a ¾ ÀhSã@s@dZddlmZddlmZe e¡ZGdd„deƒZdgZ dS)zTimesFM model configurationé)ÚPretrainedConfig)Úloggingcs‚eZdZdZdZgZdZdddddd d d d d d gd ¢ddddddfeeeeeeeeeeee eeee eeedœ‡fdd„ Z ‡Z S)Ú 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. ZtimesfmFé ié€ré2iéPégíµ ÷Æ°>) gš™™™™™¹?gš™™™™™É?g333333Ó?gš™™™™™Ù?gà?g333333ã?gffffffæ?gš™™™™™é?gÍÌÌÌÌÌì?g@b¾ÐAgg{®Gáz”?éi')Ú patch_lengthÚcontext_lengthÚhorizon_lengthÚ freq_sizeÚnum_hidden_layersÚ hidden_sizeÚintermediate_sizeÚhead_dimÚnum_attention_headsÚ toleranceÚ rms_norm_epsÚ quantilesÚpad_valÚattention_dropoutÚuse_positional_embeddingÚinitializer_rangeÚ min_timescaleÚ max_timescalec sˆ||_||_||_| |_| |_||_||_||_||_||_ | |_ | |_ | |_ ||_ ||_||_||_||_tƒjfd|ji|¤ŽdS)NÚis_encoder_decoder)r r r rrrrrrrrrrrrrrrÚsuperÚ__init__r)Úselfr r r rrrrrrrrrrrrrrrÚkwargs©Ú __class__©úm/var/www/html/assistant/venv/lib/python3.9/site-packages/transformers/models/timesfm/configuration_timesfm.pyrPs. ÿþzTimesFmConfig.__init__) Ú__name__Ú __module__Ú __qualname__Ú__doc__Z model_typeZkeys_to_ignore_at_inferencerÚintÚfloatÚlistÚboolrÚ __classcell__r$r$r"r%rsT3íírN) r)Zconfiguration_utilsrÚutilsrZ get_loggerr&ÚloggerrÚ__all__r$r$r$r%Ús    g