# Source code for nlp_architect.procedures.transformers.seq_tag

    # ******************************************************************************
    # Copyright 2017-2019 Intel Corporation
    #
    # 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.
    # ******************************************************************************
    import argparse
    import io
    import logging
    import os

    from torch.utils.data import DataLoader, RandomSampler, SequentialSampler

    from nlp_architect.data.sequential_tagging import TokenClsInputExample, TokenClsProcessor
    from nlp_architect.data.utils import write_column_tagged_file
    from nlp_architect.models.transformers import TransformerTokenClassifier
    from nlp_architect.nn.torch import setup_backend, set_seed
    from nlp_architect.procedures.procedure import Procedure
    from nlp_architect.procedures.registry import register_inference_cmd, register_train_cmd
    from nlp_architect.procedures.transformers.base import create_base_args, inference_args, train_args
    from nlp_architect.utils.io import prepare_output_path
    from nlp_architect.utils.text import SpacyInstance

    logger = logging.getLogger(__name__)

    @register_train_cmd(
        name="transformer_token", description="Train a BERT/XLNet model with token classification head"
    )
    class TransformerTokenClsTrain(Procedure):
        @staticmethod
        def add_arguments(parser: argparse.ArgumentParser):
            parser.add_argument(
                "--data_dir",
                default=None,
                type=str,
                required=True,
                help="The input data dir. Should contain dataset files to be parsed "
                + "by the dataloaders.",
            )
            train_args(parser, models_family=TransformerTokenClassifier.MODEL_CLASS.keys())
            create_base_args(parser, model_types=TransformerTokenClassifier.MODEL_CLASS.keys())
            parser.add_argument(
                "--train_file_name",
                type=str,
                default="train.txt",
                help="File name of the training dataset",
            )

        @staticmethod
        def run_procedure(args):
            do_training(args)

    @register_inference_cmd(
        name="transformer_token", description="Run a BERT/XLNet model with token classification head"
    )
    class TransformerTokenClsRun(Procedure):
        @staticmethod
        def add_arguments(parser: argparse.ArgumentParser):
            parser.add_argument(
                "--data_file",
                default=None,
                type=str,
                required=True,
                help="The data file containing data for inference",
            )
            inference_args(parser)
            create_base_args(parser, model_types=TransformerTokenClassifier.MODEL_CLASS.keys())

        @staticmethod
        def run_procedure(args):
            do_inference(args)

    def do_training(args):
        prepare_output_path(args.output_dir, args.overwrite_output_dir)
        device, n_gpus = setup_backend(args.no_cuda)
        # Set seed
        args.seed = set_seed(args.seed, n_gpus)
        # prepare data
        processor = TokenClsProcessor(args.data_dir)

        classifier = TransformerTokenClassifier(
            model_type=args.model_type,
            model_name_or_path=args.model_name_or_path,
            labels=processor.get_labels(),
            config_name=args.config_name,
            tokenizer_name=args.tokenizer_name,
            do_lower_case=args.do_lower_case,
            output_path=args.output_dir,
            device=device,
            n_gpus=n_gpus,
        )

        train_ex = processor.get_train_examples(filename=args.train_file_name)
        if train_ex is None:
            raise Exception("No train examples found, quitting.")
        dev_ex = processor.get_dev_examples()
        test_ex = processor.get_test_examples()

        train_batch_size = args.per_gpu_train_batch_size * max(1, n_gpus)

        train_dataset = classifier.convert_to_tensors(train_ex, max_seq_length=args.max_seq_length)
        train_sampler = RandomSampler(train_dataset)
        train_dl = DataLoader(train_dataset, sampler=train_sampler, batch_size=train_batch_size)
        dev_dl = None
        test_dl = None
        if dev_ex is not None:
            dev_dataset = classifier.convert_to_tensors(dev_ex, max_seq_length=args.max_seq_length)
            dev_sampler = SequentialSampler(dev_dataset)
            dev_dl = DataLoader(
                dev_dataset, sampler=dev_sampler, batch_size=args.per_gpu_eval_batch_size
            )

        if test_ex is not None:
            test_dataset = classifier.convert_to_tensors(test_ex, max_seq_length=args.max_seq_length)
            test_sampler = SequentialSampler(test_dataset)
            test_dl = DataLoader(
                test_dataset, sampler=test_sampler, batch_size=args.per_gpu_eval_batch_size
            )

        total_steps, _ = classifier.get_train_steps_epochs(
            args.max_steps, args.num_train_epochs, args.per_gpu_train_batch_size, len(train_dataset)
        )

        classifier.setup_default_optimizer(
            weight_decay=args.weight_decay,
            learning_rate=args.learning_rate,
            adam_epsilon=args.adam_epsilon,
            warmup_steps=args.warmup_steps,
            total_steps=total_steps,
        )
        classifier.train(
            train_dl,
            dev_dl,
            test_dl,
            gradient_accumulation_steps=args.gradient_accumulation_steps,
            per_gpu_train_batch_size=args.per_gpu_train_batch_size,
            max_steps=args.max_steps,
            num_train_epochs=args.num_train_epochs,
            max_grad_norm=args.max_grad_norm,
            logging_steps=args.logging_steps,
            save_steps=args.save_steps,
        )
        classifier.save_model(args.output_dir, args=args)

    def do_inference(args):
        prepare_output_path(args.output_dir, args.overwrite_output_dir)
        device, n_gpus = setup_backend(args.no_cuda)
        args.batch_size = args.per_gpu_eval_batch_size * max(1, n_gpus)
        inference_examples = process_inference_input(args.data_file)
        classifier = TransformerTokenClassifier.load_model(
            model_path=args.model_path,
            model_type=args.model_type,
            do_lower_case=args.do_lower_case,
            load_quantized=args.load_quantized_model,
        )
        classifier.to(device, n_gpus)
        output = classifier.inference(inference_examples, args.max_seq_length, args.batch_size)
        write_column_tagged_file(args.output_dir + os.sep + "output.txt", output)

    def process_inference_input(input_file):
        with io.open(input_file) as fp:
            texts = [l.strip() for l in fp.readlines()]
        tokenizer = SpacyInstance(disable=["tagger", "parser", "ner"])
        examples = []
        for i, t in enumerate(texts):
            examples.append(TokenClsInputExample(str(i), t, tokenizer.tokenize(t)))
        return examples