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, Callable, Optional
  
  import pangu.core.backend as B
  from pangu.core.backend import  Tensor
  
  from torchmetrics.functional.classification.matthews_corrcoef import (
      _matthews_corrcoef_compute,
      _matthews_corrcoef_update,
  )
  from torchmetrics.metric import Metric
  
  
  class MatthewsCorrcoef(Metric):
      r"""
      Calculates `Matthews correlation coefficient`_ that measures
      the general correlation or quality of a classification. In the binary case it
      is defined as:
  
      .. math::
          MCC = \frac{TP*TN - FP*FN}{\sqrt{(TP+FP)*(TP+FN)*(TN+FP)*(TN+FN)}}
  
      where TP, TN, FP and FN are respectively the true postitives, true negatives,
      false positives and false negatives. Also works in the case of multi-label or
      multi-class input.
  
      Note:
          This metric produces a multi-dimensional output, so it can not be directly logged.
  
      Forward accepts
  
      - ``preds`` (float or long tensor): ``(N, ...)`` or ``(N, C, ...)`` where C is the number of classes
      - ``target`` (long tensor): ``(N, ...)``
  
      If preds and target are the same shape and preds is a float tensor, we use the ``self.threshold`` argument
      to convert into integer labels. This is the case for binary and multi-label probabilities.
  
      If preds has an extra dimension as in the case of multi-class scores we perform an argmax on ``dim=1``.
  
      Args:
          num_classes: Number of classes in the dataset.
          threshold:
              Threshold value for binary or multi-label probabilites. default: 0.5
          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)
          dist_sync_fn:
              Callback that performs the allgather operation on the metric state. When ``None``, DDP
              will be used to perform the allgather
  
      Example:
          >>> from torchmetrics import MatthewsCorrcoef
          >>> target = B.tensor([1, 1, 0, 0])
          >>> preds = B.tensor([0, 1, 0, 0])
          >>> matthews_corrcoef = MatthewsCorrcoef(num_classes=2)
          >>> matthews_corrcoef(preds, target)
          tensor(0.5774)
  
      """
      is_differentiable = False
      confmat: Tensor
  
      def __init__(
          self,
          num_classes: int,
          threshold: float = 0.5,
          compute_on_step: bool = True,
          dist_sync_on_step: bool = False,
          process_group: Optional[Any] = None,
          dist_sync_fn: Callable = None,
      ) -> None:
          super().__init__(
              compute_on_step=compute_on_step,
              dist_sync_on_step=dist_sync_on_step,
              process_group=process_group,
              dist_sync_fn=dist_sync_fn,
          )
          self.num_classes = num_classes
          self.threshold = threshold
  
          self.add_state("confmat", default=B.zeros(num_classes, num_classes), dist_reduce_fx="sum")
  
      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
          """
          confmat = _matthews_corrcoef_update(preds, target, self.num_classes, self.threshold)
          self.confmat += confmat
  
      def compute(self) -> Tensor:
          """Computes matthews correlation coefficient."""
          return _matthews_corrcoef_compute(self.confmat)
  