# Copyright 2020 The HuggingFace Team. 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,
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import logging
from dataclasses import dataclass, field

from .file_utils import add_start_docstrings
from .training_args import TrainingArguments


logger = logging.getLogger(__name__)


@dataclass
@add_start_docstrings(TrainingArguments.__doc__)
class Seq2SeqTrainingArguments(TrainingArguments):
    """
    sortish_sampler (:obj:`bool`, `optional`, defaults to :obj:`False`):
        Whether to use a `sortish sampler` or not. Only possible if the underlying datasets are `Seq2SeqDataset` for
        now but will become generally available in the near future.

        It sorts the inputs according to lengths in order to minimize the padding size, with a bit of randomness for
        the training set.
    predict_with_generate (:obj:`bool`, `optional`, defaults to :obj:`False`):
        Whether to use generate to calculate generative metrics (ROUGE, BLEU).
    """

    sortish_sampler: bool = field(default=False, metadata={"help": "Whether to use SortishSampler or not."})
    predict_with_generate: bool = field(
        default=False, metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}
    )