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"""TimesFM model configuration"""

from huggingface_hub.dataclasses import strict

from ...configuration_utils import PreTrainedConfig
from ...utils import auto_docstring


@auto_docstring(checkpoint="google/timesfm-2.0-500m-pytorch")
@strict
class TimesFmConfig(PreTrainedConfig):
    r"""
    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.
    tolerance (`float`, *optional*, defaults to 1e-06):
        The tolerance for the quantile loss.
    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.
    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.
    """

    model_type = "timesfm"
    keys_to_ignore_at_inference = []
    is_encoder_decoder = False

    patch_length: int = 32
    context_length: int = 512
    horizon_length: int = 128
    freq_size: int = 3
    num_hidden_layers: int = 50
    hidden_size: int = 1280
    intermediate_size: int = 1280
    head_dim: int = 80
    num_attention_heads: int = 16
    tolerance: float = 1e-6
    rms_norm_eps: float = 1e-6
    quantiles: list[float] | tuple[float, ...] = (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9)
    pad_val: float = 1123581321.0
    attention_dropout: float | int = 0.0
    use_positional_embedding: bool = False
    initializer_range: float = 0.02
    min_timescale: int = 1
    max_timescale: int = 10_000


__all__ = ["TimesFmConfig"]