codingutf8 2021 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 trocr model configuration from configurationutils import pretrainedconfig from utils import logging logger logging getloggername trocrpretrainedconfigarchivemap microsofttrocrbasehandwritten https huggingface comicrosofttrocrbasehandwrittenresolvemainconfig json see all trocr models at https huggingface comodels filtertrocr class trocrconfigpretrainedconfig r this is the configuration class to store the configuration of a trocrforcausallm it is used to instantiate an trocr 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 trocr microsofttrocrbasehandwrittenhttps huggingface comicrosofttrocrbasehandwritten 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 trocr model defines the number of different tokens that can be represented by the inputsids passed when calling trocrforcausallm dmodel int optional defaults to 1024 dimensionality of the layers and the pooler layer decoderlayers int optional defaults to 12 number of decoder layers 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 activationfunction str or function optional defaults to gelu the nonlinear activation function function or string in the pooler if string gelu relu silu and gelunew are supported 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 dropout float optional defaults to 0 1 the dropout probability for all fully connected layers in the embeddings 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 initstd float optional defaults to 0 02 the standard deviation of the truncatednormalinitializer for initializing all weight matrices 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 scaleembedding bool optional defaults to false whether or not to scale the word embeddings by sqrtdmodel uselearnedpositionembeddings bool optional defaults to true whether or not to use learned position embeddings if not sinusoidal position embeddings will be used layernormembedding bool optional defaults to true whether or not to use a layernorm after the word position embeddings example python from transformers import trocrconfig trocrforcausallm initializing a trocrbase style configuration configuration trocrconfig initializing a model with random weights from the trocrbase style configuration model trocrforcausallmconfiguration accessing the model configuration configuration model config modeltype trocr keystoignoreatinference pastkeyvalues attributemap numattentionheads decoderattentionheads hiddensize dmodel numhiddenlayers decoderlayers def init self vocabsize50265 dmodel1024 decoderlayers12 decoderattentionheads16 decoderffndim4096 activationfunctiongelu maxpositionembeddings512 dropout0 1 attentiondropout0 0 activationdropout0 0 decoderstarttokenid2 initstd0 02 decoderlayerdrop0 0 usecachetrue scaleembeddingfalse uselearnedpositionembeddingstrue layernormembeddingtrue padtokenid1 bostokenid0 eostokenid2 kwargs self vocabsize vocabsize self dmodel dmodel self decoderlayers decoderlayers self decoderattentionheads decoderattentionheads self decoderffndim decoderffndim self activationfunction activationfunction self maxpositionembeddings maxpositionembeddings self dropout dropout self attentiondropout attentiondropout self activationdropout activationdropout self initstd initstd self decoderlayerdrop decoderlayerdrop self usecache usecache self scaleembedding scaleembedding self uselearnedpositionembeddings uselearnedpositionembeddings self layernormembedding layernormembedding super init padtokenidpadtokenid bostokenidbostokenid eostokenideostokenid decoderstarttokeniddecoderstarttokenid kwargs coding utf 8 2021 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 trocr model configuration see all trocr models at https huggingface co models filter trocr this is the configuration class to store the configuration of a trocrforcausallm it is used to instantiate an trocr 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 trocr microsoft trocr base handwritten https huggingface co microsoft trocr base handwritten 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 trocr model defines the number of different tokens that can be represented by the inputs_ids passed when calling trocrforcausallm d_model int optional defaults to 1024 dimensionality of the layers and the pooler layer decoder_layers int optional defaults to 12 number of decoder layers 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 activation_function str or function optional defaults to gelu the non linear activation function function or string in the pooler if string gelu relu silu and gelu_new are supported 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 dropout float optional defaults to 0 1 the dropout probability for all fully connected layers in the embeddings 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 init_std float optional defaults to 0 02 the standard deviation of the truncated_normal_initializer for initializing all weight matrices 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 scale_embedding bool optional defaults to false whether or not to scale the word embeddings by sqrt d_model use_learned_position_embeddings bool optional defaults to true whether or not to use learned position embeddings if not sinusoidal position embeddings will be used layernorm_embedding bool optional defaults to true whether or not to use a layernorm after the word position embeddings example python from transformers import trocrconfig trocrforcausallm initializing a trocr base style configuration configuration trocrconfig initializing a model with random weights from the trocr base style configuration model trocrforcausallm configuration accessing the model configuration configuration model config
from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)

TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "microsoft/trocr-base-handwritten": (
        "https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
    ),
    
}


class TrOCRConfig(PretrainedConfig):
    r

    model_type = "trocr"
    keys_to_ignore_at_inference = ["past_key_values"]
    attribute_map = {
        "num_attention_heads": "decoder_attention_heads",
        "hidden_size": "d_model",
        "num_hidden_layers": "decoder_layers",
    }

    def __init__(
        self,
        vocab_size=50265,
        d_model=1024,
        decoder_layers=12,
        decoder_attention_heads=16,
        decoder_ffn_dim=4096,
        activation_function="gelu",
        max_position_embeddings=512,
        dropout=0.1,
        attention_dropout=0.0,
        activation_dropout=0.0,
        decoder_start_token_id=2,
        init_std=0.02,
        decoder_layerdrop=0.0,
        use_cache=True,
        scale_embedding=False,
        use_learned_position_embeddings=True,
        layernorm_embedding=True,
        pad_token_id=1,
        bos_token_id=0,
        eos_token_id=2,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.d_model = d_model
        self.decoder_layers = decoder_layers
        self.decoder_attention_heads = decoder_attention_heads
        self.decoder_ffn_dim = decoder_ffn_dim
        self.activation_function = activation_function
        self.max_position_embeddings = max_position_embeddings
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.activation_dropout = activation_dropout
        self.init_std = init_std
        self.decoder_layerdrop = decoder_layerdrop
        self.use_cache = use_cache
        self.scale_embedding = scale_embedding
        self.use_learned_position_embeddings = use_learned_position_embeddings
        self.layernorm_embedding = layernorm_embedding

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