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    þÍ:j·  ã            	       ó|   — d dl mZ d dlZd dlmZ d dlmZ dededeeeef   fd„Zd	ed
ededefd„Zdededefd„Z	y)é    )ÚTupleN)ÚTensor)Ú_check_same_shapeÚpredsÚtargetÚreturnc                 ó  — t        | dd…df   |«       | j                  \  }}|dk  rt        d| j                  › d�«      ‚t        j                  | d¬«      d   } |j                  d«      j                  | «      }t        j                  t        j                  | |z
  «      d¬«      |z  }t        j                  | j                  d«      | j                  d«      z
  «      }t        j                  |d¬«      d|z  |z  z  }|||fS )	aC  Compute intermediate CRPS values before aggregation.

    Args:
        preds: Tensor of shape (batch_size, ensemble_members)
        target: Tensor of shape (batch_size,)

    Returns:
        batch_size: int
        diff: Tensor (batch-wise absolute error term)
        ensemble_sum: Tensor (pairwise ensemble term)

    Nr   é   z<CRPS requires at least 2 ensemble members, but you provided ú.é   )Údim)r   r
   )	r   ÚshapeÚ
ValueErrorÚtorchÚsortÚ	unsqueezeÚ	expand_asÚsumÚabs)r   r   Ú
batch_sizeÚn_ensemble_membersÚobservation_inflatedÚdiffÚensemble_diffsÚensemble_sums           ú|/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/torchmetrics/functional/regression/crps.pyÚ_crps_updater      sü   € ô �ešA˜q˜D‘k 6Ô*à%*§[¡[Ñ"€JÐ"Ø˜AÒÜÐWÐX]×XcÑXcÐWdÐdeÐfÓgÐgô �J‰J�u !Ô$ QÑ'€Eð "×+Ñ+¨AÓ.×8Ñ8¸Ó?Ðô �9‰9”U—Y‘Y˜uÐ';Ñ;Ó<À!ÔDÐGYÑY€Dô —Y‘Y˜uŸ™¨qÓ1°E·O±OÀAÓ4FÑFÓG€NÜ—9‘9˜^°Ô8¸AÐ@RÑ<RÐUgÑ<gÑh€Là�t˜\Ð)Ð)ó    r   r   r   c                 ó2   — t        j                  ||z
  «      S )zFinal CRPS computation.)r   Úmean)r   r   r   s      r   Ú_crps_computer!   ;   s   € ä�:‰:�d˜\Ñ)Ó*Ð*r   c                 ó<   — t        | |«      \  }}}t        |||«      S )a2  Computes continuous ranked probability score.

    .. math::
        CRPS(F, y) = \int_{-\infty}^{\infty} (F(x) - 1_{x \geq y})^2 dx

    where :math:`F` is the predicted cumulative distribution function and :math:`y` is the true target. The metric is
    usually used to evaluate probabilistic regression models, such as forecasting models. A lower CRPS indicates a
    better forecast, meaning that forecasted probabilities are closer to the true observed values. CRPS can also be
    seen as a generalization of the brier score for non binary classification problems.

    Args:
        preds: a 2d tensor of shape (batch_size, ensemble_members) with predictions. The second dimension represents
            the ensemble members.
        target: a 1d tensor of shape (batch_size) with the target values.

    Return:
        Tensor with CRPS

    Raises:
        ValueError:
            If the number of ensemble members is less than 2.
        ValueError:
            If the first dimension of preds and target do not match.

    Example::
        >>> from torchmetrics.functional.regression import continuous_ranked_probability_score
        >>> from torch import randn
        >>> preds = randn(10, 5)
        >>> target = randn(10)
        >>> continuous_ranked_probability_score(preds, target)
        tensor(0.7731)

    )r   r!   )r   r   r   r   r   s        r   Ú#continuous_ranked_probability_scorer#   @   s'   € ôD &2°%¸Ó%@Ñ"€J��lÜ˜ T¨<Ó8Ð8r   )
Útypingr   r   r   Útorchmetrics.utilities.checksr   Úintr   r!   r#   © r   r   ú<module>r(      su   ðõ ã Ý å ;ð!*˜ð !*¨ð !*°5¸¸fÀfÐ9LÑ3Mó !*ðH+˜cð +¨ð +¸vð +È&ó +ð
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