# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # 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""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) from ..deprecated._archive_maps import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402 class TrOCRConfig(PretrainedConfig): 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 [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 ```""" 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, )