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# 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,
# 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 logging
from dataclasses import dataclass, field
from typing import Optional

from .training_args import TrainingArguments
from .utils import add_start_docstrings

logger = logging.getLogger(__name__)

@dataclass
@add_start_docstrings(TrainingArguments.__doc__)
class Seq2SeqTrainingArguments(TrainingArguments):
    """
    Args:
        sortish_sampler (`bool`, *optional*, defaults to `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 (`bool`, *optional*, defaults to `False`):
            Whether to use generate to calculate generative metrics (ROUGE, BLEU).
        generation_max_length (`int`, *optional*):
            The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default to the
            `max_length` value of the model configuration.
        generation_num_beams (`int`, *optional*):
            The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default to the
            `num_beams` value of the model configuration.
    """

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)."}
    )
    generation_max_length: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
                "to the `max_length` value of the model configuration."
            )
        },
    )
    generation_num_beams: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
                "to the `num_beams` value of the model configuration."
            )
        },
    )

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