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 align model configuration import os from typing import typechecking list union if typechecking pass from configurationutils import pretrainedconfig from utils import logging logger logging getloggername alignpretrainedconfigarchivemap kakaobrainalignbase https huggingface cokakaobrainalignbaseresolvemainconfig json class aligntextconfigpretrainedconfig r this is the configuration class to store the configuration of a aligntextmodel it is used to instantiate a align text encoder according to the specified arguments defining the model architecture instantiating a configuration with the defaults will yield a similar configuration to that of the text encoder of the align kakaobrainalignbasehttps huggingface cokakaobrainalignbase architecture the default values here are copied from bert 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 align text model defines the number of different tokens that can be represented by the inputsids passed when calling aligntextmodel 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 aligntextmodel 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 padtokenid int optional defaults to 0 padding token id positionembeddingtype str optional defaults to absolute type of position embedding choose one of absolute relativekey relativekeyquery for positional embeddings use absolute for more information on relativekey please refer to selfattention with relative position representations shaw et al https arxiv orgabs1803 02155 for more information on relativekeyquery please refer to method 4 in improve transformer models with better relative position embeddings huang et al https arxiv orgabs2009 13658 usecache bool optional defaults to true whether or not the model should return the last keyvalues attentions not used by all models only relevant if config isdecodertrue example python from transformers import aligntextconfig aligntextmodel initializing a aligntextconfig with kakaobrainalignbase style configuration configuration aligntextconfig initializing a aligntextmodel with random weights from the kakaobrainalignbase style configuration model aligntextmodelconfiguration accessing the model configuration configuration model config modeltype aligntextmodel def init self vocabsize30522 hiddensize768 numhiddenlayers12 numattentionheads12 intermediatesize3072 hiddenactgelu hiddendropoutprob0 1 attentionprobsdropoutprob0 1 maxpositionembeddings512 typevocabsize2 initializerrange0 02 layernormeps1e12 padtokenid0 positionembeddingtypeabsolute usecachetrue kwargs super initkwargs self vocabsize vocabsize self hiddensize hiddensize self numhiddenlayers numhiddenlayers self numattentionheads numattentionheads self hiddenact hiddenact self intermediatesize intermediatesize self hiddendropoutprob hiddendropoutprob self attentionprobsdropoutprob attentionprobsdropoutprob self maxpositionembeddings maxpositionembeddings self typevocabsize typevocabsize self initializerrange initializerrange self layernormeps layernormeps self positionembeddingtype positionembeddingtype self usecache usecache self padtokenid padtokenid classmethod def frompretrainedcls pretrainedmodelnameorpath unionstr os pathlike kwargs pretrainedconfig cls settokeninkwargskwargs configdict kwargs cls getconfigdictpretrainedmodelnameorpath kwargs get the text config dict if we are loading from alignconfig if configdict getmodeltype align configdict configdicttextconfig if modeltype in configdict and hasattrcls modeltype and configdictmodeltype cls modeltype logger warning fyou are using a model of type configdict modeltype to instantiate a model of type fcls modeltype this is not supported for all configurations of models and can yield errors return cls fromdictconfigdict kwargs class alignvisionconfigpretrainedconfig r this is the configuration class to store the configuration of a alignvisionmodel it is used to instantiate a align vision encoder according to the specified arguments defining the model architecture instantiating a configuration with the defaults will yield a similar configuration to that of the vision encoder of the align kakaobrainalignbasehttps huggingface cokakaobrainalignbase architecture the default values are copied from efficientnet efficientnetb7 configuration objects inherit from pretrainedconfig and can be used to control the model outputs read the documentation from pretrainedconfig for more information args numchannels int optional defaults to 3 the number of input channels imagesize int optional defaults to 600 the input image size widthcoefficient float optional defaults to 2 0 scaling coefficient for network width at each stage depthcoefficient float optional defaults to 3 1 scaling coefficient for network depth at each stage depthdivisor int optional defaults to 8 a unit of network width kernelsizes listint optional defaults to 3 3 5 3 5 5 3 list of kernel sizes to be used in each block inchannels listint optional defaults to 32 16 24 40 80 112 192 list of input channel sizes to be used in each block for convolutional layers outchannels listint optional defaults to 16 24 40 80 112 192 320 list of output channel sizes to be used in each block for convolutional layers depthwisepadding listint optional defaults to list of block indices with square padding strides listint optional defaults to 1 2 2 2 1 2 1 list of stride sizes to be used in each block for convolutional layers numblockrepeats listint optional defaults to 1 2 2 3 3 4 1 list of the number of times each block is to repeated expandratios listint optional defaults to 1 6 6 6 6 6 6 list of scaling coefficient of each block squeezeexpansionratio float optional defaults to 0 25 squeeze expansion ratio hiddenact str or function optional defaults to silu the nonlinear activation function function or string in each block if string gelu relu selu gelunew silu and mish are supported hiddemdim int optional defaults to 1280 the hidden dimension of the layer before the classification head poolingtype str or function optional defaults to mean type of final pooling to be applied before the dense classification head available options are mean max initializerrange float optional defaults to 0 02 the standard deviation of the truncatednormalinitializer for initializing all weight matrices batchnormeps float optional defaults to 1e3 the epsilon used by the batch normalization layers batchnormmomentum float optional defaults to 0 99 the momentum used by the batch normalization layers dropconnectrate float optional defaults to 0 2 the drop rate for skip connections example python from transformers import alignvisionconfig alignvisionmodel initializing a alignvisionconfig with kakaobrainalignbase style configuration configuration alignvisionconfig initializing a alignvisionmodel with random weights from the kakaobrainalignbase style configuration model alignvisionmodelconfiguration accessing the model configuration configuration model config modeltype alignvisionmodel def init self numchannels int 3 imagesize int 600 widthcoefficient float 2 0 depthcoefficient float 3 1 depthdivisor int 8 kernelsizes listint 3 3 5 3 5 5 3 inchannels listint 32 16 24 40 80 112 192 outchannels listint 16 24 40 80 112 192 320 depthwisepadding listint strides listint 1 2 2 2 1 2 1 numblockrepeats listint 1 2 2 3 3 4 1 expandratios listint 1 6 6 6 6 6 6 squeezeexpansionratio float 0 25 hiddenact str swish hiddendim int 2560 poolingtype str mean initializerrange float 0 02 batchnormeps float 0 001 batchnormmomentum float 0 99 dropconnectrate float 0 2 kwargs super initkwargs self numchannels numchannels self imagesize imagesize self widthcoefficient widthcoefficient self depthcoefficient depthcoefficient self depthdivisor depthdivisor self kernelsizes kernelsizes self inchannels inchannels self outchannels outchannels self depthwisepadding depthwisepadding self strides strides self numblockrepeats numblockrepeats self expandratios expandratios self squeezeexpansionratio squeezeexpansionratio self hiddenact hiddenact self hiddendim hiddendim self poolingtype poolingtype self initializerrange initializerrange self batchnormeps batchnormeps self batchnormmomentum batchnormmomentum self dropconnectrate dropconnectrate self numhiddenlayers sumnumblockrepeats 4 classmethod def frompretrainedcls pretrainedmodelnameorpath unionstr os pathlike kwargs pretrainedconfig cls settokeninkwargskwargs configdict kwargs cls getconfigdictpretrainedmodelnameorpath kwargs get the vision config dict if we are loading from alignconfig if configdict getmodeltype align configdict configdictvisionconfig if modeltype in configdict and hasattrcls modeltype and configdictmodeltype cls modeltype logger warning fyou are using a model of type configdict modeltype to instantiate a model of type fcls modeltype this is not supported for all configurations of models and can yield errors return cls fromdictconfigdict kwargs class alignconfigpretrainedconfig r alignconfig is the configuration class to store the configuration of a alignmodel it is used to instantiate a align model according to the specified arguments defining the text model and vision model configs instantiating a configuration with the defaults will yield a similar configuration to that of the align kakaobrainalignbasehttps huggingface cokakaobrainalignbase architecture configuration objects inherit from pretrainedconfig and can be used to control the model outputs read the documentation from pretrainedconfig for more information args textconfig dict optional dictionary of configuration options used to initialize aligntextconfig visionconfig dict optional dictionary of configuration options used to initialize alignvisionconfig projectiondim int optional defaults to 640 dimentionality of text and vision projection layers temperatureinitvalue float optional defaults to 1 0 the inital value of the temperature paramter default is used as per the original align implementation initializerrange float optional defaults to 0 02 the standard deviation of the truncatednormalinitializer for initializing all weight matrices kwargs optional dictionary of keyword arguments example python from transformers import alignconfig alignmodel initializing a alignconfig with kakaobrainalignbase style configuration configuration alignconfig initializing a alignmodel with random weights from the kakaobrainalignbase style configuration model alignmodelconfiguration accessing the model configuration configuration model config we can also initialize a alignconfig from a aligntextconfig and a alignvisionconfig from transformers import aligntextconfig alignvisionconfig initializing align text and vision configurations configtext aligntextconfig configvision alignvisionconfig config alignconfig fromtextvisionconfigsconfigtext configvision modeltype align def init self textconfignone visionconfignone projectiondim640 temperatureinitvalue1 0 initializerrange0 02 kwargs super initkwargs if textconfig is none textconfig logger infotextconfig is none initializing the aligntextconfig with default values if visionconfig is none visionconfig logger infovisionconfig is none initializing the alignvisionconfig with default values self textconfig aligntextconfigtextconfig self visionconfig alignvisionconfigvisionconfig self projectiondim projectiondim self temperatureinitvalue temperatureinitvalue self initializerrange initializerrange classmethod def fromtextvisionconfigscls textconfig aligntextconfig visionconfig alignvisionconfig kwargs r instantiate a alignconfig or a derived class from align text model configuration and align vision model configuration returns alignconfig an instance of a configuration object 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 align model configuration this is the configuration class to store the configuration of a aligntextmodel it is used to instantiate a align text encoder according to the specified arguments defining the model architecture instantiating a configuration with the defaults will yield a similar configuration to that of the text encoder of the align kakaobrain align base https huggingface co kakaobrain align base architecture the default values here are copied from bert 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 align text model defines the number of different tokens that can be represented by the inputs_ids passed when calling aligntextmodel 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 aligntextmodel 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 pad_token_id int optional defaults to 0 padding token id position_embedding_type str optional defaults to absolute type of position embedding choose one of absolute relative_key relative_key_query for positional embeddings use absolute for more information on relative_key please refer to self attention with relative position representations shaw et al https arxiv org abs 1803 02155 for more information on relative_key_query please refer to method 4 in improve transformer models with better relative position embeddings huang et al https arxiv org abs 2009 13658 use_cache bool optional defaults to true whether or not the model should return the last key values attentions not used by all models only relevant if config is_decoder true example python from transformers import aligntextconfig aligntextmodel initializing a aligntextconfig with kakaobrain align base style configuration configuration aligntextconfig initializing a aligntextmodel with random weights from the kakaobrain align base style configuration model aligntextmodel configuration accessing the model configuration configuration model config get the text config dict if we are loading from alignconfig this is the configuration class to store the configuration of a alignvisionmodel it is used to instantiate a align vision encoder according to the specified arguments defining the model architecture instantiating a configuration with the defaults will yield a similar configuration to that of the vision encoder of the align kakaobrain align base https huggingface co kakaobrain align base architecture the default values are copied from efficientnet efficientnet b7 configuration objects inherit from pretrainedconfig and can be used to control the model outputs read the documentation from pretrainedconfig for more information args num_channels int optional defaults to 3 the number of input channels image_size int optional defaults to 600 the input image size width_coefficient float optional defaults to 2 0 scaling coefficient for network width at each stage depth_coefficient float optional defaults to 3 1 scaling coefficient for network depth at each stage depth_divisor int optional defaults to 8 a unit of network width kernel_sizes list int optional defaults to 3 3 5 3 5 5 3 list of kernel sizes to be used in each block in_channels list int optional defaults to 32 16 24 40 80 112 192 list of input channel sizes to be used in each block for convolutional layers out_channels list int optional defaults to 16 24 40 80 112 192 320 list of output channel sizes to be used in each block for convolutional layers depthwise_padding list int optional defaults to list of block indices with square padding strides list int optional defaults to 1 2 2 2 1 2 1 list of stride sizes to be used in each block for convolutional layers num_block_repeats list int optional defaults to 1 2 2 3 3 4 1 list of the number of times each block is to repeated expand_ratios list int optional defaults to 1 6 6 6 6 6 6 list of scaling coefficient of each block squeeze_expansion_ratio float optional defaults to 0 25 squeeze expansion ratio hidden_act str or function optional defaults to silu the non linear activation function function or string in each block if string gelu relu selu gelu_new silu and mish are supported hiddem_dim int optional defaults to 1280 the hidden dimension of the layer before the classification head pooling_type str or function optional defaults to mean type of final pooling to be applied before the dense classification head available options are mean max initializer_range float optional defaults to 0 02 the standard deviation of the truncated_normal_initializer for initializing all weight matrices batch_norm_eps float optional defaults to 1e 3 the epsilon used by the batch normalization layers batch_norm_momentum float optional defaults to 0 99 the momentum used by the batch normalization layers drop_connect_rate float optional defaults to 0 2 the drop rate for skip connections example python from transformers import alignvisionconfig alignvisionmodel initializing a alignvisionconfig with kakaobrain align base style configuration configuration alignvisionconfig initializing a alignvisionmodel with random weights from the kakaobrain align base style configuration model alignvisionmodel configuration accessing the model configuration configuration model config get the vision config dict if we are loading from alignconfig alignconfig is the configuration class to store the configuration of a alignmodel it is used to instantiate a align model according to the specified arguments defining the text model and vision model configs instantiating a configuration with the defaults will yield a similar configuration to that of the align kakaobrain align base https huggingface co kakaobrain align 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 text_config dict optional dictionary of configuration options used to initialize aligntextconfig vision_config dict optional dictionary of configuration options used to initialize alignvisionconfig projection_dim int optional defaults to 640 dimentionality of text and vision projection layers temperature_init_value float optional defaults to 1 0 the inital value of the temperature paramter default is used as per the original align implementation initializer_range float optional defaults to 0 02 the standard deviation of the truncated_normal_initializer for initializing all weight matrices kwargs optional dictionary of keyword arguments example python from transformers import alignconfig alignmodel initializing a alignconfig with kakaobrain align base style configuration configuration alignconfig initializing a alignmodel with random weights from the kakaobrain align base style configuration model alignmodel configuration accessing the model configuration configuration model config we can also initialize a alignconfig from a aligntextconfig and a alignvisionconfig from transformers import aligntextconfig alignvisionconfig initializing align text and vision configurations config_text aligntextconfig config_vision alignvisionconfig config alignconfig from_text_vision_configs config_text config_vision instantiate a alignconfig or a derived class from align text model configuration and align vision model configuration returns alignconfig an instance of a configuration object
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
    pass

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


logger = logging.get_logger(__name__)

ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "kakaobrain/align-base": "https://huggingface.co/kakaobrain/align-base/resolve/main/config.json",
}


class AlignTextConfig(PretrainedConfig):
    r

    model_type = "align_text_model"

    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,
        position_embedding_type="absolute",
        use_cache=True,
        **kwargs,
    ):
        super().__init__(**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.position_embedding_type = position_embedding_type
        self.use_cache = use_cache
        self.pad_token_id = pad_token_id

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
        cls._set_token_in_kwargs(kwargs)

        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)

        
        if config_dict.get("model_type") == "align":
            config_dict = config_dict["text_config"]

        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
            logger.warning(
                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
            )

        return cls.from_dict(config_dict, **kwargs)


class AlignVisionConfig(PretrainedConfig):
    r

    model_type = "align_vision_model"

    def __init__(
        self,
        num_channels: int = 3,
        image_size: int = 600,
        width_coefficient: float = 2.0,
        depth_coefficient: float = 3.1,
        depth_divisor: int = 8,
        kernel_sizes: List[int] = [3, 3, 5, 3, 5, 5, 3],
        in_channels: List[int] = [32, 16, 24, 40, 80, 112, 192],
        out_channels: List[int] = [16, 24, 40, 80, 112, 192, 320],
        depthwise_padding: List[int] = [],
        strides: List[int] = [1, 2, 2, 2, 1, 2, 1],
        num_block_repeats: List[int] = [1, 2, 2, 3, 3, 4, 1],
        expand_ratios: List[int] = [1, 6, 6, 6, 6, 6, 6],
        squeeze_expansion_ratio: float = 0.25,
        hidden_act: str = "swish",
        hidden_dim: int = 2560,
        pooling_type: str = "mean",
        initializer_range: float = 0.02,
        batch_norm_eps: float = 0.001,
        batch_norm_momentum: float = 0.99,
        drop_connect_rate: float = 0.2,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.num_channels = num_channels
        self.image_size = image_size
        self.width_coefficient = width_coefficient
        self.depth_coefficient = depth_coefficient
        self.depth_divisor = depth_divisor
        self.kernel_sizes = kernel_sizes
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.depthwise_padding = depthwise_padding
        self.strides = strides
        self.num_block_repeats = num_block_repeats
        self.expand_ratios = expand_ratios
        self.squeeze_expansion_ratio = squeeze_expansion_ratio
        self.hidden_act = hidden_act
        self.hidden_dim = hidden_dim
        self.pooling_type = pooling_type
        self.initializer_range = initializer_range
        self.batch_norm_eps = batch_norm_eps
        self.batch_norm_momentum = batch_norm_momentum
        self.drop_connect_rate = drop_connect_rate
        self.num_hidden_layers = sum(num_block_repeats) * 4

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
        cls._set_token_in_kwargs(kwargs)

        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)

        
        if config_dict.get("model_type") == "align":
            config_dict = config_dict["vision_config"]

        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
            logger.warning(
                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
            )

        return cls.from_dict(config_dict, **kwargs)


class AlignConfig(PretrainedConfig):
    r

    model_type = "align"

    def __init__(
        self,
        text_config=None,
        vision_config=None,
        projection_dim=640,
        temperature_init_value=1.0,
        initializer_range=0.02,
        **kwargs,
    ):
        super().__init__(**kwargs)

        if text_config is None:
            text_config = {}
            logger.info("text_config is None. Initializing the AlignTextConfig with default values.")

        if vision_config is None:
            vision_config = {}
            logger.info("vision_config is None. Initializing the AlignVisionConfig with default values.")

        self.text_config = AlignTextConfig(**text_config)
        self.vision_config = AlignVisionConfig(**vision_config)

        self.projection_dim = projection_dim
        self.temperature_init_value = temperature_init_value
        self.initializer_range = initializer_range

    @classmethod
    def from_text_vision_configs(cls, text_config: AlignTextConfig, vision_config: AlignVisionConfig, **kwargs):
        r

        return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs)