text: import itertools
  import json
  import linecache
  import math
  import os
  import pickle
  import socket
  from logging import getLogger
  from pathlib import Path
  from typing import Callable, Dict, Iterable, List, Tuple, Union
  
  import git
  import numpy as np
  import torch
  import torch.distributed as dist
  from rouge_score import rouge_scorer, scoring
  from sacrebleu import corpus_bleu
  from torch import nn
  from torch.utils.data import Dataset, Sampler
  
  from sentence_splitter import add_newline_to_end_of_each_sentence
  from transformers import BartTokenizer, EvalPrediction, PreTrainedTokenizer, T5Tokenizer
  from transformers.file_utils import cached_property
  from transformers.modeling_bart import shift_tokens_right
  
  
  try:
      from fairseq.data.data_utils import batch_by_size
  
      FAIRSEQ_AVAILABLE = True
  except (ImportError, ModuleNotFoundError):
      FAIRSEQ_AVAILABLE = False
  
  
  def label_smoothed_nll_loss(lprobs, target, epsilon, ignore_index=-100):
      """From fairseq"""
      if target.dim() == lprobs.dim() - 1:
          target = target.unsqueeze(-1)
      nll_loss = -lprobs.gather(dim=-1, index=target)
      smooth_loss = -lprobs.sum(dim=-1, keepdim=True)
      if ignore_index is not None:
          pad_mask = target.eq(ignore_index)
          nll_loss.masked_fill_(pad_mask, 0.0)
          smooth_loss.masked_fill_(pad_mask, 0.0)
      else:
          nll_loss = nll_loss.squeeze(-1)
          smooth_loss = smooth_loss.squeeze(-1)
  
      nll_loss = nll_loss.sum()  # mean()? Scared to break other math.
      smooth_loss = smooth_loss.sum()
      eps_i = epsilon / lprobs.size(-1)
      loss = (1.0 - epsilon) * nll_loss + eps_i * smooth_loss
      return loss, nll_loss
  
  
  def lmap(f: Callable, x: Iterable) -> List:
      """list(map(f, x))"""
      return list(map(f, x))
  
  
  def calculate_bleu(output_lns, refs_lns, **kwargs) -> dict:
      """Uses sacrebleu's corpus_bleu implementation."""
      return {"bleu": round(corpus_bleu(output_lns, [refs_lns], **kwargs).score, 4)}
  
  
  def build_compute_metrics_fn(task_name: str, tokenizer: PreTrainedTokenizer) -> Callable[[EvalPrediction], Dict]:
      def non_pad_len(tokens: np.ndarray) -> int:
          return np.count_nonzero(tokens != tokenizer.pad_token_id)
  
      def decode_pred(pred: EvalPrediction) -> Tuple[List[str], List[str]]:
          pred_str = tokenizer.batch_decode(pred.predictions, skip_special_tokens=True)
          label_str = tokenizer.batch_decode(pred.label_ids, skip_special_tokens=True)
          pred_str = lmap(str.strip, pred_str)
          label_str = lmap(str.strip, label_str)
          return pred_str, label_str
  
      def summarization_metrics(pred: EvalPrediction) -> Dict:
          pred_str, label_str = decode_pred(pred)
          rouge: Dict = calculate_rouge(pred_str, label_str)
          summ_len = np.round(np.mean(lmap(non_pad_len, pred.predictions)), 1)
          rouge.update({"gen_len": summ_len})
          return rouge
  
      def translation_metrics(pred: EvalPrediction) -> Dict:
          pred_str, label_str = decode_pred(pred)
          bleu: Dict = calculate_bleu(pred_str, label_str)
          gen_len = np.round(np.mean(lmap(non_pad_len, pred.predictions)), 1)
          bleu.update({"gen_len": gen_len})
          return bleu
  
      compute_metrics_fn = summarization_metrics if "summarization" in task_name else translation_metrics
      return compute_metrics_fn
  
  
  def trim_batch(
      input_ids,
      pad_token_id,
      attention_mask=None,
  ):
      """Remove columns that are populated exclusively by pad_token_id"""
      keep_column_mask = input_ids.ne(pad_token_id).any(dim=0)
      if attention_mask is None:
          return input_ids[:, keep_column_mask]
      else:
          return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
  
  
  class AbstractSeq2SeqDataset(Dataset):
      def __init__(
          self,
          tokenizer,
          data_dir,
          max_source_length,
          max_target_length,
          type_path="train",
          n_obs=None,
          prefix="",
          **dataset_kwargs
      ):
          super().__init__()
          self.src_file = Path(data_dir).joinpath(type_path + ".source")
          self.tgt_file = Path(data_dir).joinpath(type_path + ".target")
          self.len_file = Path(data_dir).joinpath(type_path + ".len")
          if os.path.exists(self.len_file):
              self.src_lens = pickle_load(self.len_file)
              self.used_char_len = False
          else:
              self.src_lens = self.get_char_lens(self.src_file)
              self.used_char_len = True
          self.max_source_length = max_source_length
          self.max_target_length = max_target_length
          assert min(self.src_lens) > 0, f"found empty line in {self.src_file}"
          self.tokenizer = tokenizer
          self.prefix = prefix if prefix is not None else ""
  
          if n_obs is not None:
              self.src_lens = self.src_lens[:n_obs]
          self.pad_token_id = self.tokenizer.pad_token_id
          self.dataset_kwargs = dataset_kwargs
          dataset_kwargs.update({"add_prefix_space": True} if isinstance(self.tokenizer, BartTokenizer) else {})
  
      def __len__(self):
          return len(self.src_lens)
  
      @staticmethod
      def get_char_lens(data_file):
          return [len(x) for x in Path(data_file).open().readlines()]
  
      @cached_property
      def tgt_lens(self):
          """Length in characters of target documents"""
          return self.get_char_lens(self.tgt_file)
  
      def make_sortish_sampler(self, batch_size, distributed=False, shuffle=True, **kwargs):
          if distributed:
              return DistributedSortishSampler(self, batch_size, shuffle=shuffle, **kwargs)
          else:
              return SortishSampler(self.src_lens, batch_size, shuffle=shuffle)
  
      def make_dynamic_sampler(self, max_tokens_per_batch=1024, **kwargs):
          assert FAIRSEQ_AVAILABLE, "Dynamic batch size requires `pip install fairseq`"
          assert not self.used_char_len, "You must call  python make_len_file.py before calling make_dynamic_sampler"
          sorted_indices = list(self.make_sortish_sampler(1024, shuffle=False))
  
          def num_tokens_in_example(i):
              return min(self.src_lens[i], self.max_target_length)
  
          # call fairseq cython function
          batch_sampler: List[List[int]] = batch_by_size(
              sorted_indices,
              num_tokens_fn=num_tokens_in_example,
              max_tokens=max_tokens_per_batch,
              required_batch_size_multiple=64,
          )
          shuffled_batches = [batch_sampler[i] for i in np.random.permutation(range(len(batch_sampler)))]
          # move the largest batch to the front to OOM quickly (uses an approximation for padding)
          approximate_toks_per_batch = [max(self.src_lens[i] for i in batch) * len(batch) for batch in shuffled_batches]
          largest_batch_idx = np.argmax(approximate_toks_per_batch)
          shuffled_batches[0], shuffled_batches[largest_batch_idx] = (
              shuffled_batches[largest_batch_idx],
              shuffled_batches[0],
          )
          return shuffled_batches
  
      def __getitem__(self, item):
          raise NotImplementedError("You must implement this")
  
      def collate_fn(self, batch):
          raise NotImplementedError("You must implement this")
  
  
  class LegacySeq2SeqDataset(AbstractSeq2SeqDataset):
      def __getitem__(self, index) -> Dict[str, torch.Tensor]:
          """Call tokenizer on src and tgt_lines"""
          index = index + 1  # linecache starts at 1
          source_line = self.prefix + linecache.getline(str(self.src_file), index).rstrip("\n")
          tgt_line = linecache.getline(str(self.tgt_file), index).rstrip("\n")
          assert source_line, f"empty source line for index {index}"
          assert tgt_line, f"empty tgt line for index {index}"
          source_inputs = self.encode_line(self.tokenizer, source_line, self.max_source_length)
          target_inputs = self.encode_line(self.tokenizer, tgt_line, self.max_target_length)
  
          source_ids = source_inputs["input_ids"].squeeze()
          target_ids = target_inputs["input_ids"].squeeze()
          src_mask = source_inputs["attention_mask"].squeeze()
          return {
              "input_ids": source_ids,
              "attention_mask": src_mask,
              "labels": target_ids,
          }
  
      def encode_line(self, tokenizer, line, max_length, pad_to_max_length=True, return_tensors="pt"):
          """Only used by LegacyDataset"""
          return tokenizer(
              [line],
              max_length=max_length,
              padding="max_length" if pad_to_max_length else None,
              truncation=True,
              return_tensors=return_tensors,
              **self.dataset_kwargs,
          )
  
      def collate_fn(self, batch) -> Dict[str, torch.Tensor]:
          input_ids = torch.stack([x["input_ids"] for x in batch])
          masks = torch.stack([x["attention_mask"] for x in batch])
          target_ids = torch.stack([x["labels"] for x in batch])
          pad_token_id = self.pad_token_id
          y = trim_batch(target_ids, pad_token_id)
          source_ids, source_mask = trim_batch(input_ids, pad_token_id, attention_mask=masks)
          batch = {
              "input_ids": source_ids,
              "attention_mask": source_mask,
              "labels": y,
          }
          return batch
  
  
  class Seq2SeqDataset(AbstractSeq2SeqDataset):
      """A dataset that calls prepare_seq2seq_batch."""
  
      def __getitem__(self, index) -> Dict[str, str]:
          index = index + 1  # linecache starts at 1
          source_line = self.prefix + linecache.getline(str(self.src_file), index).rstrip("\n")
          tgt_line = linecache.getline(str(self.tgt_file), index).rstrip("\n")
          assert source_line, f"empty source line for index {index}"
          assert tgt_line, f"empty tgt line for index {index}"
          return {"tgt_texts": tgt_line, "src_texts": source_line, "id": index - 1}
  
      def collate_fn(self, batch) -> Dict[str, torch.Tensor]:
          """Call prepare_seq2seq_batch."""
          batch_encoding: Dict[str, torch.Tensor] = self.tokenizer.prepare_seq2seq_batch(
              [x["src_texts"] for x in batch],
              tgt_texts=[x["tgt_texts"] for x in batch],
              max_length=self.max_source_length,
              max_target_length=self.max_target_length,
              return_tensors="pt",
              **self.dataset_kwargs,
          ).data
          batch_encoding["ids"] = torch.tensor([x["id"] for x in batch])
          return batch_encoding
  
  
  class Seq2SeqDataCollator:
      def __init__(self, tokenizer, data_args, tpu_num_cores=None):
          self.tokenizer = tokenizer
          self.pad_token_id = tokenizer.pad_token_id
          assert (
              self.pad_token_id is not None
          ), f"pad_token_id is not defined for ({self.tokenizer.__class__.__name__}), it must be defined."
          self.data_args = data_args
          self.tpu_num_cores = tpu_num_cores
          self.dataset_kwargs = {"add_prefix_space": isinstance(tokenizer, BartTokenizer)}
          if data_args.src_lang is not None:
              self.dataset_kwargs["src_lang"] = data_args.src_lang
          if data_args.tgt_lang is not None:
              self.dataset_kwargs["tgt_lang"] = data_args.tgt_lang
  
      def __call__(self, batch) -> Dict[str, torch.Tensor]:
          if hasattr(self.tokenizer, "prepare_seq2seq_batch"):
              batch = self._encode(batch)
              input_ids, attention_mask, labels = (
                  batch["input_ids"],
                  batch["attention_mask"],
                  batch["labels"],
              )
          else:
              input_ids = torch.stack([x["input_ids"] for x in batch])
              attention_mask = torch.stack([x["attention_mask"] for x in batch])
              labels = torch.stack([x["labels"] for x in batch])
  
              labels = trim_batch(labels, self.pad_token_id)
              input_ids, attention_mask = trim_batch(input_ids, self.pad_token_id, attention_mask=attention_mask)
  
          if isinstance(self.tokenizer, T5Tokenizer):
              decoder_input_ids = self._shift_right_t5(labels)
          else:
              decoder_input_ids = shift_tokens_right(labels, self.pad_token_id)
  
          batch = {
              "input_ids": input_ids,
              "attention_mask": attention_mask,
              "decoder_input_ids": decoder_input_ids,
              "labels": labels,
          }
          return batch
  
      def _shift_right_t5(self, input_ids):
          # shift inputs to the right
          shifted_input_ids = input_ids.new_zeros(input_ids.shape)
          shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
          shifted_input_ids[..., 0] = self.pad_token_id
          return shifted_input_ids
  
      def _encode(self, batch) -> Dict[str, torch.Tensor]:
          batch_encoding = self.tokenizer.prepare_seq2seq_batch(
              [x["src_texts"] for x in batch],
              tgt_texts=[x["tgt_texts"] for x in batch],
              max_length=self.data_args.max_source_length,
              max_target_length=self.data_args.max_target_length,
              padding="max_length" if self.tpu_num_cores is not None else "longest",  # TPU hack
              return_tensors="pt",
              **self.dataset_kwargs,
          )
          return batch_encoding.data
  
  
  class SortishSampler(Sampler):
      "Go through the text data by order of src length with a bit of randomness. From fastai repo."
  
      def __init__(self, data, batch_size, shuffle=True):
          self.data, self.bs, self.shuffle = data, batch_size, shuffle
  
      def __len__(self) -> int:
          return len(self.data)
  
      def __iter__(self):
          return iter(sortish_sampler_indices(self.data, self.bs, shuffle=self.shuffle))
  
  
  def sortish_sampler_indices(data: List, bs: int, shuffle=True) -> np.array:
      "Go through the text data by order of src length with a bit of randomness. From fastai repo."
      if not shuffle:
          return np.argsort(np.array(data) * -1)
  
      def key_fn(i):
          return data[i]
  
      idxs = np.random.permutation(len(data))
      sz = bs * 50
      ck_idx = [idxs[i : i + sz] for i in range(0, len(idxs), sz)]
      sort_idx = np.concatenate([sorted(s, key=key_fn, reverse=True) for s in ck_idx])
      sz = bs
      ck_idx = [sort_idx[i : i + sz] for i in range(0, len(sort_idx), sz)]
      max_ck = np.argmax([key_fn(ck[0]) for ck in ck_idx])  # find the chunk with the largest key,
      ck_idx[0], ck_idx[max_ck] = ck_idx[max_ck], ck_idx[0]  # then make sure it goes first.
      sort_idx = np.concatenate(np.random.permutation(ck_idx[1:])) if len(ck_idx) > 1 else np.array([], dtype=np.int)
      sort_idx = np.concatenate((ck_idx[0], sort_idx))
      return sort_idx
  
  
  class DistributedSortishSampler(Sampler):
      """Copied from torch DistributedSampler"""
  
      def __init__(self, dataset, batch_size, num_replicas=None, rank=None, add_extra_examples=True, shuffle=True):
          if num_replicas is None:
              if not dist.is_available():
                  raise RuntimeError("Requires distributed package to be available")
              num_replicas = dist.get_world_size()
          if rank is None:
              if not dist.is_available():
                  raise RuntimeError("Requires distributed package to be available")
              rank = dist.get_rank()
          self.dataset = dataset
          self.num_replicas = num_replicas
          self.rank = rank
          self.epoch = 0
          if add_extra_examples:
              self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas))
              self.total_size = self.num_samples * self.num_replicas
          else:
              self.total_size = len(dataset)
              self.num_samples = len(self.available_indices)
          self.batch_size = batch_size
          self.add_extra_examples = add_extra_examples
          self.shuffle = shuffle
  
      def __iter__(self) -> Iterable:
          g = torch.Generator()
          g.manual_seed(self.epoch)
  
          sortish_data = [self.dataset.src_lens[i] for i in self.available_indices]
          sortish_indices = sortish_sampler_indices(sortish_data, self.batch_size, shuffle=self.shuffle)
          indices = [self.available_indices[i] for i in sortish_indices]
          assert len(indices) == self.num_samples
          return iter(indices)
  
      @cached_property
      def available_indices(self) -> np.array:
          indices = list(range(len(self.dataset)))
          # add extra samples to make it evenly divisible
          indices += indices[: (self.total_size - len(indices))]
          assert len(indices) == self.total_size
          # subsample
          available_indices = indices[self.rank : self.total_size : self.num_replicas]
          return available_indices
  
      def __len__(self):
          return self.num_samples
  
      def set_epoch(self, epoch):
          self.epoch = epoch
  
  
  logger = getLogger(__name__)
  
  
  def use_task_specific_params(model, task):
      """Update config with summarization specific params."""
      task_specific_params = model.config.task_specific_params
  
      if task_specific_params is not None:
          pars = task_specific_params.get(task, {})
          logger.info(f"using task specific params for {task}: {pars}")
          model.config.update(pars)
  
  
  def pickle_load(path):
      """pickle.load(path)"""
      with open(path, "rb") as f:
          return pickle.load(f)
  
  
  def pickle_save(obj, path):
      """pickle.dump(obj, path)"""
      with open(path, "wb") as f:
          return pickle.dump(obj, f)
  
  
  def flatten_list(summary_ids: List[List]):
      return [x for x in itertools.chain.from_iterable(summary_ids)]
  
  
  def save_git_info(folder_path: str) -> None:
      """Save git information to output_dir/git_log.json"""
      repo_infos = get_git_info()
      save_json(repo_infos, os.path.join(folder_path, "git_log.json"))
  
  
  def save_json(content, path, indent=4, **json_dump_kwargs):
      with open(path, "w") as f:
          json.dump(content, f, indent=indent, **json_dump_kwargs)
  
  
  def load_json(path):
      with open(path) as f:
          return json.load(f)
  
  
  def get_git_info():
      try:
          repo = git.Repo(search_parent_directories=True)
          repo_infos = {
              "repo_id": str(repo),
              "repo_sha": str(repo.head.object.hexsha),
              "repo_branch": str(repo.active_branch),
              "hostname": str(socket.gethostname()),
          }
          return repo_infos
      except TypeError:
          return {
              "repo_id": None,
              "repo_sha": None,
              "repo_branch": None,
              "hostname": None,
          }
  
  
  ROUGE_KEYS = ["rouge1", "rouge2", "rougeL", "rougeLsum"]
  
  
  def extract_rouge_mid_statistics(dct):
      new_dict = {}
      for k1, v1 in dct.items():
          mid = v1.mid
          new_dict[k1] = {stat: round(getattr(mid, stat), 4) for stat in ["precision", "recall", "fmeasure"]}
      return new_dict
  
  
  def calculate_rouge(
      pred_lns: List[str],
      tgt_lns: List[str],
      use_stemmer=True,
      rouge_keys=ROUGE_KEYS,
      return_precision_and_recall=False,
      bootstrap_aggregation=True,
      newline_sep=True,
  ) -> Dict:
      """Calculate rouge using rouge_scorer package.
  
      Args:
          pred_lns: list of summaries generated by model
          tgt_lns: list of groundtruth summaries (e.g. contents of val.target)
          use_stemmer:  Bool indicating whether Porter stemmer should be used to
          strip word suffixes to improve matching.
          rouge_keys:  which metrics to compute, defaults to rouge1, rouge2, rougeL, rougeLsum
          return_precision_and_recall: (False) whether to also return precision and recall.
          bootstrap_aggregation: whether to do the typical bootstrap resampling of scores. Defaults to True, if False
              this function returns a collections.defaultdict[metric: list of values for each observation for each subscore]``
          newline_sep:(default=True) whether to add newline between sentences. This is essential for calculation rougeL
          on multi sentence summaries (CNN/DM dataset).
  
      Returns:
           Dict[score: value] if aggregate else defaultdict(list) keyed by rouge_keys
  
      """
      scorer = rouge_scorer.RougeScorer(rouge_keys, use_stemmer=use_stemmer)
      aggregator = scoring.BootstrapAggregator()
      for pred, tgt in zip(tgt_lns, pred_lns):
          # rougeLsum expects "\n" separated sentences within a summary
          if newline_sep:
              pred = add_newline_to_end_of_each_sentence(pred)
              tgt = add_newline_to_end_of_each_sentence(tgt)
          scores = scorer.score(pred, tgt)
          aggregator.add_scores(scores)
  
      if bootstrap_aggregation:
          result = aggregator.aggregate()
          if return_precision_and_recall:
              return extract_rouge_mid_statistics(result)  # here we return dict
          else:
              return {k: round(v.mid.fmeasure * 100, 4) for k, v in result.items()}
  
      else:
          return aggregator._scores  # here we return defaultdict(list)
  
  
  # Utilities for freezing parameters and checking whether they are frozen
  
  
  def freeze_params(model: nn.Module):
      """Set requires_grad=False for each of model.parameters()"""
      for par in model.parameters():
          par.requires_grad = False
  
  
  def freeze_embeds(model):
      """Freeze token embeddings and positional embeddings for bart, just token embeddings for t5."""
      model_type = model.config.model_type
  
      if model_type == "t5":
          freeze_params(model.shared)
          for d in [model.encoder, model.decoder]:
              freeze_params(d.embed_tokens)
      elif model_type == "fsmt":
          for d in [model.model.encoder, model.model.decoder]:
              freeze_params(d.embed_positions)
              freeze_params(d.embed_tokens)
      else:
          freeze_params(model.model.shared)
          for d in [model.model.encoder, model.model.decoder]:
              freeze_params(d.embed_positions)
              freeze_params(d.embed_tokens)
  
  
  def grad_status(model: nn.Module) -> Iterable:
      return (par.requires_grad for par in model.parameters())
  
  
  def any_requires_grad(model: nn.Module) -> bool:
      return any(grad_status(model))
  
  
  def assert_all_frozen(model):
      model_grads: List[bool] = list(grad_status(model))
      n_require_grad = sum(lmap(int, model_grads))
      npars = len(model_grads)
      assert not any(model_grads), f"{n_require_grad/npars:.1%} of {npars} weights require grad"
  
  
  def assert_not_all_frozen(model):
      model_grads: List[bool] = list(grad_status(model))
      npars = len(model_grads)
      assert any(model_grads), f"none of {npars} weights require grad"
  
  
  def parse_numeric_n_bool_cl_kwargs(unparsed_args: List[str]) -> Dict[str, Union[int, float, bool]]:
      """
      Parse an argv list of unspecified command line args to a dict.
      Assumes all values are either numeric or boolean in the form of true/false.
      """
      result = {}
      assert len(unparsed_args) % 2 == 0, f"got odd number of unparsed args: {unparsed_args}"
      num_pairs = len(unparsed_args) // 2
      for pair_num in range(num_pairs):
          i = 2 * pair_num
          assert unparsed_args[i].startswith("--")
          if unparsed_args[i + 1].lower() == "true":
              value = True
          elif unparsed_args[i + 1].lower() == "false":
              value = False
          else:
              try:
                  value = int(unparsed_args[i + 1])
              except ValueError:
                  value = float(unparsed_args[i + 1])  # this can raise another informative ValueError
  
          result[unparsed_args[i][2:]] = value
      return result
  
  
  def write_txt_file(ordered_tgt, path):
      f = Path(path).open("w")
      for ln in ordered_tgt:
          f.write(ln + "\n")
          f.flush()
  
  
  def chunks(lst, n):
      """Yield successive n-sized chunks from lst."""
      for i in range(0, len(lst), n):
          yield lst[i : i + n]
  
  
  def check_output_dir(args, expected_items=0):
      """
      Checks whether to bail out if output_dir already exists and has more than expected_items in it
  
      `args`: needs to have the following attributes of `args`:
        - output_dir
        - do_train
        - overwrite_output_dir
  
      `expected_items`: normally 0 (default) - i.e. empty dir, but in some cases a few files are expected (e.g. recovery from OOM)
      """
      if (
          os.path.exists(args.output_dir)
          and len(os.listdir(args.output_dir)) > expected_items
          and args.do_train
          and not args.overwrite_output_dir
      ):
          raise ValueError(
              f"Output directory ({args.output_dir}) already exists and "
              f"has {len(os.listdir(args.output_dir))} items in it (expected {expected_items} items). "
              "Use --overwrite_output_dir to overcome."
          )
  