code: # coding=utf-8
  # Copyright 2018 The Google AI Team Authors.
  #
  # 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 on classification tasks."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import os
  import time
  from albert import classifier_utils
  from albert import fine_tuning_utils
  from albert import modeling
  import tensorflow.compat.v1 as tf
  from tensorflow.contrib import cluster_resolver as contrib_cluster_resolver
  from tensorflow.contrib import tpu as contrib_tpu
  
  flags = tf.flags
  
  FLAGS = flags.FLAGS
  
  ## Required parameters
  flags.DEFINE_string(
      "data_dir",
      None,
      "The input data dir. Should contain the .tsv files (or other data files) "
      "for the task.",
  )
  
  flags.DEFINE_string(
      "albert_config_file",
      None,
      "The config json file corresponding to the pre-trained ALBERT model. "
      "This specifies the model architecture.",
  )
  
  flags.DEFINE_string("task_name", None, "The name of the task to train.")
  
  flags.DEFINE_string(
      "vocab_file", None, "The vocabulary file that the ALBERT model was trained on."
  )
  
  flags.DEFINE_string(
      "spm_model_file", None, "The model file for sentence piece tokenization."
  )
  
  flags.DEFINE_string(
      "output_dir",
      None,
      "The output directory where the model checkpoints will be written.",
  )
  
  flags.DEFINE_string(
      "cached_dir",
      None,
      "Path to cached training and dev tfrecord file. "
      "The file will be generated if not exist.",
  )
  
  ## Other parameters
  flags.DEFINE_float("train_subset", 1.0, "The subset of the train set.")
  flags.DEFINE_float("dev_subset", 1.0, "The subset of the dev set.")
  flags.DEFINE_float("test_subset", 1.0, "The subset of the test set.")
  
  flags.DEFINE_string(
      "init_checkpoint",
      None,
      "Initial checkpoint (usually from a pre-trained BERT model).",
  )
  
  flags.DEFINE_string(
      "albert_hub_module_handle", None, "If set, the ALBERT hub module to use."
  )
  
  flags.DEFINE_bool(
      "do_lower_case",
      True,
      "Whether to lower case the input text. Should be True for uncased "
      "models and False for cased models.",
  )
  
  flags.DEFINE_integer(
      "max_seq_length",
      512,
      "The maximum total input sequence length after WordPiece tokenization. "
      "Sequences longer than this will be truncated, and sequences shorter "
      "than this will be padded.",
  )
  
  flags.DEFINE_bool("do_train", False, "Whether to run training.")
  
  flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.")
  
  flags.DEFINE_bool(
      "do_predict", False, "Whether to run the model in inference mode on the test set."
  )
  
  flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.")
  
  flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.")
  
  flags.DEFINE_integer("predict_batch_size", 8, "Total batch size for predict.")
  
  flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.")
  
  flags.DEFINE_integer("train_step", 1000, "Total number of training steps to perform.")
  
  flags.DEFINE_integer(
      "warmup_step", 0, "number of steps to perform linear learning rate warmup for."
  )
  
  flags.DEFINE_integer(
      "save_checkpoints_steps", 1000, "How often to save the model checkpoint."
  )
  
  flags.DEFINE_integer("keep_checkpoint_max", 5, "How many checkpoints to keep.")
  
  flags.DEFINE_integer(
      "iterations_per_loop", 1000, "How many steps to make in each estimator call."
  )
  
  flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")
  
  flags.DEFINE_string("optimizer", "adamw", "Optimizer to use")
  
  tf.flags.DEFINE_string(
      "tpu_name",
      None,
      "The Cloud TPU to use for training. This should be either the name "
      "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 "
      "url.",
  )
  
  tf.flags.DEFINE_string(
      "tpu_zone",
      None,
      "[Optional] GCE zone where the Cloud TPU is located in. If not "
      "specified, we will attempt to automatically detect the GCE project from "
      "metadata.",
  )
  
  tf.flags.DEFINE_string(
      "gcp_project",
      None,
      "[Optional] Project name for the Cloud TPU-enabled project. If not "
      "specified, we will attempt to automatically detect the GCE project from "
      "metadata.",
  )
  
  tf.flags.DEFINE_string("master", None, "[Optional] TensorFlow master URL.")
  
  flags.DEFINE_integer(
      "num_tpu_cores",
      8,
      "Only used if `use_tpu` is True. Total number of TPU cores to use.",
  )
  
  
  def main(_):
      tf.logging.set_verbosity(tf.logging.INFO)
  
      processors = {
          "cola": classifier_utils.ColaProcessor,
          "mnli": classifier_utils.MnliProcessor,
          "mismnli": classifier_utils.MisMnliProcessor,
          "mrpc": classifier_utils.MrpcProcessor,
          "rte": classifier_utils.RteProcessor,
          "sst-2": classifier_utils.Sst2Processor,
          "sts-b": classifier_utils.StsbProcessor,
          "qqp": classifier_utils.QqpProcessor,
          "qnli": classifier_utils.QnliProcessor,
          "wnli": classifier_utils.WnliProcessor,
      }
  
      if not FLAGS.do_train and not FLAGS.do_eval and not FLAGS.do_predict:
          raise ValueError(
              "At least one of `do_train`, `do_eval` or `do_predict' must be True."
          )
  
      if not FLAGS.albert_config_file and not FLAGS.albert_hub_module_handle:
          raise ValueError(
              "At least one of `--albert_config_file` and "
              "`--albert_hub_module_handle` must be set"
          )
  
      if FLAGS.albert_config_file:
          albert_config = modeling.AlbertConfig.from_json_file(FLAGS.albert_config_file)
          if FLAGS.max_seq_length > albert_config.max_position_embeddings:
              raise ValueError(
                  "Cannot use sequence length %d because the ALBERT model "
                  "was only trained up to sequence length %d"
                  % (FLAGS.max_seq_length, albert_config.max_position_embeddings)
              )
      else:
          albert_config = None  # Get the config from TF-Hub.
  
      tf.gfile.MakeDirs(FLAGS.output_dir)
  
      task_name = FLAGS.task_name.lower()
  
      if task_name not in processors:
          raise ValueError("Task not found: %s" % (task_name))
  
      processor = processors[task_name](
          use_spm=True if FLAGS.spm_model_file else False,
          do_lower_case=FLAGS.do_lower_case,
      )
  
      label_list = processor.get_labels()
  
      tokenizer = fine_tuning_utils.create_vocab(
          vocab_file=FLAGS.vocab_file,
          do_lower_case=FLAGS.do_lower_case,
          spm_model_file=FLAGS.spm_model_file,
          hub_module=FLAGS.albert_hub_module_handle,
      )
  
      tpu_cluster_resolver = None
      if FLAGS.use_tpu and FLAGS.tpu_name:
          tpu_cluster_resolver = contrib_cluster_resolver.TPUClusterResolver(
              FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project
          )
  
      is_per_host = contrib_tpu.InputPipelineConfig.PER_HOST_V2
      if FLAGS.do_train:
          iterations_per_loop = int(
              min(FLAGS.iterations_per_loop, FLAGS.save_checkpoints_steps)
          )
      else:
          iterations_per_loop = FLAGS.iterations_per_loop
      run_config = contrib_tpu.RunConfig(
          cluster=tpu_cluster_resolver,
          master=FLAGS.master,
          model_dir=FLAGS.output_dir,
          save_checkpoints_steps=int(FLAGS.save_checkpoints_steps),
          keep_checkpoint_max=0,
          tpu_config=contrib_tpu.TPUConfig(
              iterations_per_loop=iterations_per_loop,
              num_shards=FLAGS.num_tpu_cores,
              per_host_input_for_training=is_per_host,
          ),
      )
  
      train_examples = None
      if FLAGS.do_train:
          train_examples = processor.get_train_examples(
              FLAGS.data_dir, FLAGS.train_subset
          )
      model_fn = classifier_utils.model_fn_builder(
          albert_config=albert_config,
          num_labels=len(label_list),
          init_checkpoint=FLAGS.init_checkpoint,
          learning_rate=FLAGS.learning_rate,
          num_train_steps=FLAGS.train_step,
          num_warmup_steps=FLAGS.warmup_step,
          use_tpu=FLAGS.use_tpu,
          use_one_hot_embeddings=FLAGS.use_tpu,
          task_name=task_name,
          hub_module=FLAGS.albert_hub_module_handle,
          optimizer=FLAGS.optimizer,
      )
  
      # If TPU is not available, this will fall back to normal Estimator on CPU
      # or GPU.
      estimator = contrib_tpu.TPUEstimator(
          use_tpu=FLAGS.use_tpu,
          model_fn=model_fn,
          config=run_config,
          train_batch_size=FLAGS.train_batch_size,
          eval_batch_size=FLAGS.eval_batch_size,
          predict_batch_size=FLAGS.predict_batch_size,
      )
  
      if FLAGS.do_train:
          cached_dir = FLAGS.cached_dir
          if not cached_dir:
              cached_dir = FLAGS.output_dir
          train_file = os.path.join(cached_dir, task_name + "_train.tf_record")
          if not tf.gfile.Exists(train_file):
              classifier_utils.file_based_convert_examples_to_features(
                  train_examples,
                  label_list,
                  FLAGS.max_seq_length,
                  tokenizer,
                  train_file,
                  task_name,
              )
          tf.logging.info("***** Running training *****")
          tf.logging.info("  Num examples = %d", len(train_examples))
          tf.logging.info("  Batch size = %d", FLAGS.train_batch_size)
          tf.logging.info("  Num steps = %d", FLAGS.train_step)
          train_input_fn = classifier_utils.file_based_input_fn_builder(
              input_file=train_file,
              seq_length=FLAGS.max_seq_length,
              is_training=True,
              drop_remainder=True,
              task_name=task_name,
              use_tpu=FLAGS.use_tpu,
              bsz=FLAGS.train_batch_size,
          )
          estimator.train(input_fn=train_input_fn, max_steps=FLAGS.train_step)
  
      if FLAGS.do_eval:
          eval_examples = processor.get_dev_examples(FLAGS.data_dir, FLAGS.dev_subset)
          num_actual_eval_examples = len(eval_examples)
          if FLAGS.use_tpu:
              # TPU requires a fixed batch size for all batches, therefore the number
              # of examples must be a multiple of the batch size, or else examples
              # will get dropped. So we pad with fake examples which are ignored
              # later on. These do NOT count towards the metric (all tf.metrics
              # support a per-instance weight, and these get a weight of 0.0).
              while len(eval_examples) % FLAGS.eval_batch_size != 0:
                  eval_examples.append(classifier_utils.PaddingInputExample())
  
          cached_dir = FLAGS.cached_dir
          if not cached_dir:
              cached_dir = FLAGS.output_dir
          eval_file = os.path.join(cached_dir, task_name + "_eval.tf_record")
          if not tf.gfile.Exists(eval_file):
              classifier_utils.file_based_convert_examples_to_features(
                  eval_examples,
                  label_list,
                  FLAGS.max_seq_length,
                  tokenizer,
                  eval_file,
                  task_name,
              )
  
          tf.logging.info("***** Running evaluation *****")
          tf.logging.info(
              "  Num examples = %d (%d actual, %d padding)",
              len(eval_examples),
              num_actual_eval_examples,
              len(eval_examples) - num_actual_eval_examples,
          )
          tf.logging.info("  Batch size = %d", FLAGS.eval_batch_size)
  
          # This tells the estimator to run through the entire set.
          eval_steps = None
          # However, if running eval on the TPU, you will need to specify the
          # number of steps.
          if FLAGS.use_tpu:
              assert len(eval_examples) % FLAGS.eval_batch_size == 0
              eval_steps = int(len(eval_examples) // FLAGS.eval_batch_size)
  
          eval_drop_remainder = True if FLAGS.use_tpu else False
          eval_input_fn = classifier_utils.file_based_input_fn_builder(
              input_file=eval_file,
              seq_length=FLAGS.max_seq_length,
              is_training=False,
              drop_remainder=eval_drop_remainder,
              task_name=task_name,
              use_tpu=FLAGS.use_tpu,
              bsz=FLAGS.eval_batch_size,
          )
  
          best_trial_info_file = os.path.join(FLAGS.output_dir, "best_trial.txt")
  
          def _best_trial_info():
              """Returns information about which checkpoints have been evaled so far."""
              if tf.gfile.Exists(best_trial_info_file):
                  with tf.gfile.GFile(best_trial_info_file, "r") as best_info:
                      (
                          global_step,
                          best_metric_global_step,
                          metric_value,
                      ) = best_info.read().split(":")
                      global_step = int(global_step)
                      best_metric_global_step = int(best_metric_global_step)
                      metric_value = float(metric_value)
              else:
                  metric_value = -1
                  best_metric_global_step = -1
                  global_step = -1
              tf.logging.info(
                  "Best trial info: Step: %s, Best Value Step: %s, " "Best Value: %s",
                  global_step,
                  best_metric_global_step,
                  metric_value,
              )
              return global_step, best_metric_global_step, metric_value
  
          def _remove_checkpoint(checkpoint_path):
              for ext in ["meta", "data-00000-of-00001", "index"]:
                  src_ckpt = checkpoint_path + ".{}".format(ext)
                  tf.logging.info("removing {}".format(src_ckpt))
                  tf.gfile.Remove(src_ckpt)
  
          def _find_valid_cands(curr_step):
              filenames = tf.gfile.ListDirectory(FLAGS.output_dir)
              candidates = []
              for filename in filenames:
                  if filename.endswith(".index"):
                      ckpt_name = filename[:-6]
                      idx = ckpt_name.split("-")[-1]
                      if int(idx) > curr_step:
                          candidates.append(filename)
              return candidates
  
          output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
  
          if task_name == "sts-b":
              key_name = "pearson"
          elif task_name == "cola":
              key_name = "matthew_corr"
          else:
              key_name = "eval_accuracy"
  
          global_step, best_perf_global_step, best_perf = _best_trial_info()
          writer = tf.gfile.GFile(output_eval_file, "w")
          while global_step < FLAGS.train_step:
              steps_and_files = {}
              filenames = tf.gfile.ListDirectory(FLAGS.output_dir)
              for filename in filenames:
                  if filename.endswith(".index"):
                      ckpt_name = filename[:-6]
                      cur_filename = os.path.join(FLAGS.output_dir, ckpt_name)
                      if cur_filename.split("-")[-1] == "best":
                          continue
                      gstep = int(cur_filename.split("-")[-1])
                      if gstep not in steps_and_files:
                          tf.logging.info("Add {} to eval list.".format(cur_filename))
                          steps_and_files[gstep] = cur_filename
              tf.logging.info("found {} files.".format(len(steps_and_files)))
              if not steps_and_files:
                  tf.logging.info(
                      "found 0 file, global step: {}. Sleeping.".format(global_step)
                  )
                  time.sleep(60)
              else:
                  for checkpoint in sorted(steps_and_files.items()):
                      step, checkpoint_path = checkpoint
                      if global_step >= step:
                          if (
                              best_perf_global_step != step
                              and len(_find_valid_cands(step)) > 1
                          ):
                              _remove_checkpoint(checkpoint_path)
                          continue
                      result = estimator.evaluate(
                          input_fn=eval_input_fn,
                          steps=eval_steps,
                          checkpoint_path=checkpoint_path,
                      )
                      global_step = result["global_step"]
                      tf.logging.info("***** Eval results *****")
                      for key in sorted(result.keys()):
                          tf.logging.info("  %s = %s", key, str(result[key]))
                          writer.write("%s = %s\n" % (key, str(result[key])))
                      writer.write("best = {}\n".format(best_perf))
                      if result[key_name] > best_perf:
                          best_perf = result[key_name]
                          best_perf_global_step = global_step
                      elif len(_find_valid_cands(global_step)) > 1:
                          _remove_checkpoint(checkpoint_path)
                      writer.write("=" * 50 + "\n")
                      writer.flush()
                      with tf.gfile.GFile(best_trial_info_file, "w") as best_info:
                          best_info.write(
                              "{}:{}:{}".format(
                                  global_step, best_perf_global_step, best_perf
                              )
                          )
          writer.close()
  
          for ext in ["meta", "data-00000-of-00001", "index"]:
              src_ckpt = "model.ckpt-{}.{}".format(best_perf_global_step, ext)
              tgt_ckpt = "model.ckpt-best.{}".format(ext)
              tf.logging.info("saving {} to {}".format(src_ckpt, tgt_ckpt))
              tf.io.gfile.rename(
                  os.path.join(FLAGS.output_dir, src_ckpt),
                  os.path.join(FLAGS.output_dir, tgt_ckpt),
                  overwrite=True,
              )
  
      if FLAGS.do_predict:
          predict_examples = processor.get_test_examples(
              FLAGS.data_dir, FLAGS.test_subset
          )
          num_actual_predict_examples = len(predict_examples)
          if FLAGS.use_tpu:
              # TPU requires a fixed batch size for all batches, therefore the number
              # of examples must be a multiple of the batch size, or else examples
              # will get dropped. So we pad with fake examples which are ignored
              # later on.
              while len(predict_examples) % FLAGS.predict_batch_size != 0:
                  predict_examples.append(classifier_utils.PaddingInputExample())
  
          predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record")
          classifier_utils.file_based_convert_examples_to_features(
              predict_examples,
              label_list,
              FLAGS.max_seq_length,
              tokenizer,
              predict_file,
              task_name,
          )
  
          tf.logging.info("***** Running prediction*****")
          tf.logging.info(
              "  Num examples = %d (%d actual, %d padding)",
              len(predict_examples),
              num_actual_predict_examples,
              len(predict_examples) - num_actual_predict_examples,
          )
          tf.logging.info("  Batch size = %d", FLAGS.predict_batch_size)
  
          predict_drop_remainder = True if FLAGS.use_tpu else False
          predict_input_fn = classifier_utils.file_based_input_fn_builder(
              input_file=predict_file,
              seq_length=FLAGS.max_seq_length,
              is_training=False,
              drop_remainder=predict_drop_remainder,
              task_name=task_name,
              use_tpu=FLAGS.use_tpu,
              bsz=FLAGS.predict_batch_size,
          )
  
          checkpoint_path = os.path.join(FLAGS.output_dir, "model.ckpt-best")
          result = estimator.predict(
              input_fn=predict_input_fn, checkpoint_path=checkpoint_path
          )
  
          output_predict_file = os.path.join(FLAGS.output_dir, "test_results.tsv")
          output_submit_file = os.path.join(FLAGS.output_dir, "submit_results.tsv")
          with tf.gfile.GFile(output_predict_file, "w") as pred_writer, tf.gfile.GFile(
              output_submit_file, "w"
          ) as sub_writer:
              sub_writer.write("index" + "\t" + "prediction\n")
              num_written_lines = 0
              tf.logging.info("***** Predict results *****")
              for (i, (example, prediction)) in enumerate(zip(predict_examples, result)):
                  probabilities = prediction["probabilities"]
                  if i >= num_actual_predict_examples:
                      break
                  output_line = (
                      "\t".join(
                          str(class_probability) for class_probability in probabilities
                      )
                      + "\n"
                  )
                  pred_writer.write(output_line)
  
                  if task_name != "sts-b":
                      actual_label = label_list[int(prediction["predictions"])]
                  else:
                      actual_label = str(prediction["predictions"])
                  sub_writer.write(example.guid + "\t" + actual_label + "\n")
                  num_written_lines += 1
          assert num_written_lines == num_actual_predict_examples
  
  
  if __name__ == "__main__":
      flags.mark_flag_as_required("data_dir")
      flags.mark_flag_as_required("task_name")
      flags.mark_flag_as_required("spm_model_file")
      flags.mark_flag_as_required("output_dir")
      tf.app.run()
  
apis: tensorflow.compat.v1.gfile.Remove tensorflow.compat.v1.gfile.ListDirectory tensorflow.contrib.tpu.TPUConfig tensorflow.contrib.tpu.TPUEstimator tensorflow.compat.v1.gfile.Exists tensorflow.compat.v1.gfile.GFile tensorflow.contrib.cluster_resolver.TPUClusterResolver tensorflow.compat.v1.flags.DEFINE_string tensorflow.compat.v1.logging.info tensorflow.compat.v1.app.run tensorflow.compat.v1.gfile.MakeDirs tensorflow.compat.v1.logging.set_verbosity