class_docstr: T5 Finetuning model in the same format as MegatronGPTSFTModel 
class_docstr_tok: ["T5","Finetuning","model","in","the","same","format","as","MegatronGPTSFTModel"]
code: def setup_metric(self, data_cfg):
      # XNLI is a special case.
      metric_name = "exact_string_match"
      if hasattr(self.cfg, "eval_languages"):
          metric = [
              ExactStringPerCategoryMatchMetric(self.cfg.eval_languages)
          ]
      else:
          if not hasattr(data_cfg, "metric"):
              metric_class = MetricStringToTorchMetric[
                  "exact_string_match"
              ]
          else:
              if not hasattr(data_cfg.metric, "name"):
                  raise ValueError(
                      "Metric name is not provided in the metric config."
                  )
              if data_cfg.metric.name == "loss":
                  return None, "loss"
              if data_cfg.metric.name not in MetricStringToTorchMetric:
                  raise KeyError(
                      f"{data_cfg.metric.name} is not supported. List of supported metrics: {MetricStringToTorchMetric.keys()}"
                  )
              if (
                  data_cfg.metric.name
                  in self._metrics_require_string2category_map
              ):
                  if data_cfg.metric.average is None:
                      raise ValueError(
                          f"{data_cfg.metric.name} requires specifying whether you want to compute a micro or macro average. Found None."
                      )
              if (
                  data_cfg.metric.get("labels_are_strings", False)
                  and data_cfg.metric.name
                  in self._metrics_require_string2category_map
              ):
                  if data_cfg.metric.num_classes is None:
                      raise ValueError(
                          "Number of classes is not provided in the metric section within the data config. "
                          f"Please provide the number of classes in the data config to use the {data_cfg.metric.name} metric."
                      )
                  if data_cfg.metric.get(
                      "class_labels", None
                  ) is None or not isinstance(
                      data_cfg.metric.get("class_labels", None),
                      ListConfig,
                  ):
                      raise ValueError(
                          "Class labels are not provided properly in the metric section witnin the data config. "
                          f"Please provide the class labels as a list of strings in the data config to use the {data_cfg.metric.name} metric."
                      )
                  if (
                      len(data_cfg.metric.get("class_labels", None))
                      != data_cfg.metric.num_classes
                  ):
                      raise ValueError(
                          f"Number of class labels {len(data_cfg.metric.get('class_labels', None))} does not match `num_classes` : {data_cfg.metric.num_classes}"
                      )
              metric_name = data_cfg.metric.name
              metric_class = MetricStringToTorchMetric[metric_name]
  
          # GLUE will not have a "src_file_name" attribute and will always have only a single metric.
          if hasattr(data_cfg, "src_file_name") or hasattr(
              data_cfg, "file_names"
          ):
              if (
                  hasattr(data_cfg, "src_file_name")
                  and isinstance(data_cfg.src_file_name, ListConfig)
                  and metric_name != "rouge"
              ):
                  metric = [
                      metric_class(
                          average=data_cfg.metric.average,
                          num_classes=data_cfg.metric.num_classes,
                      )
                      for _ in range(len(data_cfg.src_file_name))
                  ]
              elif (
                  hasattr(data_cfg, "file_names")
                  and isinstance(data_cfg.file_names, ListConfig)
                  and metric_name != "rouge"
              ):
                  metric = [
                      metric_class(
                          average=data_cfg.metric.average,
                          num_classes=data_cfg.metric.num_classes,
                      )
                      for _ in range(len(data_cfg.file_names))
                  ]
              elif hasattr(data_cfg, "src_file_name") and isinstance(
                  data_cfg.src_file_name, ListConfig
              ):
                  metric = [
                      metric_class()
                      for _ in range(len(data_cfg.src_file_name))
                  ]
              elif hasattr(data_cfg, "file_names") and isinstance(
                  data_cfg.file_names, ListConfig
              ):
                  metric = [
                      metric_class()
                      for _ in range(len(data_cfg.file_names))
                  ]
              else:
                  metric = [
                      metric_class(
                          average=data_cfg.metric.average,
                          num_classes=data_cfg.metric.num_classes,
                      )
                  ]
          else:
              metric = [
                  metric_class()
              ]  # GLUE does need to specify average or num_classes.
  
      return metric, metric_name
  
imports: import itertools
  import json
  from typing import Dict, List
  import torch
  from omegaconf import DictConfig, ListConfig
  from pytorch_lightning.trainer.trainer import Trainer
  from nemo.collections.common.data import ConcatMapDataset
  from nemo.collections.common.metrics import MetricStringToTorchMetric
  from nemo.collections.common.metrics.classification_accuracy import ExactStringPerCategoryMatchMetric
  from nemo.collections.nlp.data.common.sequence_to_sequence_dataset import SequenceToSequenceDataset
  from nemo.collections.nlp.data.language_modeling.megatron.t5_sft_dataset import T5SFTDataset
  from nemo.collections.nlp.models.language_modeling.megatron_t5_model import MegatronT5Model, T5Sentinel
  from nemo.collections.nlp.modules.common.megatron.utils import get_iterator_k_split
  from nemo.collections.nlp.parts.mixins.nlp_adapter_mixins import NLPAdapterModelMixin
  from nemo.collections.nlp.parts.utils_funcs import get_last_rank
  from nemo.utils import AppState, logging
label_desc: Use image node_id 2 for calling the MegatronT5SFTModel obj's underlying member method code with example usage: obj.setup_metric(data_cfg) and returns: metric,  metric_name, None,  str