text: # Copyright The PyTorch Lightning team.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  from typing import Any, List, Optional, Tuple, Union
  
  import torch
  from torch import Tensor
  
  from torchmetrics.functional.classification.precision_recall_curve import (
      _precision_recall_curve_compute,
      _precision_recall_curve_update,
  )
  from torchmetrics.metric import Metric
  from torchmetrics.utilities import rank_zero_warn
  from torchmetrics.utilities.data import dim_zero_cat
  
  
  class PrecisionRecallCurve(Metric):
      """Computes precision-recall pairs for different thresholds. Works for both binary and multiclass problems. In
      the case of multiclass, the values will be calculated based on a one-vs-the-rest approach.
  
      Forward accepts
  
      - ``preds`` (float tensor): ``(N, ...)`` (binary) or ``(N, C, ...)`` (multiclass) tensor
        with probabilities, where C is the number of classes.
  
      - ``target`` (long tensor): ``(N, ...)`` or ``(N, C, ...)`` with integer labels
  
      Args:
          num_classes: integer with number of classes for multi-label and multiclass problems.
              Should be set to ``None`` for binary problems
          pos_label: integer determining the positive class. Default is ``None``
              which for binary problem is translate to 1. For multiclass problems
              this argument should not be set as we iteratively change it in the
              range [0,num_classes-1]
          compute_on_step:
              Forward only calls ``update()`` and return None if this is set to False. default: True
          dist_sync_on_step:
              Synchronize metric state across processes at each ``forward()``
              before returning the value at the step. default: False
          process_group:
              Specify the process group on which synchronization is called. default: None (which selects the entire world)
  
      Example (binary case):
          >>> from torchmetrics import PrecisionRecallCurve
          >>> pred = torch.tensor([0, 1, 2, 3])
          >>> target = torch.tensor([0, 1, 1, 0])
          >>> pr_curve = PrecisionRecallCurve(pos_label=1)
          >>> precision, recall, thresholds = pr_curve(pred, target)
          >>> precision
          tensor([0.6667, 0.5000, 0.0000, 1.0000])
          >>> recall
          tensor([1.0000, 0.5000, 0.0000, 0.0000])
          >>> thresholds
          tensor([1, 2, 3])
  
      Example (multiclass case):
          >>> pred = torch.tensor([[0.75, 0.05, 0.05, 0.05, 0.05],
          ...                      [0.05, 0.75, 0.05, 0.05, 0.05],
          ...                      [0.05, 0.05, 0.75, 0.05, 0.05],
          ...                      [0.05, 0.05, 0.05, 0.75, 0.05]])
          >>> target = torch.tensor([0, 1, 3, 2])
          >>> pr_curve = PrecisionRecallCurve(num_classes=5)
          >>> precision, recall, thresholds = pr_curve(pred, target)
          >>> precision   # doctest: +NORMALIZE_WHITESPACE
          [tensor([1., 1.]), tensor([1., 1.]), tensor([0.2500, 0.0000, 1.0000]),
           tensor([0.2500, 0.0000, 1.0000]), tensor([0., 1.])]
          >>> recall
          [tensor([1., 0.]), tensor([1., 0.]), tensor([1., 0., 0.]), tensor([1., 0., 0.]), tensor([nan, 0.])]
          >>> thresholds
          [tensor([0.7500]), tensor([0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500])]
      """
  
      preds: List[Tensor]
      target: List[Tensor]
  
      def __init__(
          self,
          num_classes: Optional[int] = None,
          pos_label: Optional[int] = None,
          compute_on_step: bool = True,
          dist_sync_on_step: bool = False,
          process_group: Optional[Any] = None,
      ) -> None:
          super().__init__(
              compute_on_step=compute_on_step,
              dist_sync_on_step=dist_sync_on_step,
              process_group=process_group,
          )
  
          self.num_classes = num_classes
          self.pos_label = pos_label
  
          self.add_state("preds", default=[], dist_reduce_fx="cat")
          self.add_state("target", default=[], dist_reduce_fx="cat")
  
          rank_zero_warn(
              "Metric `PrecisionRecallCurve` will save all targets and predictions in buffer."
              " For large datasets this may lead to large memory footprint."
          )
  
      def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
          """Update state with predictions and targets.
  
          Args:
              preds: Predictions from model
              target: Ground truth values
          """
          preds, target, num_classes, pos_label = _precision_recall_curve_update(
              preds, target, self.num_classes, self.pos_label
          )
          self.preds.append(preds)
          self.target.append(target)
          self.num_classes = num_classes
          self.pos_label = pos_label
  
      def compute(self) -> Union[Tuple[Tensor, Tensor, Tensor], Tuple[List[Tensor], List[Tensor], List[Tensor]]]:
          """Compute the precision-recall curve.
  
          Returns:
              3-element tuple containing
  
              precision:
                  tensor where element i is the precision of predictions with
                  score >= thresholds[i] and the last element is 1.
                  If multiclass, this is a list of such tensors, one for each class.
              recall:
                  tensor where element i is the recall of predictions with
                  score >= thresholds[i] and the last element is 0.
                  If multiclass, this is a list of such tensors, one for each class.
              thresholds:
                  Thresholds used for computing precision/recall scores
          """
          preds = dim_zero_cat(self.preds)
          target = dim_zero_cat(self.target)
          if not self.num_classes:
              raise ValueError(f"`num_classes` bas to be positive number, but got {self.num_classes}")
          return _precision_recall_curve_compute(preds, target, self.num_classes, self.pos_label)
  
      @property
      def is_differentiable(self) -> bool:
          return False
  