instantiate a deberta v2 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 deberta microsoft deberta v2 xlarge https huggingface co microsoft deberta v2 xlarge architecture configuration objects inherit from pretrainedconfig and can be used to control the model outputs read the documentation from pretrainedconfig for more information arguments vocab_size int optional defaults to 128100 vocabulary size of the deberta v2 model defines the number of different tokens that can be represented by the inputs_ids passed when calling debertav2model hidden_size int optional defaults to 1536 dimensionality of the encoder layers and the pooler layer num_hidden_layers int optional defaults to 24 number of hidden layers in the transformer encoder num_attention_heads int optional defaults to 24 number of attention heads for each attention layer in the transformer encoder intermediate_size int optional defaults to 6144 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 gelu tanh gelu_fast mish linear sigmoid 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 0 the vocabulary size of the token_type_ids passed when calling debertamodel or tfdebertamodel 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 7 the epsilon used by the layer normalization layers relative_attention bool optional defaults to true whether use relative position encoding max_relative_positions int optional defaults to 1 the range of relative positions max_position_embeddings max_position_embeddings use the same value as max_position_embeddings pad_token_id int optional defaults to 0 the value used to pad input_ids position_biased_input bool optional defaults to false whether add absolute position embedding to content embedding pos_att_type list str optional the type of relative position attention it can be a combination of p2c c2p e g p2c p2c c2p p2c c2p layer_norm_eps float optional defaults to 1e 12 the epsilon used by the layer normalization layers example python from transformers import debertav2config debertav2model initializing a deberta v2 microsoft deberta v2 xlarge style configuration configuration debertav2config initializing a model with random weights from the microsoft deberta v2 xlarge style configuration model debertav2model configuration accessing the model configuration configuration model config backwards compatibility
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
    from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


logger = logging.get_logger(__name__)

DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"
    ),
    "microsoft/deberta-v2-xxlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"
    ),
}


class DebertaV2Config(PretrainedConfig):
    r

    model_type = "deberta-v2"

    def __init__(
        self,
        vocab_size=128100,
        hidden_size=1536,
        num_hidden_layers=24,
        num_attention_heads=24,
        intermediate_size=6144,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=512,
        type_vocab_size=0,
        initializer_range=0.02,
        layer_norm_eps=1e-7,
        relative_attention=False,
        max_relative_positions=-1,
        pad_token_id=0,
        position_biased_input=True,
        pos_att_type=None,
        pooler_dropout=0,
        pooler_hidden_act="gelu",
        **kwargs,
    ):
        super().__init__(**kwargs)

        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.max_position_embeddings = max_position_embeddings
        self.type_vocab_size = type_vocab_size
        self.initializer_range = initializer_range
        self.relative_attention = relative_attention
        self.max_relative_positions = max_relative_positions
        self.pad_token_id = pad_token_id
        self.position_biased_input = position_biased_input

        
        if isinstance(pos_att_type, str):
            pos_att_type = [x.strip() for x in pos_att_type.lower().split("|")]

        self.pos_att_type = pos_att_type
        self.vocab_size = vocab_size
        self.layer_norm_eps = layer_norm_eps

        self.pooler_hidden_size = kwargs.get("pooler_hidden_size", hidden_size)
        self.pooler_dropout = pooler_dropout
        self.pooler_hidden_act = pooler_hidden_act


class DebertaV2OnnxConfig(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"}
        if self._config.type_vocab_size > 0:
            return OrderedDict(
                [("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)]
            )
        else:
            return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)])

    @property
    def default_onnx_opset(self) -> int:
        return 12

    def generate_dummy_inputs(
        self,
        preprocessor: Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"],
        batch_size: int = -1,
        seq_length: int = -1,
        num_choices: int = -1,
        is_pair: bool = False,
        framework: Optional["TensorType"] = None,
        num_channels: int = 3,
        image_width: int = 40,
        image_height: int = 40,
        tokenizer: "PreTrainedTokenizerBase" = None,
    ) -> Mapping[str, Any]:
        dummy_inputs = super().generate_dummy_inputs(preprocessor=preprocessor, framework=framework)
        if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
            del dummy_inputs["token_type_ids"]
        return dummy_inputs