text: import json
  import os
  from functools import partial
  from multiprocessing import Pool, cpu_count
  
  import numpy as np
  from tqdm import tqdm
  
  from ...file_utils import is_tf_available, is_torch_available
  from ...models.bert.tokenization_bert import whitespace_tokenize
  from ...tokenization_utils_base import BatchEncoding, PreTrainedTokenizerBase, TruncationStrategy
  from ...utils import logging
  from .utils import DataProcessor
  
  
  # Store the tokenizers which insert 2 separators tokens
  MULTI_SEP_TOKENS_TOKENIZERS_SET = {"roberta", "camembert", "bart"}
  
  
  if is_torch_available():
      import torch
      from torch.utils.data import TensorDataset
  
  if is_tf_available():
      import tensorflow as tf
  
  logger = logging.get_logger(__name__)
  
  
  def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer, orig_answer_text):
      """Returns tokenized answer spans that better match the annotated answer."""
      tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text))
  
      for new_start in range(input_start, input_end + 1):
          for new_end in range(input_end, new_start - 1, -1):
              text_span = " ".join(doc_tokens[new_start : (new_end + 1)])
              if text_span == tok_answer_text:
                  return (new_start, new_end)
  
      return (input_start, input_end)
  
  
  def _check_is_max_context(doc_spans, cur_span_index, position):
      """Check if this is the 'max context' doc span for the token."""
      best_score = None
      best_span_index = None
      for (span_index, doc_span) in enumerate(doc_spans):
          end = doc_span.start + doc_span.length - 1
          if position < doc_span.start:
              continue
          if position > end:
              continue
          num_left_context = position - doc_span.start
          num_right_context = end - position
          score = min(num_left_context, num_right_context) + 0.01 * doc_span.length
          if best_score is None or score > best_score:
              best_score = score
              best_span_index = span_index
  
      return cur_span_index == best_span_index
  
  
  def _new_check_is_max_context(doc_spans, cur_span_index, position):
      """Check if this is the 'max context' doc span for the token."""
      # if len(doc_spans) == 1:
      # return True
      best_score = None
      best_span_index = None
      for (span_index, doc_span) in enumerate(doc_spans):
          end = doc_span["start"] + doc_span["length"] - 1
          if position < doc_span["start"]:
              continue
          if position > end:
              continue
          num_left_context = position - doc_span["start"]
          num_right_context = end - position
          score = min(num_left_context, num_right_context) + 0.01 * doc_span["length"]
          if best_score is None or score > best_score:
              best_score = score
              best_span_index = span_index
  
      return cur_span_index == best_span_index
  
  
  def _is_whitespace(c):
      if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
          return True
      return False
  
  
  def squad_convert_example_to_features(
      example, max_seq_length, doc_stride, max_query_length, padding_strategy, is_training
  ):
      features = []
      if is_training and not example.is_impossible:
          # Get start and end position
          start_position = example.start_position
          end_position = example.end_position
  
          # If the answer cannot be found in the text, then skip this example.
          actual_text = " ".join(example.doc_tokens[start_position : (end_position + 1)])
          cleaned_answer_text = " ".join(whitespace_tokenize(example.answer_text))
          if actual_text.find(cleaned_answer_text) == -1:
              logger.warning("Could not find answer: '%s' vs. '%s'", actual_text, cleaned_answer_text)
              return []
  
      tok_to_orig_index = []
      orig_to_tok_index = []
      all_doc_tokens = []
      for (i, token) in enumerate(example.doc_tokens):
          orig_to_tok_index.append(len(all_doc_tokens))
          if tokenizer.__class__.__name__ in [
              "RobertaTokenizer",
              "LongformerTokenizer",
              "BartTokenizer",
              "RobertaTokenizerFast",
              "LongformerTokenizerFast",
              "BartTokenizerFast",
          ]:
              sub_tokens = tokenizer.tokenize(token, add_prefix_space=True)
          else:
              sub_tokens = tokenizer.tokenize(token)
          for sub_token in sub_tokens:
              tok_to_orig_index.append(i)
              all_doc_tokens.append(sub_token)
  
      if is_training and not example.is_impossible:
          tok_start_position = orig_to_tok_index[example.start_position]
          if example.end_position < len(example.doc_tokens) - 1:
              tok_end_position = orig_to_tok_index[example.end_position + 1] - 1
          else:
              tok_end_position = len(all_doc_tokens) - 1
  
          (tok_start_position, tok_end_position) = _improve_answer_span(
              all_doc_tokens, tok_start_position, tok_end_position, tokenizer, example.answer_text
          )
  
      spans = []
  
      truncated_query = tokenizer.encode(
          example.question_text, add_special_tokens=False, truncation=True, max_length=max_query_length
      )
  
      # Tokenizers who insert 2 SEP tokens in-between <context> & <question> need to have special handling
      # in the way they compute mask of added tokens.
      tokenizer_type = type(tokenizer).__name__.replace("Tokenizer", "").lower()
      sequence_added_tokens = (
          tokenizer.max_len - tokenizer.max_len_single_sentence + 1
          if tokenizer_type in MULTI_SEP_TOKENS_TOKENIZERS_SET
          else tokenizer.max_len - tokenizer.max_len_single_sentence
      )
      sequence_pair_added_tokens = tokenizer.max_len - tokenizer.max_len_sentences_pair
  
      span_doc_tokens = all_doc_tokens
      while len(spans) * doc_stride < len(all_doc_tokens):
  
          # Define the side we want to truncate / pad and the text/pair sorting
          if tokenizer.padding_side == "right":
              texts = truncated_query
              pairs = span_doc_tokens
              truncation = TruncationStrategy.ONLY_SECOND.value
          else:
              texts = span_doc_tokens
              pairs = truncated_query
              truncation = TruncationStrategy.ONLY_FIRST.value
  
          encoded_dict = tokenizer.encode_plus(  # TODO(thom) update this logic
              texts,
              pairs,
              truncation=truncation,
              padding=padding_strategy,
              max_length=max_seq_length,
              return_overflowing_tokens=True,
              stride=max_seq_length - doc_stride - len(truncated_query) - sequence_pair_added_tokens,
              return_token_type_ids=True,
          )
  
          paragraph_len = min(
              len(all_doc_tokens) - len(spans) * doc_stride,
              max_seq_length - len(truncated_query) - sequence_pair_added_tokens,
          )
  
          if tokenizer.pad_token_id in encoded_dict["input_ids"]:
              if tokenizer.padding_side == "right":
                  non_padded_ids = encoded_dict["input_ids"][: encoded_dict["input_ids"].index(tokenizer.pad_token_id)]
              else:
                  last_padding_id_position = (
                      len(encoded_dict["input_ids"]) - 1 - encoded_dict["input_ids"][::-1].index(tokenizer.pad_token_id)
                  )
                  non_padded_ids = encoded_dict["input_ids"][last_padding_id_position + 1 :]
  
          else:
              non_padded_ids = encoded_dict["input_ids"]
  
          tokens = tokenizer.convert_ids_to_tokens(non_padded_ids)
  
          token_to_orig_map = {}
          for i in range(paragraph_len):
              index = len(truncated_query) + sequence_added_tokens + i if tokenizer.padding_side == "right" else i
              token_to_orig_map[index] = tok_to_orig_index[len(spans) * doc_stride + i]
  
          encoded_dict["paragraph_len"] = paragraph_len
          encoded_dict["tokens"] = tokens
          encoded_dict["token_to_orig_map"] = token_to_orig_map
          encoded_dict["truncated_query_with_special_tokens_length"] = len(truncated_query) + sequence_added_tokens
          encoded_dict["token_is_max_context"] = {}
          encoded_dict["start"] = len(spans) * doc_stride
          encoded_dict["length"] = paragraph_len
  
          spans.append(encoded_dict)
  
          if "overflowing_tokens" not in encoded_dict or (
              "overflowing_tokens" in encoded_dict and len(encoded_dict["overflowing_tokens"]) == 0
          ):
              break
          span_doc_tokens = encoded_dict["overflowing_tokens"]
  
      for doc_span_index in range(len(spans)):
          for j in range(spans[doc_span_index]["paragraph_len"]):
              is_max_context = _new_check_is_max_context(spans, doc_span_index, doc_span_index * doc_stride + j)
              index = (
                  j
                  if tokenizer.padding_side == "left"
                  else spans[doc_span_index]["truncated_query_with_special_tokens_length"] + j
              )
              spans[doc_span_index]["token_is_max_context"][index] = is_max_context
  
      for span in spans:
          # Identify the position of the CLS token
          cls_index = span["input_ids"].index(tokenizer.cls_token_id)
  
          # p_mask: mask with 1 for token than cannot be in the answer (0 for token which can be in an answer)
          # Original TF implem also keep the classification token (set to 0)
          p_mask = np.ones_like(span["token_type_ids"])
          if tokenizer.padding_side == "right":
              p_mask[len(truncated_query) + sequence_added_tokens :] = 0
          else:
              p_mask[-len(span["tokens"]) : -(len(truncated_query) + sequence_added_tokens)] = 0
  
          pad_token_indices = np.where(span["input_ids"] == tokenizer.pad_token_id)
          special_token_indices = np.asarray(
              tokenizer.get_special_tokens_mask(span["input_ids"], already_has_special_tokens=True)
          ).nonzero()
  
          p_mask[pad_token_indices] = 1
          p_mask[special_token_indices] = 1
  
          # Set the cls index to 0: the CLS index can be used for impossible answers
          p_mask[cls_index] = 0
  
          span_is_impossible = example.is_impossible
          start_position = 0
          end_position = 0
          if is_training and not span_is_impossible:
              # For training, if our document chunk does not contain an annotation
              # we throw it out, since there is nothing to predict.
              doc_start = span["start"]
              doc_end = span["start"] + span["length"] - 1
              out_of_span = False
  
              if not (tok_start_position >= doc_start and tok_end_position <= doc_end):
                  out_of_span = True
  
              if out_of_span:
                  start_position = cls_index
                  end_position = cls_index
                  span_is_impossible = True
              else:
                  if tokenizer.padding_side == "left":
                      doc_offset = 0
                  else:
                      doc_offset = len(truncated_query) + sequence_added_tokens
  
                  start_position = tok_start_position - doc_start + doc_offset
                  end_position = tok_end_position - doc_start + doc_offset
  
          features.append(
              SquadFeatures(
                  span["input_ids"],
                  span["attention_mask"],
                  span["token_type_ids"],
                  cls_index,
                  p_mask.tolist(),
                  example_index=0,  # Can not set unique_id and example_index here. They will be set after multiple processing.
                  unique_id=0,
                  paragraph_len=span["paragraph_len"],
                  token_is_max_context=span["token_is_max_context"],
                  tokens=span["tokens"],
                  token_to_orig_map=span["token_to_orig_map"],
                  start_position=start_position,
                  end_position=end_position,
                  is_impossible=span_is_impossible,
                  qas_id=example.qas_id,
              )
          )
      return features
  
  
  def squad_convert_example_to_features_init(tokenizer_for_convert: PreTrainedTokenizerBase):
      global tokenizer
      tokenizer = tokenizer_for_convert
  
  
  def squad_convert_examples_to_features(
      examples,
      tokenizer,
      max_seq_length,
      doc_stride,
      max_query_length,
      is_training,
      padding_strategy="max_length",
      return_dataset=False,
      threads=1,
      tqdm_enabled=True,
  ):
      """
      Converts a list of examples into a list of features that can be directly given as input to a model. It is
      model-dependant and takes advantage of many of the tokenizer's features to create the model's inputs.
  
      Args:
          examples: list of :class:`~transformers.data.processors.squad.SquadExample`
          tokenizer: an instance of a child of :class:`~transformers.PreTrainedTokenizer`
          max_seq_length: The maximum sequence length of the inputs.
          doc_stride: The stride used when the context is too large and is split across several features.
          max_query_length: The maximum length of the query.
          is_training: whether to create features for model evaluation or model training.
          padding_strategy: Default to "max_length". Which padding strategy to use
          return_dataset: Default False. Either 'pt' or 'tf'.
              if 'pt': returns a torch.data.TensorDataset, if 'tf': returns a tf.data.Dataset
          threads: multiple processing threads.
  
  
      Returns:
          list of :class:`~transformers.data.processors.squad.SquadFeatures`
  
      Example::
  
          processor = SquadV2Processor()
          examples = processor.get_dev_examples(data_dir)
  
          features = squad_convert_examples_to_features(
              examples=examples,
              tokenizer=tokenizer,
              max_seq_length=args.max_seq_length,
              doc_stride=args.doc_stride,
              max_query_length=args.max_query_length,
              is_training=not evaluate,
          )
      """
      # Defining helper methods
      features = []
  
      threads = min(threads, cpu_count())
      with Pool(threads, initializer=squad_convert_example_to_features_init, initargs=(tokenizer,)) as p:
          annotate_ = partial(
              squad_convert_example_to_features,
              max_seq_length=max_seq_length,
              doc_stride=doc_stride,
              max_query_length=max_query_length,
              padding_strategy=padding_strategy,
              is_training=is_training,
          )
          features = list(
              tqdm(
                  p.imap(annotate_, examples, chunksize=32),
                  total=len(examples),
                  desc="convert squad examples to features",
                  disable=not tqdm_enabled,
              )
          )
  
      new_features = []
      unique_id = 1000000000
      example_index = 0
      for example_features in tqdm(
          features, total=len(features), desc="add example index and unique id", disable=not tqdm_enabled
      ):
          if not example_features:
              continue
          for example_feature in example_features:
              example_feature.example_index = example_index
              example_feature.unique_id = unique_id
              new_features.append(example_feature)
              unique_id += 1
          example_index += 1
      features = new_features
      del new_features
      if return_dataset == "pt":
          if not is_torch_available():
              raise RuntimeError("PyTorch must be installed to return a PyTorch dataset.")
  
          # Convert to Tensors and build dataset
          all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
          all_attention_masks = torch.tensor([f.attention_mask for f in features], dtype=torch.long)
          all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long)
          all_cls_index = torch.tensor([f.cls_index for f in features], dtype=torch.long)
          all_p_mask = torch.tensor([f.p_mask for f in features], dtype=torch.float)
          all_is_impossible = torch.tensor([f.is_impossible for f in features], dtype=torch.float)
  
          if not is_training:
              all_feature_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
              dataset = TensorDataset(
                  all_input_ids, all_attention_masks, all_token_type_ids, all_feature_index, all_cls_index, all_p_mask
              )
          else:
              all_start_positions = torch.tensor([f.start_position for f in features], dtype=torch.long)
              all_end_positions = torch.tensor([f.end_position for f in features], dtype=torch.long)
              dataset = TensorDataset(
                  all_input_ids,
                  all_attention_masks,
                  all_token_type_ids,
                  all_start_positions,
                  all_end_positions,
                  all_cls_index,
                  all_p_mask,
                  all_is_impossible,
              )
  
          return features, dataset
      elif return_dataset == "tf":
          if not is_tf_available():
              raise RuntimeError("TensorFlow must be installed to return a TensorFlow dataset.")
  
          def gen():
              for i, ex in enumerate(features):
                  if ex.token_type_ids is None:
                      yield (
                          {
                              "input_ids": ex.input_ids,
                              "attention_mask": ex.attention_mask,
                              "feature_index": i,
                              "qas_id": ex.qas_id,
                          },
                          {
                              "start_positions": ex.start_position,
                              "end_positions": ex.end_position,
                              "cls_index": ex.cls_index,
                              "p_mask": ex.p_mask,
                              "is_impossible": ex.is_impossible,
                          },
                      )
                  else:
                      yield (
                          {
                              "input_ids": ex.input_ids,
                              "attention_mask": ex.attention_mask,
                              "token_type_ids": ex.token_type_ids,
                              "feature_index": i,
                              "qas_id": ex.qas_id,
                          },
                          {
                              "start_positions": ex.start_position,
                              "end_positions": ex.end_position,
                              "cls_index": ex.cls_index,
                              "p_mask": ex.p_mask,
                              "is_impossible": ex.is_impossible,
                          },
                      )
  
          # Why have we split the batch into a tuple? PyTorch just has a list of tensors.
          if "token_type_ids" in tokenizer.model_input_names:
              train_types = (
                  {
                      "input_ids": tf.int32,
                      "attention_mask": tf.int32,
                      "token_type_ids": tf.int32,
                      "feature_index": tf.int64,
                      "qas_id": tf.string,
                  },
                  {
                      "start_positions": tf.int64,
                      "end_positions": tf.int64,
                      "cls_index": tf.int64,
                      "p_mask": tf.int32,
                      "is_impossible": tf.int32,
                  },
              )
  
              train_shapes = (
                  {
                      "input_ids": tf.TensorShape([None]),
                      "attention_mask": tf.TensorShape([None]),
                      "token_type_ids": tf.TensorShape([None]),
                      "feature_index": tf.TensorShape([]),
                      "qas_id": tf.TensorShape([]),
                  },
                  {
                      "start_positions": tf.TensorShape([]),
                      "end_positions": tf.TensorShape([]),
                      "cls_index": tf.TensorShape([]),
                      "p_mask": tf.TensorShape([None]),
                      "is_impossible": tf.TensorShape([]),
                  },
              )
          else:
              train_types = (
                  {"input_ids": tf.int32, "attention_mask": tf.int32, "feature_index": tf.int64, "qas_id": tf.string},
                  {
                      "start_positions": tf.int64,
                      "end_positions": tf.int64,
                      "cls_index": tf.int64,
                      "p_mask": tf.int32,
                      "is_impossible": tf.int32,
                  },
              )
  
              train_shapes = (
                  {
                      "input_ids": tf.TensorShape([None]),
                      "attention_mask": tf.TensorShape([None]),
                      "feature_index": tf.TensorShape([]),
                      "qas_id": tf.TensorShape([]),
                  },
                  {
                      "start_positions": tf.TensorShape([]),
                      "end_positions": tf.TensorShape([]),
                      "cls_index": tf.TensorShape([]),
                      "p_mask": tf.TensorShape([None]),
                      "is_impossible": tf.TensorShape([]),
                  },
              )
  
          return tf.data.Dataset.from_generator(gen, train_types, train_shapes)
      else:
          return features
  
  
  class SquadProcessor(DataProcessor):
      """
      Processor for the SQuAD data set. overridden by SquadV1Processor and SquadV2Processor, used by the version 1.1 and
      version 2.0 of SQuAD, respectively.
      """
  
      train_file = None
      dev_file = None
  
      def _get_example_from_tensor_dict(self, tensor_dict, evaluate=False):
          if not evaluate:
              answer = tensor_dict["answers"]["text"][0].numpy().decode("utf-8")
              answer_start = tensor_dict["answers"]["answer_start"][0].numpy()
              answers = []
          else:
              answers = [
                  {"answer_start": start.numpy(), "text": text.numpy().decode("utf-8")}
                  for start, text in zip(tensor_dict["answers"]["answer_start"], tensor_dict["answers"]["text"])
              ]
  
              answer = None
              answer_start = None
  
          return SquadExample(
              qas_id=tensor_dict["id"].numpy().decode("utf-8"),
              question_text=tensor_dict["question"].numpy().decode("utf-8"),
              context_text=tensor_dict["context"].numpy().decode("utf-8"),
              answer_text=answer,
              start_position_character=answer_start,
              title=tensor_dict["title"].numpy().decode("utf-8"),
              answers=answers,
          )
  
      def get_examples_from_dataset(self, dataset, evaluate=False):
          """
          Creates a list of :class:`~transformers.data.processors.squad.SquadExample` using a TFDS dataset.
  
          Args:
              dataset: The tfds dataset loaded from `tensorflow_datasets.load("squad")`
              evaluate: Boolean specifying if in evaluation mode or in training mode
  
          Returns:
              List of SquadExample
  
          Examples::
  
              >>> import tensorflow_datasets as tfds
              >>> dataset = tfds.load("squad")
  
              >>> training_examples = get_examples_from_dataset(dataset, evaluate=False)
              >>> evaluation_examples = get_examples_from_dataset(dataset, evaluate=True)
          """
  
          if evaluate:
              dataset = dataset["validation"]
          else:
              dataset = dataset["train"]
  
          examples = []
          for tensor_dict in tqdm(dataset):
              examples.append(self._get_example_from_tensor_dict(tensor_dict, evaluate=evaluate))
  
          return examples
  
      def get_train_examples(self, data_dir, filename=None):
          """
          Returns the training examples from the data directory.
  
          Args:
              data_dir: Directory containing the data files used for training and evaluating.
              filename: None by default, specify this if the training file has a different name than the original one
                  which is `train-v1.1.json` and `train-v2.0.json` for squad versions 1.1 and 2.0 respectively.
  
          """
          if data_dir is None:
              data_dir = ""
  
          if self.train_file is None:
              raise ValueError("SquadProcessor should be instantiated via SquadV1Processor or SquadV2Processor")
  
          with open(
              os.path.join(data_dir, self.train_file if filename is None else filename), "r", encoding="utf-8"
          ) as reader:
              input_data = json.load(reader)["data"]
          return self._create_examples(input_data, "train")
  
      def get_dev_examples(self, data_dir, filename=None):
          """
          Returns the evaluation example from the data directory.
  
          Args:
              data_dir: Directory containing the data files used for training and evaluating.
              filename: None by default, specify this if the evaluation file has a different name than the original one
                  which is `dev-v1.1.json` and `dev-v2.0.json` for squad versions 1.1 and 2.0 respectively.
          """
          if data_dir is None:
              data_dir = ""
  
          if self.dev_file is None:
              raise ValueError("SquadProcessor should be instantiated via SquadV1Processor or SquadV2Processor")
  
          with open(
              os.path.join(data_dir, self.dev_file if filename is None else filename), "r", encoding="utf-8"
          ) as reader:
              input_data = json.load(reader)["data"]
          return self._create_examples(input_data, "dev")
  
      def _create_examples(self, input_data, set_type):
          is_training = set_type == "train"
          examples = []
          for entry in tqdm(input_data):
              title = entry["title"]
              for paragraph in entry["paragraphs"]:
                  context_text = paragraph["context"]
                  for qa in paragraph["qas"]:
                      qas_id = qa["id"]
                      question_text = qa["question"]
                      start_position_character = None
                      answer_text = None
                      answers = []
  
                      is_impossible = qa.get("is_impossible", False)
                      if not is_impossible:
                          if is_training:
                              answer = qa["answers"][0]
                              answer_text = answer["text"]
                              start_position_character = answer["answer_start"]
                          else:
                              answers = qa["answers"]
  
                      example = SquadExample(
                          qas_id=qas_id,
                          question_text=question_text,
                          context_text=context_text,
                          answer_text=answer_text,
                          start_position_character=start_position_character,
                          title=title,
                          is_impossible=is_impossible,
                          answers=answers,
                      )
                      examples.append(example)
          return examples
  
  
  class SquadV1Processor(SquadProcessor):
      train_file = "train-v1.1.json"
      dev_file = "dev-v1.1.json"
  
  
  class SquadV2Processor(SquadProcessor):
      train_file = "train-v2.0.json"
      dev_file = "dev-v2.0.json"
  
  
  class SquadExample:
      """
      A single training/test example for the Squad dataset, as loaded from disk.
  
      Args:
          qas_id: The example's unique identifier
          question_text: The question string
          context_text: The context string
          answer_text: The answer string
          start_position_character: The character position of the start of the answer
          title: The title of the example
          answers: None by default, this is used during evaluation. Holds answers as well as their start positions.
          is_impossible: False by default, set to True if the example has no possible answer.
      """
  
      def __init__(
          self,
          qas_id,
          question_text,
          context_text,
          answer_text,
          start_position_character,
          title,
          answers=[],
          is_impossible=False,
      ):
          self.qas_id = qas_id
          self.question_text = question_text
          self.context_text = context_text
          self.answer_text = answer_text
          self.title = title
          self.is_impossible = is_impossible
          self.answers = answers
  
          self.start_position, self.end_position = 0, 0
  
          doc_tokens = []
          char_to_word_offset = []
          prev_is_whitespace = True
  
          # Split on whitespace so that different tokens may be attributed to their original position.
          for c in self.context_text:
              if _is_whitespace(c):
                  prev_is_whitespace = True
              else:
                  if prev_is_whitespace:
                      doc_tokens.append(c)
                  else:
                      doc_tokens[-1] += c
                  prev_is_whitespace = False
              char_to_word_offset.append(len(doc_tokens) - 1)
  
          self.doc_tokens = doc_tokens
          self.char_to_word_offset = char_to_word_offset
  
          # Start and end positions only has a value during evaluation.
          if start_position_character is not None and not is_impossible:
              self.start_position = char_to_word_offset[start_position_character]
              self.end_position = char_to_word_offset[
                  min(start_position_character + len(answer_text) - 1, len(char_to_word_offset) - 1)
              ]
  
  
  class SquadFeatures:
      """
      Single squad example features to be fed to a model. Those features are model-specific and can be crafted from
      :class:`~transformers.data.processors.squad.SquadExample` using the
      :method:`~transformers.data.processors.squad.squad_convert_examples_to_features` method.
  
      Args:
          input_ids: Indices of input sequence tokens in the vocabulary.
          attention_mask: Mask to avoid performing attention on padding token indices.
          token_type_ids: Segment token indices to indicate first and second portions of the inputs.
          cls_index: the index of the CLS token.
          p_mask: Mask identifying tokens that can be answers vs. tokens that cannot.
              Mask with 1 for tokens than cannot be in the answer and 0 for token that can be in an answer
          example_index: the index of the example
          unique_id: The unique Feature identifier
          paragraph_len: The length of the context
          token_is_max_context: List of booleans identifying which tokens have their maximum context in this feature object.
              If a token does not have their maximum context in this feature object, it means that another feature object
              has more information related to that token and should be prioritized over this feature for that token.
          tokens: list of tokens corresponding to the input ids
          token_to_orig_map: mapping between the tokens and the original text, needed in order to identify the answer.
          start_position: start of the answer token index
          end_position: end of the answer token index
          encoding: optionally store the BatchEncoding with the fast-tokenizer alignement methods.
      """
  
      def __init__(
          self,
          input_ids,
          attention_mask,
          token_type_ids,
          cls_index,
          p_mask,
          example_index,
          unique_id,
          paragraph_len,
          token_is_max_context,
          tokens,
          token_to_orig_map,
          start_position,
          end_position,
          is_impossible,
          qas_id: str = None,
          encoding: BatchEncoding = None,
      ):
          self.input_ids = input_ids
          self.attention_mask = attention_mask
          self.token_type_ids = token_type_ids
          self.cls_index = cls_index
          self.p_mask = p_mask
  
          self.example_index = example_index
          self.unique_id = unique_id
          self.paragraph_len = paragraph_len
          self.token_is_max_context = token_is_max_context
          self.tokens = tokens
          self.token_to_orig_map = token_to_orig_map
  
          self.start_position = start_position
          self.end_position = end_position
          self.is_impossible = is_impossible
          self.qas_id = qas_id
  
          self.encoding = encoding
  
  
  class SquadResult:
      """
      Constructs a SquadResult which can be used to evaluate a model's output on the SQuAD dataset.
  
      Args:
          unique_id: The unique identifier corresponding to that example.
          start_logits: The logits corresponding to the start of the answer
          end_logits: The logits corresponding to the end of the answer
      """
  
      def __init__(self, unique_id, start_logits, end_logits, start_top_index=None, end_top_index=None, cls_logits=None):
          self.start_logits = start_logits
          self.end_logits = end_logits
          self.unique_id = unique_id
  
          if start_top_index:
              self.start_top_index = start_top_index
              self.end_top_index = end_top_index
              self.cls_logits = cls_logits
  