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from dataclasses import dataclass, field
from typing import Optional

from transformers import TrainingArguments


@dataclass
class PRMConfig(TrainingArguments):
    r"""
    Configuration class for the [`PRMTrainer`].

    This class includes only the parameters that are specific to PRM training. For a full list of training arguments,
    please refer to the [`~transformers.TrainingArguments`] documentation. Note that default values in this class may
    differ from those in [`~transformers.TrainingArguments`].

    Using [`~transformers.HfArgumentParser`] we can turn this class into
    [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the
    command line.

    Parameters:
        max_length (`int` or `None`, *optional*, defaults to `1024`):
            Maximum length of the sequences (prompt + completion) used for truncation.
        max_prompt_length (`int` or `None`, *optional*, defaults to `512`):
            Maximum length of the prompt used for truncation.
        max_completion_length (`int`, *optional*):
            Maximum length of the completion used for truncation. The completion is the concatenation of the steps.
        disable_dropout (`bool`, *optional*, defaults to `True`):
            Whether to disable dropout in the model.
        step_separator (`str`, *optional*, defaults to `"\n"`):
            Separator used to separate each step of the reasoning process.
        train_on_last_step_only (`bool`, *optional*, defaults to `False`):
            Whether to train only on the last step.
        dataset_num_proc (`int`, *optional*):
            Number of processes to use for processing the dataset.
    """

    # Parameters whose default values are overridden from TrainingArguments
    learning_rate: float = field(
        default=1e-5,
        metadata={"help": "The initial learning rate for AdamW."},
    )
    logging_steps: float = field(
        default=10,
        metadata={
            "help": "Log every X updates steps. Should be an integer or a float in range `[0,1)`. If smaller than 1, "
            "will be interpreted as ratio of total training steps."
        },
    )
    gradient_checkpointing: bool = field(
        default=True,
        metadata={
            "help": "If True, use gradient checkpointing to save memory at the expense of slower backward pass."
        },
    )
    bf16: Optional[bool] = field(
        default=None,
        metadata={
            "help": "Whether to use bf16 (mixed) precision instead of 32-bit. Requires Ampere or higher NVIDIA "
            "architecture or Intel XPU or using CPU (use_cpu) or Ascend NPU. If not set, it defaults to `True` if "
            "`fp16` is not set."
        },
    )

    max_length: Optional[int] = field(
        default=1024,
        metadata={"help": "Maximum length of the sequences (prompt + completion) used for truncation."},
    )
    max_prompt_length: Optional[int] = field(
        default=512,
        metadata={"help": "Maximum length of the prompt used for truncation."},
    )
    max_completion_length: Optional[int] = field(
        default=None,
        metadata={
            "help": "Maximum length of the completion used for truncation. The completion is the concatenation of the "
            "steps."
        },
    )
    disable_dropout: bool = field(
        default=True,
        metadata={"help": "Whether to disable dropout in the model and reference model."},
    )
    step_separator: str = field(
        default="\n",
        metadata={"help": "Separator used to separate each step of the reasoning process."},
    )
    train_on_last_step_only: bool = field(
        default=False,
        metadata={"help": "Whether to train only on the last step."},
    )
    dataset_num_proc: Optional[int] = field(
        default=None,
        metadata={"help": "Number of processes to use for processing the dataset."},
    )

    def __post_init__(self):
        self.bf16 = not (self.fp16) if self.bf16 is None else self.bf16

        super().__post_init__()
