codingutf8 2022 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 get the vision config dict if we are loading from gitconfig coding utf 8 2022 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 this is the configuration class to store the configuration of a gitvisionmodel it is used to instantiate a git 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 git microsoft git base https huggingface co microsoft git 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 hidden_size int optional defaults to 768 dimensionality of the encoder layers and the pooler layer intermediate_size int optional defaults to 3072 dimensionality of the intermediate i e feed forward layer in the transformer encoder 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 image_size int optional defaults to 224 the size resolution of each image patch_size int optional defaults to 16 the size resolution of each patch hidden_act str or function optional defaults to quick_gelu the non linear activation function function or string in the encoder and pooler if string gelu relu selu and gelu_new quick_gelu are supported layer_norm_eps float optional defaults to 1e 5 the epsilon used by the layer normalization layers attention_dropout float optional defaults to 0 0 the dropout ratio for the attention probabilities initializer_range float optional defaults to 0 02 the standard deviation of the truncated_normal_initializer for initializing all weight matrices example python from transformers import gitvisionconfig gitvisionmodel initializing a gitvisionconfig with microsoft git base style configuration configuration gitvisionconfig initializing a gitvisionmodel with random weights from the microsoft git base style configuration model gitvisionmodel configuration accessing the model configuration configuration model config get the vision config dict if we are loading from gitconfig this is the configuration class to store the configuration of a gitmodel it is used to instantiate a git 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 git microsoft git base https huggingface co microsoft git 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 vision_config dict optional dictionary of configuration options used to initialize gitvisionconfig vocab_size int optional defaults to 30522 vocabulary size of the git model defines the number of different tokens that can be represented by the inputs_ids passed when calling gitmodel hidden_size int optional defaults to 768 dimensionality of the encoder layers and the pooler layer num_hidden_layers int optional defaults to 6 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 1024 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 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 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 num_image_with_embedding int optional the number of temporal embeddings to add in case the model is used for video captioning vqa examples python from transformers import gitconfig gitmodel initializing a git microsoft git base style configuration configuration gitconfig initializing a model with random weights from the microsoft git base style configuration model gitmodel configuration accessing the model configuration configuration model config
import os
from typing import Union

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


logger = logging.get_logger(__name__)

GIT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/main/config.json",
}


class GitVisionConfig(PretrainedConfig):
    r

    model_type = "git_vision_model"

    def __init__(
        self,
        hidden_size=768,
        intermediate_size=3072,
        num_hidden_layers=12,
        num_attention_heads=12,
        num_channels=3,
        image_size=224,
        patch_size=16,
        hidden_act="quick_gelu",
        layer_norm_eps=1e-5,
        attention_dropout=0.0,
        initializer_range=0.02,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.num_channels = num_channels
        self.patch_size = patch_size
        self.image_size = image_size
        self.initializer_range = initializer_range
        self.attention_dropout = attention_dropout
        self.layer_norm_eps = layer_norm_eps
        self.hidden_act = hidden_act

    @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") == "git":
            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 GitConfig(PretrainedConfig):
    r

    model_type = "git"

    def __init__(
        self,
        vision_config=None,
        vocab_size=30522,
        hidden_size=768,
        num_hidden_layers=6,
        num_attention_heads=12,
        intermediate_size=3072,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=1024,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        pad_token_id=0,
        position_embedding_type="absolute",
        use_cache=True,
        tie_word_embeddings=False,
        bos_token_id=101,
        eos_token_id=102,
        num_image_with_embedding=None,
        **kwargs,
    ):
        super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, pad_token_id=pad_token_id, **kwargs)

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

        self.vision_config = GitVisionConfig(**vision_config)
        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.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.position_embedding_type = position_embedding_type
        self.use_cache = use_cache
        self.tie_word_embeddings = tie_word_embeddings
        self.num_image_with_embedding = num_image_with_embedding

        self.bos_token_id = bos_token_id
        self.eos_token_id = eos_token_id