U 4AfL@sndZddlZddlmZddlmZddlmZee Z GdddeZ Gd d d eZ Gd d d eZ dS) zAltCLIP model configurationN)Union)PretrainedConfig)loggingcs&eZdZdZdZdfdd ZZS)AltCLIPTextConfiga This is the configuration class to store the configuration of a [`AltCLIPTextModel`]. It is used to instantiate a AltCLIP text 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 AltCLIP [BAAI/AltCLIP](https://huggingface.co/BAAI/AltCLIP) 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 250002): Vocabulary size of the AltCLIP model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`AltCLIPTextModel`]. hidden_size (`int`, *optional*, defaults to 1024): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 24): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 4096): 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 514): 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 1): The vocabulary size of the `token_type_ids` passed when calling [`AltCLIPTextModel`] initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. initializer_factor (`float`, *optional*, defaults to 0.02): A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing). layer_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the layer normalization layers. pad_token_id (`int`, *optional*, defaults to 1): The id of the *padding* token. bos_token_id (`int`, *optional*, defaults to 0): The id of the *beginning-of-sequence* token. eos_token_id (`Union[int, List[int]]`, *optional*, defaults to 2): The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens. 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`. project_dim (`int`, *optional*, defaults to 768): The dimensions of the teacher model before the mapping layer. Examples: ```python >>> from transformers import AltCLIPTextModel, AltCLIPTextConfig >>> # Initializing a AltCLIPTextConfig with BAAI/AltCLIP style configuration >>> configuration = AltCLIPTextConfig() >>> # Initializing a AltCLIPTextModel (with random weights) from the BAAI/AltCLIP style configuration >>> model = AltCLIPTextModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```Zaltclip_text_modelgelu皙?{Gz?h㈵>rabsoluteTc s~tjf|||d|||_||_||_||_||_||_||_||_ | |_ | |_ | |_ | |_ | |_||_||_||_dS)N) pad_token_id bos_token_id eos_token_id)super__init__ vocab_size hidden_sizenum_hidden_layersnum_attention_heads hidden_actintermediate_sizehidden_dropout_probattention_probs_dropout_probmax_position_embeddingstype_vocab_sizeinitializer_rangeinitializer_factorlayer_norm_epsposition_embedding_type use_cache project_dim)selfrrrrrrr r!r"r#r$r%r&rrrr'r(r)kwargs __class__U/tmp/pip-unpacked-wheel-zw5xktn0/transformers/models/altclip/configuration_altclip.pyrfs"zAltCLIPTextConfig.__init__)rrr r r r r r rrrrrrrrrTr)__name__ __module__ __qualname____doc__ model_typer __classcell__r.r.r,r/rs,Hrc sDeZdZdZdZdfdd Zeeee j fddddZ Z S)AltCLIPVisionConfiga This is the configuration class to store the configuration of a [`AltCLIPModel`]. It is used to instantiate an AltCLIP 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 AltCLIP [BAAI/AltCLIP](https://huggingface.co/BAAI/AltCLIP) 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. projection_dim (`int`, *optional*, defaults to 512): Dimensionality of text and vision projection layers. 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. num_channels (`int`, *optional*, defaults to 3): The number of input channels. image_size (`int`, *optional*, defaults to 224): The size (resolution) of each image. patch_size (`int`, *optional*, defaults to 32): 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-05): 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. initializer_factor (`float`, *optional*, defaults to 1.0): A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing). Example: ```python >>> from transformers import AltCLIPVisionConfig, AltCLIPVisionModel >>> # Initializing a AltCLIPVisionConfig with BAAI/AltCLIP style configuration >>> configuration = AltCLIPVisionConfig() >>> # Initializing a AltCLIPVisionModel (with random weights) from the BAAI/AltCLIP style configuration >>> model = AltCLIPVisionModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```Zaltclip_vision_modelr  r quick_gelurr?c s`tjf|||_||_||_||_||_||_||_||_ | |_ | |_ | |_ | |_ | |_dS)N)rrrrprojection_dimrr num_channels patch_size image_sizer$r%attention_dropoutr&r)r*rrr?rrr@rBrArr&rCr$r%r+r,r.r/rszAltCLIPVisionConfig.__init__r)pretrained_model_name_or_pathreturncKs~|||j|f|\}}|ddkr2|d}d|krpt|drp|d|jkrptd|dd|jd|j|f|S)Nr4altclip vision_configzYou are using a model of type z to instantiate a model of type zN. This is not supported for all configurations of models and can yield errors.)Z_set_token_in_kwargsZget_config_dictgethasattrr4loggerwarning from_dict)clsrDr+Z config_dictr.r.r/from_pretraineds  z#AltCLIPVisionConfig.from_pretrained) rr7r8r9r9rr:r;r<rr=rr>) r0r1r2r3r4r classmethodrstrosPathLikerNr5r.r.r,r/r6s$7!r6cs:eZdZdZdZd fdd Zeeedd d Z Z S) AltCLIPConfiga This is the configuration class to store the configuration of a [`AltCLIPModel`]. It is used to instantiate an AltCLIP 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 AltCLIP [BAAI/AltCLIP](https://huggingface.co/BAAI/AltCLIP) 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 [`AltCLIPTextConfig`]. vision_config (`dict`, *optional*): Dictionary of configuration options used to initialize [`AltCLIPVisionConfig`]. projection_dim (`int`, *optional*, defaults to 768): Dimensionality of text and vision projection layers. logit_scale_init_value (`float`, *optional*, defaults to 2.6592): The initial value of the *logit_scale* parameter. Default is used as per the original CLIP implementation. kwargs (*optional*): Dictionary of keyword arguments. Example: ```python >>> from transformers import AltCLIPConfig, AltCLIPModel >>> # Initializing a AltCLIPConfig with BAAI/AltCLIP style configuration >>> configuration = AltCLIPConfig() >>> # Initializing a AltCLIPModel (with random weights) from the BAAI/AltCLIP style configuration >>> model = AltCLIPModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config >>> # We can also initialize a AltCLIPConfig from a AltCLIPTextConfig and a AltCLIPVisionConfig >>> # Initializing a AltCLIPText and AltCLIPVision configuration >>> config_text = AltCLIPTextConfig() >>> config_vision = AltCLIPVisionConfig() >>> config = AltCLIPConfig.from_text_vision_configs(config_text, config_vision) ```rFNr/L F@c  s|dd}|dd}tjf||dk r|dkr:i}tf|}|D]V\} } | |krP| || krP| dkrP| |krd| d| d} n d| d} t| qP|||dk rl|dkri}t f|} d | krd d | d D| d <| D]`\} } | |kr| || kr| dkr| |krHd| d | d} n d | d} t| q|| |dkri}td|dkri}tdtf||_ t f||_ ||_ ||_ d|_dS)Ntext_config_dictvision_config_dict)Ztransformers_version`zp` is found in both `text_config_dict` and `text_config` but with different values. The value `text_config_dict["z"]` will be used instead.zm`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The value `text_config["z"]` will be overridden.Zid2labelcSsi|]\}}t||qSr.)rP).0keyvaluer.r.r/ _sz*AltCLIPConfig.__init__..zv` is found in both `vision_config_dict` and `vision_config` but with different values. The value `vision_config_dict["zs`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. The value `vision_config["zR`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values.zV`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values.r>)poprrrto_dictitemsrJinfoupdater6 text_configrGr?logit_scale_init_valuer%) r*rarGr?rbr+rUrVZ_text_config_dictrYrZmessageZ_vision_config_dictr,r.r/r.sX       "         zAltCLIPConfig.__init__rarGcKs|f||d|S)z Instantiate a [`AltCLIPConfig`] (or a derived class) from altclip text model configuration and altclip vision model configuration. Returns: [`AltCLIPConfig`]: An instance of a configuration object rd)r])rMrarGr+r.r.r/from_text_vision_configss z&AltCLIPConfig.from_text_vision_configs)NNrrT) r0r1r2r3r4rrOrr6rer5r.r.r,r/rSs,XrS)r3rQtypingrZconfiguration_utilsrutilsrZ get_loggerr0rJrr6rSr.r.r.r/s    vn