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.
  """BERT finetuning runner."""
  
  import argparse
  import csv
  import logging
  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
  from pytorch_pretrained_bert.modeling import BertForMultipleChoice
  from pytorch_pretrained_bert.optimization import BertAdam, warmup_linear
  from pytorch_pretrained_bert.tokenization import BertTokenizer
  
  logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
                      datefmt = '%m/%d/%Y %H:%M:%S',
                      level = logging.INFO)
  logger = logging.getLogger(__name__)
  
  
  class SwagExample(object):
      """A single training/test example for the SWAG dataset."""
      def __init__(self,
                   swag_id,
                   context_sentence,
                   start_ending,
                   ending_0,
                   ending_1,
                   ending_2,
                   ending_3,
                   label = None):
          self.swag_id = swag_id
          self.context_sentence = context_sentence
          self.start_ending = start_ending
          self.endings = [
              ending_0,
              ending_1,
              ending_2,
              ending_3,
          ]
          self.label = label
  
      def __str__(self):
          return self.__repr__()
  
      def __repr__(self):
          l = [
              "swag_id: {}".format(self.swag_id),
              "context_sentence: {}".format(self.context_sentence),
              "start_ending: {}".format(self.start_ending),
              "ending_0: {}".format(self.endings[0]),
              "ending_1: {}".format(self.endings[1]),
              "ending_2: {}".format(self.endings[2]),
              "ending_3: {}".format(self.endings[3]),
          ]
  
          if self.label is not None:
              l.append("label: {}".format(self.label))
  
          return ", ".join(l)
  
  
  class InputFeatures(object):
      def __init__(self,
                   example_id,
                   choices_features,
                   label
  
      ):
          self.example_id = example_id
          self.choices_features = [
              {
                  'input_ids': input_ids,
                  'input_mask': input_mask,
                  'segment_ids': segment_ids
              }
              for _, input_ids, input_mask, segment_ids in choices_features
          ]
          self.label = label
  
  
  def read_swag_examples(input_file, is_training):
      with open(input_file, 'r', encoding='utf-8') as f:
          reader = csv.reader(f)
          lines = []
          for line in reader:
              if sys.version_info[0] == 2:
                  line = list(unicode(cell, 'utf-8') for cell in line)
              lines.append(line)
  
      if is_training and lines[0][-1] != 'label':
          raise ValueError(
              "For training, the input file must contain a label column."
          )
  
      examples = [
          SwagExample(
              swag_id = line[2],
              context_sentence = line[4],
              start_ending = line[5], # in the swag dataset, the
                                           # common beginning of each
                                           # choice is stored in "sent2".
              ending_0 = line[7],
              ending_1 = line[8],
              ending_2 = line[9],
              ending_3 = line[10],
              label = int(line[11]) if is_training else None
          ) for line in lines[1:] # we skip the line with the column names
      ]
  
      return examples
  
  def convert_examples_to_features(examples, tokenizer, max_seq_length,
                                   is_training):
      """Loads a data file into a list of `InputBatch`s."""
  
      # Swag is a multiple choice task. To perform this task using Bert,
      # we will use the formatting proposed in "Improving Language
      # Understanding by Generative Pre-Training" and suggested by
      # @jacobdevlin-google in this issue
      # https://github.com/google-research/bert/issues/38.
      #
      # Each choice will correspond to a sample on which we run the
      # inference. For a given Swag example, we will create the 4
      # following inputs:
      # - [CLS] context [SEP] choice_1 [SEP]
      # - [CLS] context [SEP] choice_2 [SEP]
      # - [CLS] context [SEP] choice_3 [SEP]
      # - [CLS] context [SEP] choice_4 [SEP]
      # The model will output a single value for each input. To get the
      # final decision of the model, we will run a softmax over these 4
      # outputs.
      features = []
      for example_index, example in enumerate(examples):
          context_tokens = tokenizer.tokenize(example.context_sentence)
          start_ending_tokens = tokenizer.tokenize(example.start_ending)
  
          choices_features = []
          for ending_index, ending in enumerate(example.endings):
              # We create a copy of the context tokens in order to be
              # able to shrink it according to ending_tokens
              context_tokens_choice = context_tokens[:]
              ending_tokens = start_ending_tokens + tokenizer.tokenize(ending)
              # Modifies `context_tokens_choice` and `ending_tokens` in
              # place so that the total length is less than the
              # specified length.  Account for [CLS], [SEP], [SEP] with
              # "- 3"
              _truncate_seq_pair(context_tokens_choice, ending_tokens, max_seq_length - 3)
  
              tokens = ["[CLS]"] + context_tokens_choice + ["[SEP]"] + ending_tokens + ["[SEP]"]
              segment_ids = [0] * (len(context_tokens_choice) + 2) + [1] * (len(ending_tokens) + 1)
  
              input_ids = tokenizer.convert_tokens_to_ids(tokens)
              input_mask = [1] * len(input_ids)
  
              # Zero-pad up to the sequence length.
              padding = [0] * (max_seq_length - len(input_ids))
              input_ids += padding
              input_mask += padding
              segment_ids += padding
  
              assert len(input_ids) == max_seq_length
              assert len(input_mask) == max_seq_length
              assert len(segment_ids) == max_seq_length
  
              choices_features.append((tokens, input_ids, input_mask, segment_ids))
  
          label = example.label
          if example_index < 5:
              logger.info("*** Example ***")
              logger.info("swag_id: {}".format(example.swag_id))
              for choice_idx, (tokens, input_ids, input_mask, segment_ids) in enumerate(choices_features):
                  logger.info("choice: {}".format(choice_idx))
                  logger.info("tokens: {}".format(' '.join(tokens)))
                  logger.info("input_ids: {}".format(' '.join(map(str, input_ids))))
                  logger.info("input_mask: {}".format(' '.join(map(str, input_mask))))
                  logger.info("segment_ids: {}".format(' '.join(map(str, segment_ids))))
              if is_training:
                  logger.info("label: {}".format(label))
  
          features.append(
              InputFeatures(
                  example_id = example.swag_id,
                  choices_features = choices_features,
                  label = label
              )
          )
  
      return features
  
  def _truncate_seq_pair(tokens_a, tokens_b, max_length):
      """Truncates a sequence pair in place to the maximum length."""
  
      # This is a simple heuristic which will always truncate the longer sequence
      # one token at a time. This makes more sense than truncating an equal percent
      # of tokens from each, since if one sequence is very short then each token
      # that's truncated likely contains more information than a longer sequence.
      while True:
          total_length = len(tokens_a) + len(tokens_b)
          if total_length <= max_length:
              break
          if len(tokens_a) > len(tokens_b):
              tokens_a.pop()
          else:
              tokens_b.pop()
  
  def accuracy(out, labels):
      outputs = np.argmax(out, axis=1)
      return np.sum(outputs == labels)
  
  def select_field(features, field):
      return [
          [
              choice[field]
              for choice in feature.choices_features
          ]
          for feature in features
      ]
  
  def main():
      parser = argparse.ArgumentParser()
  
      ## Required parameters
      parser.add_argument("--data_dir",
                          default=None,
                          type=str,
                          required=True,
                          help="The input data dir. Should contain the .csv files (or other data files) for the task.")
      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 will be written.")
  
      ## Other parameters
      parser.add_argument("--max_seq_length",
                          default=128,
                          type=int,
                          help="The maximum total input sequence length after WordPiece tokenization. \n"
                               "Sequences longer than this will be truncated, and sequences shorter \n"
                               "than this will be padded.")
      parser.add_argument("--do_train",
                          action='store_true',
                          help="Whether to run training.")
      parser.add_argument("--do_eval",
                          action='store_true',
                          help="Whether to run eval on the dev set.")
      parser.add_argument("--do_lower_case",
                          action='store_true',
                          help="Set this flag if you are using an uncased model.")
      parser.add_argument("--train_batch_size",
                          default=32,
                          type=int,
                          help="Total batch size for training.")
      parser.add_argument("--eval_batch_size",
                          default=8,
                          type=int,
                          help="Total batch size for eval.")
      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("--no_cuda",
                          action='store_true',
                          help="Whether not to use CUDA when available")
      parser.add_argument("--local_rank",
                          type=int,
                          default=-1,
                          help="local_rank for distributed training on gpus")
      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('--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")
  
      args = parser.parse_args()
  
      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')
      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_eval:
          raise ValueError("At least one of `do_train` or `do_eval` must be True.")
  
      if os.path.exists(args.output_dir) and os.listdir(args.output_dir):
          raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
      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)
  
      train_examples = None
      num_train_optimization_steps = None
      if args.do_train:
          train_examples = read_swag_examples(os.path.join(args.data_dir, 'train.csv'), is_training = True)
          num_train_optimization_steps = int(
              len(train_examples) / args.train_batch_size / 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 model
      model = BertForMultipleChoice.from_pretrained(args.bert_model,
          cache_dir=os.path.join(str(PYTORCH_PRETRAINED_BERT_CACHE), 'distributed_{}'.format(args.local_rank)),
          num_choices=4)
      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)
  
      # 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)
      else:
          optimizer = BertAdam(optimizer_grouped_parameters,
                               lr=args.learning_rate,
                               warmup=args.warmup_proportion,
                               t_total=num_train_optimization_steps)
  
      global_step = 0
      if args.do_train:
          train_features = convert_examples_to_features(
              train_examples, tokenizer, args.max_seq_length, True)
          logger.info("***** Running training *****")
          logger.info("  Num examples = %d", len(train_examples))
          logger.info("  Batch size = %d", args.train_batch_size)
          logger.info("  Num steps = %d", num_train_optimization_steps)
          all_input_ids = torch.tensor(select_field(train_features, 'input_ids'), dtype=torch.long)
          all_input_mask = torch.tensor(select_field(train_features, 'input_mask'), dtype=torch.long)
          all_segment_ids = torch.tensor(select_field(train_features, 'segment_ids'), dtype=torch.long)
          all_label = torch.tensor([f.label for f in train_features], dtype=torch.long)
          train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label)
          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)
  
          model.train()
          for _ in trange(int(args.num_train_epochs), desc="Epoch"):
              tr_loss = 0
              nb_tr_examples, nb_tr_steps = 0, 0
              for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")):
                  batch = tuple(t.to(device) for t in batch)
                  input_ids, input_mask, segment_ids, label_ids = batch
                  loss = model(input_ids, segment_ids, input_mask, label_ids)
                  if n_gpu > 1:
                      loss = loss.mean() # mean() to average on multi-gpu.
                  if args.fp16 and args.loss_scale != 1.0:
                      # rescale loss for fp16 training
                      # see https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html
                      loss = loss * args.loss_scale
                  if args.gradient_accumulation_steps > 1:
                      loss = loss / args.gradient_accumulation_steps
                  tr_loss += loss.item()
                  nb_tr_examples += input_ids.size(0)
                  nb_tr_steps += 1
  
                  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 that handles this automatically
                          lr_this_step = args.learning_rate * warmup_linear(global_step/num_train_optimization_steps, args.warmup_proportion)
                          for param_group in optimizer.param_groups:
                              param_group['lr'] = lr_this_step
                      optimizer.step()
                      optimizer.zero_grad()
                      global_step += 1
  
  
      if args.do_train:
          # Save a trained model and the associated configuration
          model_to_save = model.module if hasattr(model, 'module') else model  # Only save the model it-self
          output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
          torch.save(model_to_save.state_dict(), output_model_file)
          output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
          with open(output_config_file, 'w') as f:
              f.write(model_to_save.config.to_json_string())
  
          # Load a trained model and config that you have fine-tuned
          config = BertConfig(output_config_file)
          model = BertForMultipleChoice(config, num_choices=4)
          model.load_state_dict(torch.load(output_model_file))
      else:
          model = BertForMultipleChoice.from_pretrained(args.bert_model, num_choices=4)
      model.to(device)
  
  
      if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
          eval_examples = read_swag_examples(os.path.join(args.data_dir, 'val.csv'), is_training = True)
          eval_features = convert_examples_to_features(
              eval_examples, tokenizer, args.max_seq_length, True)
          logger.info("***** Running evaluation *****")
          logger.info("  Num examples = %d", len(eval_examples))
          logger.info("  Batch size = %d", args.eval_batch_size)
          all_input_ids = torch.tensor(select_field(eval_features, 'input_ids'), dtype=torch.long)
          all_input_mask = torch.tensor(select_field(eval_features, 'input_mask'), dtype=torch.long)
          all_segment_ids = torch.tensor(select_field(eval_features, 'segment_ids'), dtype=torch.long)
          all_label = torch.tensor([f.label for f in eval_features], dtype=torch.long)
          eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label)
          # Run prediction for full data
          eval_sampler = SequentialSampler(eval_data)
          eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
  
          model.eval()
          eval_loss, eval_accuracy = 0, 0
          nb_eval_steps, nb_eval_examples = 0, 0
          for input_ids, input_mask, segment_ids, label_ids in eval_dataloader:
              input_ids = input_ids.to(device)
              input_mask = input_mask.to(device)
              segment_ids = segment_ids.to(device)
              label_ids = label_ids.to(device)
  
              with torch.no_grad():
                  tmp_eval_loss = model(input_ids, segment_ids, input_mask, label_ids)
                  logits = model(input_ids, segment_ids, input_mask)
  
              logits = logits.detach().cpu().numpy()
              label_ids = label_ids.to('cpu').numpy()
              tmp_eval_accuracy = accuracy(logits, label_ids)
  
              eval_loss += tmp_eval_loss.mean().item()
              eval_accuracy += tmp_eval_accuracy
  
              nb_eval_examples += input_ids.size(0)
              nb_eval_steps += 1
  
          eval_loss = eval_loss / nb_eval_steps
          eval_accuracy = eval_accuracy / nb_eval_examples
  
          result = {'eval_loss': eval_loss,
                    'eval_accuracy': eval_accuracy,
                    'global_step': global_step,
                    'loss': tr_loss/nb_tr_steps}
  
          output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
          with open(output_eval_file, "w") as writer:
              logger.info("***** Eval results *****")
              for key in sorted(result.keys()):
                  logger.info("  %s = %s", key, str(result[key]))
                  writer.write("%s = %s\n" % (key, str(result[key])))
  
  
  if __name__ == "__main__":
      main()
  