# Source code for nlp_architect.procedures.transformers.base

    # ******************************************************************************
    # 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

    from nlp_architect.models.transformers.base_model import get_models

    def create_base_args(parser: argparse.ArgumentParser, model_types=None):
        """Add base arguments for Transformers based models
        """
        # Required parameters
        if model_types is not None and len(model_types) > 1:
            parser.add_argument(
                "--model_type",
                default=None,
                type=str,
                choices=model_types,
                required=True,
                help="Model type selected in the list: " + ", ".join(model_types),
            )
        parser.add_argument(
            "--output_dir",
            default=None,
            type=str,
            required=True,
            help="The output directory where the model predictions and checkpoints " "will be written.",
        )

        # Other parameters
        parser.add_argument(
            "--tokenizer_name",
            default="",
            type=str,
            help="Pretrained tokenizer name or path if not the same as model_name",
        )
        parser.add_argument(
            "--max_seq_length",
            default=128,
            type=int,
            help="The maximum total input sequence length after tokenization. "
            "Sequences longer than this will be truncated, sequences shorter "
            "will be padded.",
        )
        parser.add_argument(
            "--cache_dir",
            default="",
            type=str,
            help="Where do you want to store the pre-trained models downloaded " "from s3",
        )
        parser.add_argument(
            "--do_lower_case",
            action="store_true",
            help="Set this flag if you are using an uncased model.",
        )
        parser.add_argument(
            "--per_gpu_eval_batch_size",
            default=8,
            type=int,
            help="Batch size per GPU/CPU for evaluation.",
        )
        parser.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available")
        parser.add_argument(
            "--overwrite_output_dir",
            action="store_true",
            help="Overwrite the content of the output directory",
        )
        parser.add_argument(
            "--overwrite_cache",
            action="store_true",
            help="Overwrite the cached training and evaluation sets",
        )

    def inference_args(parser: argparse.ArgumentParser):
        """ Add inference specific arguments for Transoformer based models
        """
        parser.add_argument(
            "--model_path", default=None, type=str, required=True, help="Path to pre-trained model"
        )
        parser.add_argument(
            "--load_quantized_model",
            action="store_true",
            help="Load and perform Inference from saved quantized model,\
                            'quant_pytorch_model.bin' file must exist in directory and model\
                                 type must be 'quant_<model>'",
        )

    def train_args(parser: argparse.ArgumentParser, models_family=None):
        """ Add training specific arguments for Transformer based models
        """
        parser.add_argument(
            "--model_name_or_path",
            default=None,
            type=str,
            required=True,
            help="Path to pre-trained model or shortcut name selected in the list: "
            + ", ".join(get_models(models_family)),
        )
        parser.add_argument(
            "--config_name",
            default="",
            type=str,
            help="Pretrained config name or path if not the same as model_name",
        )
        parser.add_argument(
            "--evaluate_during_training",
            action="store_true",
            help="Run evaluation during training at each logging step.",
        )
        parser.add_argument(
            "--per_gpu_train_batch_size",
            default=8,
            type=int,
            help="Batch size per GPU/CPU for training.",
        )
        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(
            "--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam."
        )
        parser.add_argument(
            "--weight_decay", default=0.0, type=float, help="Weight deay if we apply some."
        )
        parser.add_argument(
            "--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer."
        )
        parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
        parser.add_argument(
            "--num_train_epochs",
            default=3,
            type=int,
            help="Total number of training epochs to perform.",
        )
        parser.add_argument(
            "--max_steps",
            default=-1,
            type=int,
            help="If > 0: set total number of training steps to perform. " "Override num_train_epochs.",
        )
        parser.add_argument(
            "--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps."
        )
        parser.add_argument("--logging_steps", type=int, default=50, help="Log every X updates steps.")
        parser.add_argument(
            "--save_steps", type=int, default=500, help="Save checkpoint every X updates steps."
        )
        parser.add_argument(
            "--eval_all_checkpoints",
            action="store_true",
            help="Evaluate all checkpoints starting with the same prefix as "
            + "model_name ending and ending with step number",
        )
        parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")