code: # Copyright 2020 The HuggingFace Team. All rights reserved.
  #
  # 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, Dict, List, Optional, Tuple, Union
  
  import torch
  from packaging import version
  from torch import nn
  from torch.utils.data import DistributedSampler, RandomSampler
  from torch.utils.data.dataset import Dataset
  
  from .file_utils import is_torch_tpu_available
  from .trainer import Trainer
  from .trainer_pt_utils import get_tpu_sampler
  from .trainer_utils import PredictionOutput
  from .training_args import ParallelMode
  from .utils import logging
  
  
  if version.parse(torch.__version__) >= version.parse("1.6"):
      from torch.cuda.amp import autocast
  
  
  logger = logging.get_logger(__name__)
  
  
  class Seq2SeqTrainer(Trainer):
      def _get_train_sampler(self) -> Optional[torch.utils.data.sampler.Sampler]:
          if isinstance(self.train_dataset, torch.utils.data.IterableDataset):
              return None
          elif is_torch_tpu_available():
              return get_tpu_sampler(self.train_dataset)
          else:
              if self.args.sortish_sampler:
                  self.train_dataset.make_sortish_sampler(
                      self.args.per_device_train_batch_size,
                      distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED),
                  )
  
              return (
                  RandomSampler(self.train_dataset)
                  if self.args.local_rank == -1
                  else DistributedSampler(self.train_dataset)
              )
  
      def evaluate(
          self,
          eval_dataset: Optional[Dataset] = None,
          ignore_keys: Optional[List[str]] = None,
          metric_key_prefix: str = "eval",
          max_length: Optional[int] = None,
          num_beams: Optional[int] = None,
      ) -> Dict[str, float]:
          """
          Run evaluation and returns metrics.
  
          The calling script will be responsible for providing a method to compute metrics, as they are task-dependent
          (pass it to the init :obj:`compute_metrics` argument).
  
          You can also subclass and override this method to inject custom behavior.
  
          Args:
              eval_dataset (:obj:`Dataset`, `optional`):
                  Pass a dataset if you wish to override :obj:`self.eval_dataset`. If it is an :obj:`datasets.Dataset`,
                  columns not accepted by the ``model.forward()`` method are automatically removed. It must implement the
                  :obj:`__len__` method.
              ignore_keys (:obj:`List[str]`, `optional`):
                  A list of keys in the output of your model (if it is a dictionary) that should be ignored when
                  gathering predictions.
              metric_key_prefix (:obj:`str`, `optional`, defaults to :obj:`"eval"`):
                  An optional prefix to be used as the metrics key prefix. For example the metrics "bleu" will be named
                  "eval_bleu" if the prefix is ``"eval"`` (default)
              max_length (:obj:`int`, `optional`):
                  The maximum target length to use when predicting with the generate method.
              num_beams (:obj:`int`, `optional`):
                  Number of beams for beam search that will be used when predicting with the generate method. 1 means no
                  beam search.
  
          Returns:
              A dictionary containing the evaluation loss and the potential metrics computed from the predictions. The
              dictionary also contains the epoch number which comes from the training state.
          """
          self._max_length = max_length
          self._num_beams = num_beams
          return super().evaluate(eval_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)
  
      def predict(
          self,
          test_dataset: Dataset,
          ignore_keys: Optional[List[str]] = None,
          metric_key_prefix: str = "eval",
          max_length: Optional[int] = None,
          num_beams: Optional[int] = None,
      ) -> PredictionOutput:
          """
          Run prediction and returns predictions and potential metrics.
  
          Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method
          will also return metrics, like in :obj:`evaluate()`.
  
          Args:
              test_dataset (:obj:`Dataset`):
                  Dataset to run the predictions on. If it is an :obj:`datasets.Dataset`, columns not accepted by the
                  ``model.forward()`` method are automatically removed. Has to implement the method :obj:`__len__`
              ignore_keys (:obj:`List[str]`, `optional`):
                  A list of keys in the output of your model (if it is a dictionary) that should be ignored when
                  gathering predictions.
              metric_key_prefix (:obj:`str`, `optional`, defaults to :obj:`"eval"`):
                  An optional prefix to be used as the metrics key prefix. For example the metrics "bleu" will be named
                  "eval_bleu" if the prefix is ``"eval"`` (default)
              max_length (:obj:`int`, `optional`):
                  The maximum target length to use when predicting with the generate method.
              num_beams (:obj:`int`, `optional`):
                  Number of beams for beam search that will be used when predicting with the generate method. 1 means no
                  beam search.
  
          .. note::
  
              If your predictions or labels have different sequence lengths (for instance because you're doing dynamic
              padding in a token classification task) the predictions will be padded (on the right) to allow for
              concatenation into one array. The padding index is -100.
  
          Returns: `NamedTuple` A namedtuple with the following keys:
  
              - predictions (:obj:`np.ndarray`): The predictions on :obj:`test_dataset`.
              - label_ids (:obj:`np.ndarray`, `optional`): The labels (if the dataset contained some).
              - metrics (:obj:`Dict[str, float]`, `optional`): The potential dictionary of metrics (if the dataset
                contained labels).
          """
          self._max_length = max_length
          self._num_beams = num_beams
          return super().predict(test_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)
  
      def prediction_step(
          self,
          model: nn.Module,
          inputs: Dict[str, Union[torch.Tensor, Any]],
          prediction_loss_only: bool,
          ignore_keys: Optional[List[str]] = None,
      ) -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
          """
          Perform an evaluation step on :obj:`model` using obj:`inputs`.
  
          Subclass and override to inject custom behavior.
  
          Args:
              model (:obj:`nn.Module`):
                  The model to evaluate.
              inputs (:obj:`Dict[str, Union[torch.Tensor, Any]]`):
                  The inputs and targets of the model.
  
                  The dictionary will be unpacked before being fed to the model. Most models expect the targets under the
                  argument :obj:`labels`. Check your model's documentation for all accepted arguments.
              prediction_loss_only (:obj:`bool`):
                  Whether or not to return the loss only.
  
          Return:
              Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]: A tuple with the loss, logits and
              labels (each being optional).
          """
  
          if not self.args.predict_with_generate or prediction_loss_only:
              return super().prediction_step(
                  model, inputs, prediction_loss_only=prediction_loss_only, ignore_keys=ignore_keys
              )
  
          has_labels = "labels" in inputs
          inputs = self._prepare_inputs(inputs)
  
          gen_kwargs = {
              "max_length": self._max_length if self._max_length is not None else self.model.config.max_length,
              "num_beams": self._num_beams if self._num_beams is not None else self.model.config.num_beams,
          }
  
          generated_tokens = self.model.generate(
              inputs["input_ids"],
              attention_mask=inputs["attention_mask"],
              **gen_kwargs,
          )
          # in case the batch is shorter than max length, the output should be padded
          if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
              generated_tokens = self._pad_tensors_to_max_len(generated_tokens, gen_kwargs["max_length"])
  
          with torch.no_grad():
              if self.use_amp:
                  with autocast():
                      outputs = model(**inputs)
              else:
                  outputs = model(**inputs)
              if has_labels:
                  if self.label_smoother is not None:
                      loss = self.label_smoother(outputs, inputs["labels"]).mean().detach()
                  else:
                      loss = (outputs["loss"] if isinstance(outputs, dict) else outputs[0]).mean().detach()
              else:
                  loss = None
  
          if self.args.prediction_loss_only:
              return (loss, None, None)
  
          labels = inputs["labels"]
          if labels.shape[-1] < gen_kwargs["max_length"]:
              labels = self._pad_tensors_to_max_len(labels, gen_kwargs["max_length"])
  
          return (loss, generated_tokens, labels)
  
      def _pad_tensors_to_max_len(self, tensor, max_length):
          if self.tokenizer is None:
              raise ValueError(
                  f"Tensor need to be padded to `max_length={max_length}` but no tokenzier was passed when creating "
                  "this `Trainer`. Make sure to create your `Trainer` with the appropriate tokenizer."
              )
          # If PAD token is not defined at least EOS token has to be defined
          pad_token_id = (
              self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
          )
  
          padded_tensor = pad_token_id * torch.ones(
              (tensor.shape[0], max_length), dtype=tensor.dtype, device=tensor.device
          )
          padded_tensor[:, : tensor.shape[-1]] = tensor
          return padded_tensor
  