S^(hrdZddlmZddlmZddlmZmZeje Z GddeeZ dgZ y)zBiT model configuration)PretrainedConfig)logging)BackboneConfigMixin*get_aligned_output_features_output_indicesc\eZdZdZdZddgZddgZddgd gd dd d d ddd dd d ffd ZxZS) BitConfiga This is the configuration class to store the configuration of a [`BitModel`]. It is used to instantiate an BiT 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 BiT [google/bit-50](https://huggingface.co/google/bit-50) architecture. 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. embedding_size (`int`, *optional*, defaults to 64): Dimensionality (hidden size) for the embedding layer. hidden_sizes (`List[int]`, *optional*, defaults to `[256, 512, 1024, 2048]`): Dimensionality (hidden size) at each stage. depths (`List[int]`, *optional*, defaults to `[3, 4, 6, 3]`): Depth (number of layers) for each stage. layer_type (`str`, *optional*, defaults to `"preactivation"`): The layer to use, it can be either `"preactivation"` or `"bottleneck"`. hidden_act (`str`, *optional*, defaults to `"relu"`): The non-linear activation function in each block. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. global_padding (`str`, *optional*): Padding strategy to use for the convolutional layers. Can be either `"valid"`, `"same"`, or `None`. num_groups (`int`, *optional*, defaults to 32): Number of groups used for the `BitGroupNormActivation` layers. drop_path_rate (`float`, *optional*, defaults to 0.0): The drop path rate for the stochastic depth. embedding_dynamic_padding (`bool`, *optional*, defaults to `False`): Whether or not to make use of dynamic padding for the embedding layer. output_stride (`int`, *optional*, defaults to 32): The output stride of the model. width_factor (`int`, *optional*, defaults to 1): The width factor for the model. out_features (`List[str]`, *optional*): If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc. (depending on how many stages the model has). If unset and `out_indices` is set, will default to the corresponding stages. If unset and `out_indices` is unset, will default to the last stage. Must be in the same order as defined in the `stage_names` attribute. out_indices (`List[int]`, *optional*): If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how many stages the model has). If unset and `out_features` is set, will default to the corresponding stages. If unset and `out_features` is unset, will default to the last stage. Must be in the same order as defined in the `stage_names` attribute. Example: ```python >>> from transformers import BitConfig, BitModel >>> # Initializing a BiT bit-50 style configuration >>> configuration = BitConfig() >>> # Initializing a model (with random weights) from the bit-50 style configuration >>> model = BitModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` bit preactivation bottleneckSAMEVALIDr@)iii)rrreluN gFc pt|d i|||jvr*td|ddj |j|<|j |j vr|j }ntd|d||_||_||_ ||_ ||_ ||_ ||_ ||_| |_| |_| |_| |_dgt'dt)|dzDcgc]}d| c}z|_t-| ||j* \|_|_ycc}w) Nz layer_type=z is not one of ,zPadding strategy z not supportedstemrstage) out_features out_indices stage_names)super__init__ layer_types ValueErrorjoinuppersupported_padding num_channelsembedding_size hidden_sizesdepths layer_type hidden_actglobal_padding num_groupsdrop_path_rateembedding_dynamic_padding output_stride width_factorrangelenrr _out_features _out_indices)selfr$r%r&r'r(r)r*r+r,r-r.r/rrkwargsidx __class__s g/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/transformers/models/bit/configuration_bit.pyrzBitConfig.__init__[s@$ "6" T-- -{:,ochhtO_O_F`Eabc c  %##%)?)??!/!5!5!7 #4^4DN!STT(,( $$,$,)B&*("8aVWX@Y&Zse}&ZZ0Z%;DL\L\1 -D-'[s7 D3) __name__ __module__ __qualname____doc__ model_typerr#r __classcell__)r7s@r8rrs[;zJ"L1K)+""'* * rN) r<configuration_utilsrutilsrutils.backbone_utilsrr get_loggerr9loggerr__all__rr?r8rFsC3c   H %l #%5l ^ -r?