rhS`dZddlmZddlmZej eZGddeZdgZ y)zTimesFM model configuration)PretrainedConfig)loggingc%eZdZdZdZgZdZdddddd d d d d d gd ddddddfdededededededededededede eded ed!e d"ed#ed$ef$fd% Z xZ S)& TimesFmConfiga% This is the configuration class to store the configuration of a [`TimesFmModelForPrediction`] or a [`TFTimesFmModel`]. It is used to instantiate a TimesFM 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 TimesFM [google/timesfm-2.0-500m-pytorch](https://huggingface.co/google/timesfm-2.0-500m-pytorch) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Arguments: patch_length (`int`, *optional*, defaults to 32): The length of one patch in the input sequence. context_length (`int`, *optional*, defaults to 512): The length of the input context. horizon_length (`int`, *optional*, defaults to 128): The length of the prediction horizon. freq_size (`int`, *optional*, defaults to 3): The number of frequency embeddings. num_hidden_layers (`int`, *optional*, defaults to 50): Number of Transformer layers. hidden_size (`int`, *optional*, defaults to 1280): Size of the hidden layers in the feed-forward networks. intermediate_size (`int`, *optional*, defaults to 1280): Dimension of the MLP representations. head_dim (`int`, *optional*, defaults to 80): Size of the key, query, value projections per attention head. The `inner_dim` of the projection layer will be defined as `num_attention_heads * head_dim`. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. tolerance (`float`, *optional*, defaults to 1e-06): The tolerance for the quantile loss. rms_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the RMS normalization layers. quantiles (`list[float]`, *optional*, defaults to `[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]`): The quantiles to predict. pad_val (`float`, *optional*, defaults to 1123581321.0): The value used to pad the predictions. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout probability for the attention scores. use_positional_embedding (`bool`, *optional*, defaults to `False`): Whether to add positional embeddings. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. min_timescale (`int`, *optional*, defaults to 1): The start of the geometric positional index. Determines the periodicity of the added signal. max_timescale (`int`, *optional*, defaults to 10000): The end of the geometric positional index. Determines the frequency of the added signal. timesfmF ir2iPgư>) g?g?g333333?g?g?g333333?gffffff?g?g?g@bAgg{Gz?i' patch_lengthcontext_lengthhorizon_length freq_sizenum_hidden_layers hidden_sizeintermediate_sizehead_dimnum_attention_heads tolerance rms_norm_eps quantilespad_valattention_dropoutuse_positional_embeddinginitializer_range min_timescale max_timescalec 8||_||_||_| |_| |_||_||_||_||_||_ | |_ | |_ | |_ ||_ ||_||_||_||_t%|Ldd|j(i|y)Nis_encoder_decoder)rrrrrrrrrrrrrrrrrrsuper__init__r!)selfrrrrrrrrrrrrrrrrrrkwargs __class__s /var/www/html/ai-insurance-compliance-backend/venv/lib/python3.12/site-packages/transformers/models/timesfm/configuration_timesfm.pyr$zTimesFmConfig.__init__Ps,),," "&!2  !2#6 "(!2(@%!2**  #66  ) __name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inferencer!intfloatlistboolr$ __classcell__)r's@r(rrs1fJ"$!!!#!%#%"!N%#&).#'#', , ,  ,  ,  , , , , !, , , ;, , !, #'!, "!#, $%, &', , r)rN) r-configuration_utilsrutilsr get_loggerr*loggerr__all__r"r)r(r:s>"3   H %d $d N  r)