rh>dZddlmZddlmZej eZGddeZGddeZ Gdd eZ gd Z y ) zALIGN model configuration)PretrainedConfig)loggingcJeZdZdZdZdZ dfd ZxZS)AlignTextConfiga| This is the configuration class to store the configuration of a [`AlignTextModel`]. It is used to instantiate a ALIGN text 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 text encoder of the ALIGN [kakaobrain/align-base](https://huggingface.co/kakaobrain/align-base) architecture. The default values here are copied from BERT. 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 30522): Vocabulary size of the Align Text model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`AlignTextModel`]. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. 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. 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 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). type_vocab_size (`int`, *optional*, defaults to 2): The vocabulary size of the `token_type_ids` passed when calling [`AlignTextModel`]. 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. pad_token_id (`int`, *optional*, defaults to 0): Padding token id. 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://huggingface.co/papers/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://huggingface.co/papers/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`. Example: ```python >>> from transformers import AlignTextConfig, AlignTextModel >>> # Initializing a AlignTextConfig with kakaobrain/align-base style configuration >>> configuration = AlignTextConfig() >>> # Initializing a AlignTextModel (with random weights) from the kakaobrain/align-base style configuration >>> model = AlignTextModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```align_text_model text_configc t|di|||_||_||_||_||_||_||_||_ | |_ | |_ | |_ | |_ ||_||_| |_y)N)super__init__ vocab_size hidden_sizenum_hidden_layersnum_attention_heads hidden_actintermediate_sizehidden_dropout_probattention_probs_dropout_probmax_position_embeddingstype_vocab_sizeinitializer_rangelayer_norm_epsposition_embedding_type use_cache pad_token_id)selfr rrrrrrrrrrrrrrkwargs __class__s /var/www/html/ai-insurance-compliance-backend/venv/lib/python3.12/site-packages/transformers/models/align/configuration_align.pyr zAlignTextConfig.__init__]s& "6"$&!2#6 $!2#6 ,H)'>$.!2,'>$"()i:wi r!i gelu皙?r#i{Gz?g-q=absoluteT)__name__ __module__ __qualname____doc__ model_typebase_config_keyr __classcell__rs@rrrsN? B$J#O%( # *!#)#)r rc)eZdZdZdZdZdddddgd gd gd ggd gd gdddddddddfdedededededeedeedeedeed eed!eed"eed#ed$e d%ed&e d'ed(ed)ed*ef(fd+ Z xZ S),AlignVisionConfiga  This is the configuration class to store the configuration of a [`AlignVisionModel`]. It is used to instantiate a ALIGN 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 ALIGN [kakaobrain/align-base](https://huggingface.co/kakaobrain/align-base) architecture. The default values are copied from EfficientNet (efficientnet-b7) Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: num_channels (`int`, *optional*, defaults to 3): The number of input channels. image_size (`int`, *optional*, defaults to 600): The input image size. width_coefficient (`float`, *optional*, defaults to 2.0): Scaling coefficient for network width at each stage. depth_coefficient (`float`, *optional*, defaults to 3.1): Scaling coefficient for network depth at each stage. depth_divisor `int`, *optional*, defaults to 8): A unit of network width. kernel_sizes (`list[int]`, *optional*, defaults to `[3, 3, 5, 3, 5, 5, 3]`): List of kernel sizes to be used in each block. in_channels (`list[int]`, *optional*, defaults to `[32, 16, 24, 40, 80, 112, 192]`): List of input channel sizes to be used in each block for convolutional layers. out_channels (`list[int]`, *optional*, defaults to `[16, 24, 40, 80, 112, 192, 320]`): List of output channel sizes to be used in each block for convolutional layers. depthwise_padding (`list[int]`, *optional*, defaults to `[]`): List of block indices with square padding. strides (`list[int]`, *optional*, defaults to `[1, 2, 2, 2, 1, 2, 1]`): List of stride sizes to be used in each block for convolutional layers. num_block_repeats (`list[int]`, *optional*, defaults to `[1, 2, 2, 3, 3, 4, 1]`): List of the number of times each block is to repeated. expand_ratios (`list[int]`, *optional*, defaults to `[1, 6, 6, 6, 6, 6, 6]`): List of scaling coefficient of each block. squeeze_expansion_ratio (`float`, *optional*, defaults to 0.25): Squeeze expansion ratio. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in each block. If string, `"gelu"`, `"relu"`, `"selu", `"gelu_new"`, `"silu"` and `"mish"` are supported. hidden_dim (`int`, *optional*, defaults to 1280): The hidden dimension of the layer before the classification head. pooling_type (`str` or `function`, *optional*, defaults to `"mean"`): Type of final pooling to be applied before the dense classification head. Available options are [`"mean"`, `"max"`] initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. batch_norm_eps (`float`, *optional*, defaults to 1e-3): The epsilon used by the batch normalization layers. batch_norm_momentum (`float`, *optional*, defaults to 0.99): The momentum used by the batch normalization layers. drop_connect_rate (`float`, *optional*, defaults to 0.2): The drop rate for skip connections. Example: ```python >>> from transformers import AlignVisionConfig, AlignVisionModel >>> # Initializing a AlignVisionConfig with kakaobrain/align-base style configuration >>> configuration = AlignVisionConfig() >>> # Initializing a AlignVisionModel (with random weights) from the kakaobrain/align-base style configuration >>> model = AlignVisionModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```align_vision_model vision_configriXg@g@)rrrr5r5r) (Pp)r7r8r9r:r;r<i@)r$r$r$r=r$r=)r=r$r$rrr=)r=r?r?r?r?r?g?swishi meanr%gMbP?gGz?g? num_channels image_sizewidth_coefficientdepth_coefficient depth_divisor kernel_sizes in_channels out_channelsdepthwise_paddingstridesnum_block_repeats expand_ratiossqueeze_expansion_ratior hidden_dim pooling_typerbatch_norm_epsbatch_norm_momentumdrop_connect_ratec bt|di|||_||_||_||_||_||_||_||_ | |_ | |_ | |_ | |_ | |_||_||_||_||_||_||_||_t-| dz|_y)Nr>r )r r rBrCrDrErFrGrHrIrJrKrLrMrNrrOrPrrQrRrSsumr)rrBrCrDrErFrGrHrIrJrKrLrMrNrrOrPrrQrRrSrrs rr zAlignVisionConfig.__init__s0 "6"($!2!2*(&(!2 !2*'>$$$(!2,#6 !2!$%6!7!!;r ) r(r)r*r+r,r-intfloatliststrr r.r/s@rr1r1s@C J&J%O#&#&"7!?"A')2'<#8)-!"#' %%)#&+.<.<.<! .< ! .<  .<3i.<#Y.<3i.< 9.<c.< 9.<Cy.<"'.<.< !.<"#.<$!%.<&'.<(#).<*!+.<.>> from transformers import AlignConfig, AlignModel >>> # Initializing a AlignConfig with kakaobrain/align-base style configuration >>> configuration = AlignConfig() >>> # Initializing a AlignModel (with random weights) from the kakaobrain/align-base style configuration >>> model = AlignModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config >>> # We can also initialize a AlignConfig from a AlignTextConfig and a AlignVisionConfig >>> from transformers import AlignTextConfig, AlignVisionConfig >>> # Initializing ALIGN Text and Vision configurations >>> config_text = AlignTextConfig() >>> config_vision = AlignVisionConfig() >>> config = AlignConfig.from_text_vision_configs(config_text, config_vision) ```align)rr3c t|di||i}tjd|i}tjdt di||_t di||_||_||_ ||_ y)NzJtext_config is None. Initializing the AlignTextConfig with default values.zNvision_config is None. Initializing the AlignVisionConfig with default values.r ) r r loggerinforrr1r3projection_dimtemperature_init_valuer)rrr3r`rarrrs rr zAlignConfig.__init__0s} "6"  K KKd e  M KKh i*9[9.??,&<#!2r )NNig?r%) r(r)r*r+r,rr1 sub_configsr r.r/s@rr[r[s6- ^J"1DUVK" 33r r[)rr1r[N) r+configuration_utilsrutilsr get_loggerr(r^rr1r[__all__r r rrgs\ 3   H %h)&h)Vw<(w