# 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()

tensorflow.compat.v1.gfile.MakeDirs tensorflow.compat.v1.logging.info tensorflow.compat.v1.gfile.Remove tensorflow.compat.v1.flags.DEFINE_string tensorflow.contrib.tpu.TPUConfig tensorflow.contrib.tpu.TPUEstimator tensorflow.compat.v1.gfile.Exists tensorflow.compat.v1.logging.set_verbosity tensorflow.compat.v1.gfile.ListDirectory tensorflow.contrib.cluster_resolver.TPUClusterResolver tensorflow.compat.v1.gfile.GFile tensorflow.compat.v1.app.run