S^(h(tddlmZddlmZejeZGddeZGddeZddgZ y))PretrainedConfig)loggingcBeZdZdZdZdZ dfd ZxZS)GitVisionConfiga 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 ```git_vision_model vision_configc  t |di| ||_||_||_||_||_||_||_| |_ | |_ | |_ ||_ y)N) super__init__ hidden_sizeintermediate_sizenum_hidden_layersnum_attention_heads num_channels patch_size image_sizeinitializer_rangeattention_dropoutlayer_norm_eps hidden_act)selfr rrrrrrrrrrkwargs __class__s g/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/transformers/models/git/configuration_git.pyr zGitVisionConfig.__init__Ksj "6"&!2!2#6 ($$!2!2,$)  rr quick_gelugh㈵>g{Gz?)__name__ __module__ __qualname____doc__ model_typebase_config_keyr __classcell__rs@rrrsB- ^$J%O%%rrcVeZdZdZdZdeiZ dfd ZxZS) GitConfiga 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 ```gitrc lt|d||| d||i}tjdt di||_||_||_||_||_ ||_ ||_ ||_ | |_ | |_| |_| |_||_||_||_||_||_||_y)N) bos_token_id eos_token_id pad_token_idzLvision_config is None. initializing the GitVisionConfig with default values.r )r r loggerinforr vocab_sizer rrrrhidden_dropout_probattention_probs_dropout_probmax_position_embeddingsrrposition_embedding_type use_cachetie_word_embeddingsnum_image_with_embeddingr0r1)rrr5r rrrrr6r7r8rrr2r9r:r;r0r1r<rrs rr zGitConfig.__init__s. sl\hslrs  M KKf g,=}=$&!2#6 $!2#6 ,H)'>$!2,'>$"#6 (@%((r)Ni:wrrrgelu皙?r?ir#g-q=absoluteTFefN) r$r%r&r'r(r sub_configsr r*r+s@rr-r-is_= ~J"O4K%( $ *!!%)/)/)rr-N) configuration_utilsrutilsr get_loggerr$r3rr-__all__r rrrIsN"4   H %N%&N%br) r)j ) *r