Zj !SrSSKJr SSKJr SSKJrJr \R"\ 5r \"SS9\"SS \555r \"SS9\"S S \555r \"SS9\"S S \555r /SQrg)zALIGN model configuration)strict)PreTrainedConfig)auto_docstringloggingzkakaobrain/align-base) checkpointc\rSrSr%SrSrSrSr\\ S'Sr \\ S'S r \\ S 'S r \\ S 'S r \\ S 'Sr\\ S'Sr\\-\ S'Sr\\-\ S'Sr\\ S'Sr\\ S'Sr\\ S'Sr\\ S'Sr\S-\ S'Sr\S-\ S'Sr\\\-S-\ S'S rg)!AlignTextConfiga 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_configi:w vocab_sizei hidden_size num_hidden_layersnum_attention_headsi intermediate_sizegelu hidden_actg?hidden_dropout_probattention_probs_dropout_probimax_position_embeddingstype_vocab_size{Gz?initializer_rangeg-q=layer_norm_epsrN pad_token_id bos_token_id eos_token_id)__name__ __module__ __qualname____firstlineno____doc__ model_typebase_config_keyrint__annotations__rrrrrstrrfloatrrrrrrrr list__static_attributes__r!~/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/align/configuration_align.pyr r s $J#OJKs!!!s!J'**03 %#+3#&S&OS#u#!NE! L#* #L#*#+/L#S /D(/r/r c^\rSrSr%SrSrSrSr\\ S'Sr \\ \-\ \\4-\ S'S r \\ S 'S r\\ S 'S r\\ S'Sr\ \\ \S4-\ S'Sr\ \\ \S4-\ S'Sr\ \\ \S4-\ S'Sr\ \ \S4-\ S'Sr\ \\ \S4-\ S'Sr\ \\ \S4-\ S'Sr\ \\ \S4-\ S'Sr\\ S'S r\\ S!'S"r\\ S#'S$r\\ S%'S&r\\ S''S(r\\ S)'S*r\\ S+'S,r \\-\ S-'U4S.jr!S/r"U=r#$)0AlignVisionConfig@aC 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_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"`] 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_configr num_channelsiX image_sizeg@width_coefficientg@depth_coefficient depth_divisor)rrrr<r<r. kernel_sizes) (Pp in_channels)r?r@rArBrCrDi@ out_channelsr!depthwise_padding)rrrrHrrHstrides)rHrrrrrHnum_block_repeats)rHrLrLrLrLrL expand_ratiosg?squeeze_expansion_ratioswishri hidden_dimmean pooling_typerrgMbP?batch_norm_epsgGz?batch_norm_momentumg?drop_connect_ratec >[UR5S-UlSH!n[X[ [ X555 M# [ TU]"S0UD6 g)NrJ)r=rErFrGrIrKrMr!)sumrKrsetattrr-getattrsuper __post_init__)selfkwargsattr __class__s r0r[AlignVisionConfig.__post_init__sO!$T%;%;!>> 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(text_config=config_text, vision_config=config_vision) ```align)r r5Nr r5iprojection_dimg?temperature_init_valuerrc >URc%[5Ul[RS5 O9[ UR[ 5(a[S0URD6UlUR c%[5Ul[RS5 O9[ UR [ 5(a[S0UR D6Ul[TU]$"S0UD6 g)NzP`text_config` is `None`. Initializing the `AlignTextConfig` with default values.zT`vision_config` is `None`. initializing the `AlignVisionConfig` with default values.r!) r r loggerinfo isinstancedictr5r2rZr[)r\r]r_s r0r[AlignConfig.__post_init__s    #.0D  KKj k (($ / /.B1A1ABD     %!2!4D  KKn o **D 1 1!2!HT5G5G!HD  ''r/)r"r#r$r%r&r'r r2 sub_configsr rnrr*r5rhr)rir,rr[r.rbrcs@r0reresn 8J"1DUVK26K((4/648M4**T18NC$'E'#u# ( (r/re)r r2reN)r&huggingface_hub.dataclassesrconfiguration_utilsrutilsrr get_loggerr"rkr r2re__all__r!r/r0rvs .3,   H %23"0&"04"0J23V((V(4V(r233("3(43(l Br/