text: # coding=utf-8
  # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
  # Copyright (c) 2018, NVIDIA CORPORATION.  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.
  """Run BERT on SQuAD."""
  
  from __future__ import absolute_import, division, print_function
  
  import argparse
  import collections
  import json
  import logging
  import math
  import os
  import random
  import sys
  from io import open
  
  import numpy as np
  import torch
  from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
                                TensorDataset)
  from torch.utils.data.distributed import DistributedSampler
  from tqdm import tqdm, trange
  from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE, WEIGHTS_NAME, CONFIG_NAME
  from pytorch_pretrained_bert.modeling_mt_drop import BertForQuestionAnswering_count, BertConfig           ## changed module name
  from pytorch_pretrained_bert.optimization import BertAdam, WarmupLinearSchedule
  from pytorch_pretrained_bert.tokenization import (BasicTokenizer,
                                                    BertTokenizer,
                                                    whitespace_tokenize)
  
  if sys.version_info[0] == 2:
      import cPickle as pickle
  else:
      import pickle
  
  logger = logging.getLogger(__name__)
  
  
  class SquadExample(object):
      """
      A single training/test example for the Squad dataset.
      For examples without an answer, the start and end position are -1.
      """
  
      def __init__(self,
                   qas_id,
                   question_text,
                   doc_tokens,
                   orig_answer_text=None,
                   start_position=None,
                   end_position=None,
                   is_impossible=None,
                   answer_as_counts=None):                                        ## added
          self.qas_id = qas_id
          self.question_text = question_text
          self.doc_tokens = doc_tokens
          self.orig_answer_text = orig_answer_text
          self.start_position = start_position
          self.end_position = end_position
          self.is_impossible = is_impossible
          self.answer_as_counts = answer_as_counts                                ## added
  
      def __str__(self):
          return self.__repr__()
  
      def __repr__(self):
          s = ""
          s += "qas_id: %s" % (self.qas_id)
          s += ", question_text: %s" % (
              self.question_text)
          s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens))
          if self.start_position:
              s += ", start_position: %d" % (self.start_position)
          if self.end_position:
              s += ", end_position: %d" % (self.end_position)
          if self.is_impossible:
              s += ", is_impossible: %r" % (self.is_impossible)
          if self.answer_as_counts:
              s += ", answer_as_counts: %r" % (self.answer_as_counts)             ## added
          return s
  
  
  class InputFeatures(object):
      """A single set of features of data."""
  
      def __init__(self,
                   unique_id,
                   example_index,
                   doc_span_index,
                   tokens,
                   token_to_orig_map,
                   token_is_max_context,
                   input_ids,
                   input_mask,
                   segment_ids,
                   start_position=None,
                   end_position=None,
                   is_impossible=None,
                   answer_as_counts=None):                                         ## added
          self.unique_id = unique_id
          self.example_index = example_index
          self.doc_span_index = doc_span_index
          self.tokens = tokens
          self.token_to_orig_map = token_to_orig_map
          self.token_is_max_context = token_is_max_context
          self.input_ids = input_ids
          self.input_mask = input_mask
          self.segment_ids = segment_ids
          self.start_position = start_position
          self.end_position = end_position
          self.is_impossible = is_impossible
          self.answer_as_counts = answer_as_counts                                ## added answer_as_counts
  
  
  def read_squad_examples(input_file, is_training, version_2_with_negative):
      """Read a SQuAD json file into a list of SquadExample."""
      with open(input_file, "r", encoding='utf-8') as reader:
          input_data = json.load(reader)["data"]
  
      def is_whitespace(c):
          if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
              return True
          return False
  
      examples = []
      counter = 0                                                                 ## added
      for entry in input_data:
          for paragraph in entry["paragraphs"]:
              paragraph_text = paragraph["context"]
              doc_tokens = []
              char_to_word_offset = []
              prev_is_whitespace = True
              for c in paragraph_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)
  
              for qa in paragraph["qas"]:
                  qas_id = qa["id"]
                  question_text = qa["question"]
                  answer_as_counts = qa["answer_as_counts"][0]                    ## added
                  if answer_as_counts < 0 or answer_as_counts > 10:               ## added
                      answer_as_counts = 10                                       ## added
                  start_position = None
                  end_position = None
                  orig_answer_text = None
                  is_impossible = False
                  if is_training:
                      if version_2_with_negative:
                          #is_impossible = qa["is_impossible"]                    ## outcomment
                          is_impossible = qa["answers_as_passage_spans"][0]["is_impossible"]    ## added because new json
                          #print("check is_impossible...", is_impossible)         ## added print
                      #if (len(qa["answers"]) != 1) and (not is_impossible):      ## outcomment
                      if (len(qa["answers_as_passage_spans"]) != 1) and (not is_impossible):      ## added
                          raise ValueError(
                              "For training, each question should have exactly 1 answer.")
                      if not is_impossible:
                          #answer = qa["answers"][0]                              ## outcomment
                          answer = qa["answers_as_passage_spans"][0]              ## added because new json
                          orig_answer_text = answer["text"]
                          answer_offset = answer["answer_start"]
                          answer_length = len(orig_answer_text)
                          start_position = char_to_word_offset[answer_offset]
                          end_position = char_to_word_offset[answer_offset + answer_length - 1]
                          # Only add answers where the text can be exactly recovered from the
                          # document. If this CAN'T happen it's likely due to weird Unicode
                          # stuff so we will just skip the example.
                          #
                          # Note that this means for training mode, every example is NOT
                          # guaranteed to be preserved.
                          actual_text = " ".join(doc_tokens[start_position:(end_position + 1)])
                          cleaned_answer_text = " ".join(
                              whitespace_tokenize(orig_answer_text))
                          if actual_text.find(cleaned_answer_text) == -1:
                              #logger.warning("Could not find answer: '%s' vs. '%s'",     ## outcommented because we have counter
                                             #actual_text, cleaned_answer_text)
                              counter += 1                                                ## added
                              continue
                      else:
                          start_position = -1
                          end_position = -1
                          orig_answer_text = ""                                    ## outcomment - no, back to original
                          #answer = qa["answers"][0]                               ## added - no, back after new json
                          #orig_answer_text = answer["text"]                       ## added - no, back...
  
                  example = SquadExample(
                      qas_id=qas_id,
                      question_text=question_text,
                      doc_tokens=doc_tokens,
                      orig_answer_text=orig_answer_text,
                      start_position=start_position,
                      end_position=end_position,
                      is_impossible=is_impossible,
                      answer_as_counts=answer_as_counts)                          ## added
  
                  examples.append(example)
      print("skipped examples because matching answer could not be found:  ", counter)     ## added
      #print("print some examples...", examples[:10])                                      ## added print
      return examples
  
  
  def convert_examples_to_features(examples, tokenizer, max_seq_length,
                                   doc_stride, max_query_length, is_training):
      """Loads a data file into a list of `InputBatch`s."""
  
      print("converting examples to features...")                                 ## added print
      unique_id = 1000000000
  
      features = []
      for (example_index, example) in enumerate(examples):
          query_tokens = tokenizer.tokenize(example.question_text)
  
          if len(query_tokens) > max_query_length:
              query_tokens = query_tokens[0:max_query_length]
  
          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))
              sub_tokens = tokenizer.tokenize(token)
              for sub_token in sub_tokens:
                  tok_to_orig_index.append(i)
                  all_doc_tokens.append(sub_token)
  
          tok_start_position = None
          tok_end_position = None
          if is_training and example.is_impossible:
              tok_start_position = -1
              tok_end_position = -1
          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.orig_answer_text)
  
          # The -3 accounts for [CLS], [SEP] and [SEP]
          max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
  
          # We can have documents that are longer than the maximum sequence length.
          # To deal with this we do a sliding window approach, where we take chunks
          # of the up to our max length with a stride of `doc_stride`.
          _DocSpan = collections.namedtuple(  # pylint: disable=invalid-name
              "DocSpan", ["start", "length"])
          doc_spans = []
          start_offset = 0
          while start_offset < len(all_doc_tokens):
              length = len(all_doc_tokens) - start_offset
              if length > max_tokens_for_doc:
                  length = max_tokens_for_doc
              doc_spans.append(_DocSpan(start=start_offset, length=length))
              if start_offset + length == len(all_doc_tokens):
                  break
              start_offset += min(length, doc_stride)
  
          for (doc_span_index, doc_span) in enumerate(doc_spans):
              tokens = []
              token_to_orig_map = {}
              token_is_max_context = {}
              segment_ids = []
              tokens.append("[CLS]")
              segment_ids.append(0)
              for token in query_tokens:
                  tokens.append(token)
                  segment_ids.append(0)
              tokens.append("[SEP]")
              segment_ids.append(0)
  
              for i in range(doc_span.length):
                  split_token_index = doc_span.start + i
                  token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]
  
                  is_max_context = _check_is_max_context(doc_spans, doc_span_index,
                                                         split_token_index)
                  token_is_max_context[len(tokens)] = is_max_context
                  tokens.append(all_doc_tokens[split_token_index])
                  segment_ids.append(1)
              tokens.append("[SEP]")
              segment_ids.append(1)
  
              input_ids = tokenizer.convert_tokens_to_ids(tokens)
  
              # The mask has 1 for real tokens and 0 for padding tokens. Only real
              # tokens are attended to.
              input_mask = [1] * len(input_ids)
  
              # Zero-pad up to the sequence length.
              while len(input_ids) < max_seq_length:
                  input_ids.append(0)
                  input_mask.append(0)
                  segment_ids.append(0)
  
              assert len(input_ids) == max_seq_length
              assert len(input_mask) == max_seq_length
              assert len(segment_ids) == max_seq_length
  
              start_position = None
              end_position = None
              if is_training and not example.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 = doc_span.start
                  doc_end = doc_span.start + doc_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 = 0
                      end_position = 0
                  else:
                      doc_offset = len(query_tokens) + 2
                      start_position = tok_start_position - doc_start + doc_offset
                      end_position = tok_end_position - doc_start + doc_offset
              if is_training and example.is_impossible:
                  start_position = 0
                  end_position = 0
              if example_index < 3:                                               ## 3 changed from 20
                  logger.info("*** Example ***")
                  logger.info("unique_id: %s" % (unique_id))
                  logger.info("example_index: %s" % (example_index))
                  logger.info("doc_span_index: %s" % (doc_span_index))
                  logger.info("tokens: %s" % " ".join(tokens))
                  logger.info("token_to_orig_map: %s" % " ".join([
                      "%d:%d" % (x, y) for (x, y) in token_to_orig_map.items()]))
                  logger.info("token_is_max_context: %s" % " ".join([
                      "%d:%s" % (x, y) for (x, y) in token_is_max_context.items()
                  ]))
                  logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
                  logger.info(
                      "input_mask: %s" % " ".join([str(x) for x in input_mask]))
                  logger.info(
                      "segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
                  if is_training and example.is_impossible:
                      logger.info("impossible example")
                      logger.info("answer: %s" % (example.orig_answer_text))              ## added example.orig_
                  if is_training and not example.is_impossible:
                      answer_text = " ".join(tokens[start_position:(end_position + 1)])
                      logger.info("start_position: %d" % (start_position))
                      logger.info("end_position: %d" % (end_position))
                      logger.info(
                          "answer: %s" % (answer_text))
  
              features.append(
                  InputFeatures(
                      unique_id=unique_id,
                      example_index=example_index,
                      doc_span_index=doc_span_index,
                      tokens=tokens,
                      token_to_orig_map=token_to_orig_map,
                      token_is_max_context=token_is_max_context,
                      input_ids=input_ids,
                      input_mask=input_mask,
                      segment_ids=segment_ids,
                      start_position=start_position,
                      end_position=end_position,
                      is_impossible=example.is_impossible,
                      answer_as_counts=example.answer_as_counts))                 ## added
              unique_id += 1
  
      return features
  
  
  def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,
                           orig_answer_text):
      """Returns tokenized answer spans that better match the annotated answer."""
  
      # The SQuAD annotations are character based. We first project them to
      # whitespace-tokenized words. But then after WordPiece tokenization, we can
      # often find a "better match". For example:
      #
      #   Question: What year was John Smith born?
      #   Context: The leader was John Smith (1895-1943).
      #   Answer: 1895
      #
      # The original whitespace-tokenized answer will be "(1895-1943).". However
      # after tokenization, our tokens will be "( 1895 - 1943 ) .". So we can match
      # the exact answer, 1895.
      #
      # However, this is not always possible. Consider the following:
      #
      #   Question: What country is the top exporter of electornics?
      #   Context: The Japanese electronics industry is the lagest in the world.
      #   Answer: Japan
      #
      # In this case, the annotator chose "Japan" as a character sub-span of
      # the word "Japanese". Since our WordPiece tokenizer does not split
      # "Japanese", we just use "Japanese" as the annotation. This is fairly rare
      # in SQuAD, but does happen.
      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."""
  
      # Because of the sliding window approach taken to scoring documents, a single
      # token can appear in multiple documents. E.g.
      #  Doc: the man went to the store and bought a gallon of milk
      #  Span A: the man went to the
      #  Span B: to the store and bought
      #  Span C: and bought a gallon of
      #  ...
      #
      # Now the word 'bought' will have two scores from spans B and C. We only
      # want to consider the score with "maximum context", which we define as
      # the *minimum* of its left and right context (the *sum* of left and
      # right context will always be the same, of course).
      #
      # In the example the maximum context for 'bought' would be span C since
      # it has 1 left context and 3 right context, while span B has 4 left context
      # and 0 right context.
      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
  
  
  RawResult = collections.namedtuple("RawResult",
                                     ["unique_id", "start_logits", "end_logits", "answer_as_counts"])      ## added answer_as_counts
  
  
  def write_predictions(all_examples, all_features, all_results, n_best_size,
                        max_answer_length, do_lower_case, output_prediction_file,
                        output_nbest_file, output_null_log_odds_file, verbose_logging,
                        version_2_with_negative, null_score_diff_threshold):
      """Write final predictions to the json file and log-odds of null if needed."""
      logger.info("Writing predictions to: %s" % (output_prediction_file))
      logger.info("Writing nbest to: %s" % (output_nbest_file))
  
      example_index_to_features = collections.defaultdict(list)
      for feature in all_features:
          example_index_to_features[feature.example_index].append(feature)
  
      unique_id_to_result = {}
      for result in all_results:
          unique_id_to_result[result.unique_id] = result
  
      _PrelimPrediction = collections.namedtuple(  # pylint: disable=invalid-name
          "PrelimPrediction",
          ["feature_index", "start_index", "end_index", "start_logit", "end_logit"])
  
      all_predictions = collections.OrderedDict()
      all_nbest_json = collections.OrderedDict()
      scores_diff_json = collections.OrderedDict()
  
      for (example_index, example) in enumerate(all_examples):
          features = example_index_to_features[example_index]
          #print("example_index :", example_index)                                 ## added print
          #print("features :", features)                                           ## added print
          #print("result.unique_id :", result.unique_id)                           ## added print
  
          prelim_predictions = []
          # keep track of the minimum score of null start+end of position 0
          score_null = 1000000  # large and positive
          min_null_feature_index = 0  # the paragraph slice with min null score
          null_start_logit = 0  # the start logit at the slice with min null score
          null_end_logit = 0  # the end logit at the slice with min null score
          for (feature_index, feature) in enumerate(features):
              result = unique_id_to_result[feature.unique_id]
              start_indexes = _get_best_indexes(result.start_logits, n_best_size)
              end_indexes = _get_best_indexes(result.end_logits, n_best_size)
              # if we could have irrelevant answers, get the min score of irrelevant
              if version_2_with_negative:
                  feature_null_score = result.start_logits[0] + result.end_logits[0]
                  if feature_null_score < score_null:
                      score_null = feature_null_score
                      min_null_feature_index = feature_index
                      null_start_logit = result.start_logits[0]
                      null_end_logit = result.end_logits[0]
              for start_index in start_indexes:
                  for end_index in end_indexes:
                      # We could hypothetically create invalid predictions, e.g., predict
                      # that the start of the span is in the question. We throw out all
                      # invalid predictions.
                      if start_index >= len(feature.tokens):
                          continue
                      if end_index >= len(feature.tokens):
                          continue
                      if start_index not in feature.token_to_orig_map:
                          continue
                      if end_index not in feature.token_to_orig_map:
                          continue
                      if not feature.token_is_max_context.get(start_index, False):
                          continue
                      if end_index < start_index:
                          continue
                      length = end_index - start_index + 1
                      if length > max_answer_length:
                          continue
                      prelim_predictions.append(
                          _PrelimPrediction(
                              feature_index=feature_index,
                              start_index=start_index,
                              end_index=end_index,
                              start_logit=result.start_logits[start_index],
                              end_logit=result.end_logits[end_index]))
          if version_2_with_negative:
              prelim_predictions.append(
                  _PrelimPrediction(
                      feature_index=min_null_feature_index,
                      start_index=0,
                      end_index=0,
                      start_logit=null_start_logit,
                      end_logit=null_end_logit))
          prelim_predictions = sorted(
              prelim_predictions,
              key=lambda x: (x.start_logit + x.end_logit),
              reverse=True)
  
          _NbestPrediction = collections.namedtuple(  # pylint: disable=invalid-name
              "NbestPrediction", ["text", "start_logit", "end_logit"])
  
          seen_predictions = {}
          nbest = []
          for pred in prelim_predictions:
              if len(nbest) >= n_best_size:
                  break
              feature = features[pred.feature_index]
              if pred.start_index > 0:  # this is a non-null prediction
                  tok_tokens = feature.tokens[pred.start_index:(pred.end_index + 1)]
                  orig_doc_start = feature.token_to_orig_map[pred.start_index]
                  orig_doc_end = feature.token_to_orig_map[pred.end_index]
                  orig_tokens = example.doc_tokens[orig_doc_start:(orig_doc_end + 1)]
                  tok_text = " ".join(tok_tokens)
  
                  # De-tokenize WordPieces that have been split off.
                  tok_text = tok_text.replace(" ##", "")
                  tok_text = tok_text.replace("##", "")
  
                  # Clean whitespace
                  tok_text = tok_text.strip()
                  tok_text = " ".join(tok_text.split())
                  orig_text = " ".join(orig_tokens)
  
                  final_text = get_final_text(tok_text, orig_text, do_lower_case, verbose_logging)
                  if final_text in seen_predictions:
                      continue
  
                  seen_predictions[final_text] = True
              else:
                  final_text = ""
                  seen_predictions[final_text] = True
  
              nbest.append(
                  _NbestPrediction(
                      text=final_text,
                      start_logit=pred.start_logit,
                      end_logit=pred.end_logit))
          # if we didn't include the empty option in the n-best, include it
          if version_2_with_negative:
              if "" not in seen_predictions:
                  nbest.append(
                      _NbestPrediction(
                          text="",
                          start_logit=null_start_logit,
                          end_logit=null_end_logit))
  
              # In very rare edge cases we could only have single null prediction.
              # So we just create a nonce prediction in this case to avoid failure.
              if len(nbest)==1:
                  nbest.insert(0,
                      _NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
  
          # In very rare edge cases we could have no valid predictions. So we
          # just create a nonce prediction in this case to avoid failure.
          if not nbest:
              nbest.append(
                  _NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
  
          assert len(nbest) >= 1
  
          total_scores = []
          best_non_null_entry = None
          for entry in nbest:
              total_scores.append(entry.start_logit + entry.end_logit)
              if not best_non_null_entry:
                  if entry.text:
                      best_non_null_entry = entry
  
          probs = _compute_softmax(total_scores)
  
          nbest_json = []
          for (i, entry) in enumerate(nbest):
              output = collections.OrderedDict()
              output["text"] = entry.text
              output["probability"] = probs[i]
              output["start_logit"] = entry.start_logit
              output["end_logit"] = entry.end_logit
              nbest_json.append(output)
  
          assert len(nbest_json) >= 1
  
          if not version_2_with_negative:
              all_predictions[example.qas_id] = nbest_json[0]["text"]
          else:
              # predict "" iff the null score - the score of best non-null > threshold
              score_diff = score_null - best_non_null_entry.start_logit - (
                  best_non_null_entry.end_logit)
              scores_diff_json[example.qas_id] = score_diff
              if score_diff > null_score_diff_threshold:
                  #all_predictions[example.qas_id] = ""                                                  ## outcomment
                  all_predictions[example.qas_id] = str(result.answer_as_counts)                         ## added
                  #print("in write predictions result.answer_as_counts: ", result.answer_as_counts)      ## added
              else:
                  all_predictions[example.qas_id] = best_non_null_entry.text
              all_nbest_json[example.qas_id] = nbest_json
  
      with open(output_prediction_file, "w") as writer:
          writer.write(json.dumps(all_predictions, indent=4) + "\n")
  
      with open(output_nbest_file, "w") as writer:
          writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
  
      if version_2_with_negative:
          with open(output_null_log_odds_file, "w") as writer:
              writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
  
  
  def get_final_text(pred_text, orig_text, do_lower_case, verbose_logging=False):
      """Project the tokenized prediction back to the original text."""
  
      # When we created the data, we kept track of the alignment between original
      # (whitespace tokenized) tokens and our WordPiece tokenized tokens. So
      # now `orig_text` contains the span of our original text corresponding to the
      # span that we predicted.
      #
      # However, `orig_text` may contain extra characters that we don't want in
      # our prediction.
      #
      # For example, let's say:
      #   pred_text = steve smith
      #   orig_text = Steve Smith's
      #
      # We don't want to return `orig_text` because it contains the extra "'s".
      #
      # We don't want to return `pred_text` because it's already been normalized
      # (the SQuAD eval script also does punctuation stripping/lower casing but
      # our tokenizer does additional normalization like stripping accent
      # characters).
      #
      # What we really want to return is "Steve Smith".
      #
      # Therefore, we have to apply a semi-complicated alignment heuristic between
      # `pred_text` and `orig_text` to get a character-to-character alignment. This
      # can fail in certain cases in which case we just return `orig_text`.
  
      def _strip_spaces(text):
          ns_chars = []
          ns_to_s_map = collections.OrderedDict()
          for (i, c) in enumerate(text):
              if c == " ":
                  continue
              ns_to_s_map[len(ns_chars)] = i
              ns_chars.append(c)
          ns_text = "".join(ns_chars)
          return (ns_text, ns_to_s_map)
  
      # We first tokenize `orig_text`, strip whitespace from the result
      # and `pred_text`, and check if they are the same length. If they are
      # NOT the same length, the heuristic has failed. If they are the same
      # length, we assume the characters are one-to-one aligned.
      tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
  
      tok_text = " ".join(tokenizer.tokenize(orig_text))
  
      start_position = tok_text.find(pred_text)
      if start_position == -1:
          if verbose_logging:
              logger.info(
                  "Unable to find text: '%s' in '%s'" % (pred_text, orig_text))
          return orig_text
      end_position = start_position + len(pred_text) - 1
  
      (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
      (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)
  
      if len(orig_ns_text) != len(tok_ns_text):
          if verbose_logging:
              logger.info("Length not equal after stripping spaces: '%s' vs '%s'",
                          orig_ns_text, tok_ns_text)
          return orig_text
  
      # We then project the characters in `pred_text` back to `orig_text` using
      # the character-to-character alignment.
      tok_s_to_ns_map = {}
      for (i, tok_index) in tok_ns_to_s_map.items():
          tok_s_to_ns_map[tok_index] = i
  
      orig_start_position = None
      if start_position in tok_s_to_ns_map:
          ns_start_position = tok_s_to_ns_map[start_position]
          if ns_start_position in orig_ns_to_s_map:
              orig_start_position = orig_ns_to_s_map[ns_start_position]
  
      if orig_start_position is None:
          if verbose_logging:
              logger.info("Couldn't map start position")
          return orig_text
  
      orig_end_position = None
      if end_position in tok_s_to_ns_map:
          ns_end_position = tok_s_to_ns_map[end_position]
          if ns_end_position in orig_ns_to_s_map:
              orig_end_position = orig_ns_to_s_map[ns_end_position]
  
      if orig_end_position is None:
          if verbose_logging:
              logger.info("Couldn't map end position")
          return orig_text
  
      output_text = orig_text[orig_start_position:(orig_end_position + 1)]
      return output_text
  
  
  def _get_best_indexes(logits, n_best_size):
      """Get the n-best logits from a list."""
      index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
  
      best_indexes = []
      for i in range(len(index_and_score)):
          if i >= n_best_size:
              break
          best_indexes.append(index_and_score[i][0])
      return best_indexes
  
  
  def _compute_softmax(scores):
      """Compute softmax probability over raw logits."""
      if not scores:
          return []
  
      max_score = None
      for score in scores:
          if max_score is None or score > max_score:
              max_score = score
  
      exp_scores = []
      total_sum = 0.0
      for score in scores:
          x = math.exp(score - max_score)
          exp_scores.append(x)
          total_sum += x
  
      probs = []
      for score in exp_scores:
          probs.append(score / total_sum)
      return probs
  
  def main():
      parser = argparse.ArgumentParser()
  
      ## Required parameters
      parser.add_argument("--bert_model", default=None, type=str, required=True,
                          help="Bert pre-trained model selected in the list: bert-base-uncased, "
                          "bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, "
                          "bert-base-multilingual-cased, bert-base-chinese.")
      parser.add_argument("--output_dir", default=None, type=str, required=True,
                          help="The output directory where the model checkpoints and predictions will be written.")
  
      ## Other parameters
      parser.add_argument("--train_file", default=None, type=str, help="SQuAD json for training. E.g., train-v1.1.json")
      parser.add_argument("--predict_file", default=None, type=str,
                          help="SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json")
      parser.add_argument("--max_seq_length", default=384, type=int,
                          help="The maximum total input sequence length after WordPiece tokenization. Sequences "
                               "longer than this will be truncated, and sequences shorter than this will be padded.")
      parser.add_argument("--doc_stride", default=128, type=int,
                          help="When splitting up a long document into chunks, how much stride to take between chunks.")
      parser.add_argument("--max_query_length", default=64, type=int,
                          help="The maximum number of tokens for the question. Questions longer than this will "
                               "be truncated to this length.")
      parser.add_argument("--do_train", action='store_true', help="Whether to run training.")
      parser.add_argument("--do_predict", action='store_true', help="Whether to run eval on the dev set.")
      parser.add_argument("--train_batch_size", default=32, type=int, help="Total batch size for training.")
      parser.add_argument("--predict_batch_size", default=8, type=int, help="Total batch size for predictions.")
      parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
      parser.add_argument("--num_train_epochs", default=3.0, type=float,
                          help="Total number of training epochs to perform.")
      parser.add_argument("--warmup_proportion", default=0.1, type=float,
                          help="Proportion of training to perform linear learning rate warmup for. E.g., 0.1 = 10%% "
                               "of training.")
      parser.add_argument("--n_best_size", default=20, type=int,
                          help="The total number of n-best predictions to generate in the nbest_predictions.json "
                               "output file.")
      parser.add_argument("--max_answer_length", default=30, type=int,
                          help="The maximum length of an answer that can be generated. This is needed because the start "
                               "and end predictions are not conditioned on one another.")
      parser.add_argument("--verbose_logging", action='store_true',
                          help="If true, all of the warnings related to data processing will be printed. "
                               "A number of warnings are expected for a normal SQuAD evaluation.")
      parser.add_argument("--no_cuda",
                          action='store_true',
                          help="Whether not to use CUDA when available")
      parser.add_argument('--seed',
                          type=int,
                          default=42,
                          help="random seed for initialization")
      parser.add_argument('--gradient_accumulation_steps',
                          type=int,
                          default=1,
                          help="Number of updates steps to accumulate before performing a backward/update pass.")
      parser.add_argument("--do_lower_case",
                          action='store_true',
                          help="Whether to lower case the input text. True for uncased models, False for cased models.")
      parser.add_argument("--local_rank",
                          type=int,
                          default=-1,
                          help="local_rank for distributed training on gpus")
      parser.add_argument('--fp16',
                          action='store_true',
                          help="Whether to use 16-bit float precision instead of 32-bit")
      parser.add_argument('--loss_scale',
                          type=float, default=0,
                          help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
                               "0 (default value): dynamic loss scaling.\n"
                               "Positive power of 2: static loss scaling value.\n")
      parser.add_argument('--version_2_with_negative',
                          action='store_true',
                          help='If true, the SQuAD examples contain some that do not have an answer.')
      parser.add_argument('--null_score_diff_threshold',
                          type=float, default=0.0,
                          help="If null_score - best_non_null is greater than the threshold predict null.")
      parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.")
      parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.")
      args = parser.parse_args()
      print(args)
  
      if args.server_ip and args.server_port:
          # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
          import ptvsd
          print("Waiting for debugger attach")
          ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
          ptvsd.wait_for_attach()
  
      if args.local_rank == -1 or args.no_cuda:
          device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
          n_gpu = torch.cuda.device_count()
      else:
          torch.cuda.set_device(args.local_rank)
          device = torch.device("cuda", args.local_rank)
          n_gpu = 1
          # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
          torch.distributed.init_process_group(backend='nccl')
  
      logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
                          datefmt = '%m/%d/%Y %H:%M:%S',
                          level = logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
  
      logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
          device, n_gpu, bool(args.local_rank != -1), args.fp16))
  
      if args.gradient_accumulation_steps < 1:
          raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
                              args.gradient_accumulation_steps))
  
      args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
  
      random.seed(args.seed)
      np.random.seed(args.seed)
      torch.manual_seed(args.seed)
      if n_gpu > 0:
          torch.cuda.manual_seed_all(args.seed)
  
      if not args.do_train and not args.do_predict:
          raise ValueError("At least one of `do_train` or `do_predict` must be True.")
  
      if args.do_train:
          if not args.train_file:
              raise ValueError(
                  "If `do_train` is True, then `train_file` must be specified.")
      if args.do_predict:
          if not args.predict_file:
              raise ValueError(
                  "If `do_predict` is True, then `predict_file` must be specified.")
  
      if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train:
          raise ValueError("Output directory () already exists and is not empty.")
      if not os.path.exists(args.output_dir):
          os.makedirs(args.output_dir)
  
      tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)
  
      # Prepare model
      model = BertForQuestionAnswering_count.from_pretrained(args.bert_model, max_seq_length=args.max_seq_length,         ## change name and add max_seq_length
                  cache_dir=os.path.join(str(PYTORCH_PRETRAINED_BERT_CACHE), 'distributed_{}'.format(args.local_rank)))
  
      if args.fp16:
          model.half()
      model.to(device)
      if args.local_rank != -1:
          try:
              from apex.parallel import DistributedDataParallel as DDP
          except ImportError:
              raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")
  
          model = DDP(model)
      elif n_gpu > 1:
          model = torch.nn.DataParallel(model)
  
      if args.do_train:
  
          # Prepare data loader
  
          train_examples = read_squad_examples(
              input_file=args.train_file, is_training=True, version_2_with_negative=args.version_2_with_negative)
  
          ## Here you could truncate the examples to have a toy set for debugging:
          #train_examples = train_examples[:100]                                                                   ## Reduce number of train examples to ????
          #print('only 100 examples for training')                                                                 ## added
  
          cached_train_features_file = args.train_file+'_count_{0}_{1}_{2}_{3}'.format(                            ## Added _count in string
              list(filter(None, args.bert_model.split('/'))).pop(), str(args.max_seq_length), str(args.doc_stride), str(args.max_query_length))
          try:
              with open(cached_train_features_file, "rb") as reader:
                  train_features = pickle.load(reader)
          except:
              train_features = convert_examples_to_features(
                  examples=train_examples,
                  tokenizer=tokenizer,
                  max_seq_length=args.max_seq_length,
                  doc_stride=args.doc_stride,
                  max_query_length=args.max_query_length,
                  is_training=True)
              if args.local_rank == -1 or torch.distributed.get_rank() == 0:
                  logger.info("  Saving train features into cached file %s", cached_train_features_file)
                  with open(cached_train_features_file, "wb") as writer:
                      pickle.dump(train_features, writer)
          all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long)
          all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long)
          all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long)
          all_start_positions = torch.tensor([f.start_position for f in train_features], dtype=torch.long)
          all_end_positions = torch.tensor([f.end_position for f in train_features], dtype=torch.long)
          all_answers_as_counts = torch.tensor([f.answer_as_counts for f in train_features], dtype=torch.long)     ## added answers_as_counts tensor long
          train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
                                     all_start_positions, all_end_positions, all_answers_as_counts)                ## added all_answers_as_counts
  
          #print("saving feature_check...")                                                                        ## added print
          #feature_check = np.column_stack((all_start_positions, all_end_positions, all_answers_as_counts))        ## Added
          #np.savetxt('feature_check', feature_check)                                                              ## added for detecting bad numbers
  
          if args.local_rank == -1:
              train_sampler = RandomSampler(train_data)
          else:
              train_sampler = DistributedSampler(train_data)
          train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)
          num_train_optimization_steps = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
          if args.local_rank != -1:
              num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size()
  
          # Prepare optimizer
  
          param_optimizer = list(model.named_parameters())
  
          # hack to remove pooler, which is not used
          # thus it produce None grad that break apex
          param_optimizer = [n for n in param_optimizer if 'pooler' not in n[0]]
  
          no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
          optimizer_grouped_parameters = [
              {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},
              {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
              ]
  
          if args.fp16:
              try:
                  from apex.optimizers import FP16_Optimizer
                  from apex.optimizers import FusedAdam
              except ImportError:
                  raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")
  
              optimizer = FusedAdam(optimizer_grouped_parameters,
                                    lr=args.learning_rate,
                                    bias_correction=False,
                                    max_grad_norm=1.0)
              if args.loss_scale == 0:
                  optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
              else:
                  optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale)
              warmup_linear = WarmupLinearSchedule(warmup=args.warmup_proportion,
                                                   t_total=num_train_optimization_steps)
          else:
              optimizer = BertAdam(optimizer_grouped_parameters,
                                   lr=args.learning_rate,
                                   warmup=args.warmup_proportion,
                                   t_total=num_train_optimization_steps)
  
          global_step = 0
  
          logger.info("***** Running training *****")
          logger.info("  Num orig examples = %d", len(train_examples))
          logger.info("  Num split examples = %d", len(train_features))
          logger.info("  Batch size = %d", args.train_batch_size)
          logger.info("  Num steps = %d", num_train_optimization_steps)
  
          model.train()
  
          #losses = []                                                                                    ## Added for "Tensorboard"
          #classification_losses = []                                                                     ## Added for "Tensorboard"
          #accuracies = []                                                                                ## Added for "Tensorboard"
  
          for _ in trange(int(args.num_train_epochs), desc="Epoch"):
              for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])):
                  if n_gpu == 1:
                      batch = tuple(t.to(device) for t in batch) # multi-gpu does scattering it-self
                  input_ids, input_mask, segment_ids, start_positions, end_positions, answers_as_counts = batch          ## added answers_as_counts
                  loss = model(input_ids, segment_ids, input_mask, start_positions, end_positions, answers_as_counts)    ## added answers_as_counts
  
                  #losses.append(loss.item())                                                             ## Added
                  #classification_losses.append(classification_loss.item())                               ## Added
                  #accuracies.append(accuracy.item())                                                     ## Added
  
                  if n_gpu > 1:
                      loss = loss.mean() # mean() to average on multi-gpu.
                  if args.gradient_accumulation_steps > 1:
                      loss = loss / args.gradient_accumulation_steps
  
                  if args.fp16:
                      optimizer.backward(loss)
                  else:
                      loss.backward()
                  if (step + 1) % args.gradient_accumulation_steps == 0:
                      if args.fp16:
                          # modify learning rate with special warm up BERT uses
                          # if args.fp16 is False, BertAdam is used and handles this automatically
                          lr_this_step = args.learning_rate * warmup_linear.get_lr(global_step, args.warmup_proportion)
                          for param_group in optimizer.param_groups:
                              param_group['lr'] = lr_this_step
                      optimizer.step()
                      optimizer.zero_grad()
                      global_step += 1
  
              #data = np.column_stack((losses, classification_losses, accuracies))         ## Added
              #np.savetxt('mt_basic_indicators.out', data)                                 ## Added for graphs
  
      if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
          # Save a trained model, configuration and tokenizer
          model_to_save = model.module if hasattr(model, 'module') else model  # Only save the model it-self
  
          # If we save using the predefined names, we can load using `from_pretrained`
          output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
          output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
  
          torch.save(model_to_save.state_dict(), output_model_file)
          model_to_save.config.to_json_file(output_config_file)
          tokenizer.save_vocabulary(args.output_dir)
  
          # Load a trained model and vocabulary that you have fine-tuned
          model = BertForQuestionAnswering_count.from_pretrained(args.output_dir, args.max_seq_length)    ## Changed name & Added max_seq_length
          tokenizer = BertTokenizer.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
      else:
          model = BertForQuestionAnswering_count.from_pretrained(args.bert_model, args.max_seq_length)    ## Changed name & Added max_seq_length
  
      model.to(device)
  
      if args.do_predict and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
          eval_examples = read_squad_examples(
              input_file=args.predict_file, is_training=False, version_2_with_negative=args.version_2_with_negative)
  
          ## Here you could truncate the dev set to have a toy set for debugging:
          #eval_examples = eval_examples[:100]                                                             ## Reduce number of dev examples to ????
          #print('only 100 examples for evaluation')
  
          eval_features = convert_examples_to_features(
              examples=eval_examples,
              tokenizer=tokenizer,
              max_seq_length=args.max_seq_length,
              doc_stride=args.doc_stride,
              max_query_length=args.max_query_length,
              is_training=False)
  
          logger.info("***** Running predictions *****")
          logger.info("  Num orig examples = %d", len(eval_examples))
          logger.info("  Num split examples = %d", len(eval_features))
          logger.info("  Batch size = %d", args.predict_batch_size)
  
          all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
          all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
          all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
          all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
          eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_example_index)
          # Run prediction for full data
          eval_sampler = SequentialSampler(eval_data)
          eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.predict_batch_size)
  
          model.eval()
          all_results = []
          logger.info("Start evaluating")
          for input_ids, input_mask, segment_ids, example_indices in tqdm(eval_dataloader, desc="Evaluating", disable=args.local_rank not in [-1, 0]):
              if len(all_results) % 1000 == 0:
                  logger.info("Processing example: %d" % (len(all_results)))
              input_ids = input_ids.to(device)
              input_mask = input_mask.to(device)
              segment_ids = segment_ids.to(device)
              with torch.no_grad():
                  batch_start_logits, batch_end_logits, batch_answers_as_counts = model(input_ids, segment_ids, input_mask)       ## added batch_answers_as_counts
              for i, example_index in enumerate(example_indices):
                  start_logits = batch_start_logits[i].detach().cpu().tolist()
                  end_logits = batch_end_logits[i].detach().cpu().tolist()
                  answer_as_counts = batch_answers_as_counts[i].detach().cpu().tolist()             ## added
                  #print("answer_as_counts_preds from model: ", answer_as_counts)                   ## added
                  eval_feature = eval_features[example_index.item()]
                  unique_id = int(eval_feature.unique_id)
                  all_results.append(RawResult(unique_id=unique_id,
                                               start_logits=start_logits,
                                               end_logits=end_logits,
                                               answer_as_counts=answer_as_counts))                  ## added
  
          #print("all_results: ", all_results)                                                      ## added print
  
          output_prediction_file = os.path.join(args.output_dir, "predictions.json")
          output_nbest_file = os.path.join(args.output_dir, "nbest_predictions.json")
          output_null_log_odds_file = os.path.join(args.output_dir, "null_odds.json")
          write_predictions(eval_examples, eval_features, all_results,
                            args.n_best_size, args.max_answer_length,
                            args.do_lower_case, output_prediction_file,
                            output_nbest_file, output_null_log_odds_file, args.verbose_logging,
                            args.version_2_with_negative, args.null_score_diff_threshold)
  
  
  if __name__ == "__main__":
      main()
  