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from dataclasses import dataclass, field
from typing import Optional

from transformers import TrainingArguments


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

    This class includes only the parameters that are specific to Online DPO 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:
        reward_model_path (`str` or `None`, *optional*, defaults to `None`):
            Path to the reward model. Either `judge` or `reward_model_path` must be set, but not both.
        judge (`str` or `None`, *optional*, defaults to `None`):
            Name of the judge to use. Either `judge` or `reward_model_path` must be set, but not both.
        max_new_tokens (`int`, *optional*, defaults to `64`):
            Maximum number of tokens to generate per completion.
        max_length (`int`, *optional*, defaults to `256`):
            Maximum total length of the sequence (prompt + completion) used to compute log probabilities. If the
            sequence exceeds this limit, the leftmost tokens will be truncated to preserve as much of the completion as
            possible.
        temperature (`float`, *optional*, defaults to `0.9`):
            Temperature for sampling. The higher the temperature, the more random the completions.
        missing_eos_penalty (`float` or `None`, *optional*, defaults to `None`):
            Penalty applied to the score when the model fails to generate an EOS token. This is useful to encourage to
            generate completions shorter than the maximum length (`max_new_tokens`). The penalty must be a positive
            value.
        beta (`float` or `list[float]`, *optional*, defaults to `0.1`):
            Parameter controlling the deviation from the reference model. Higher β means less deviation from the
            reference model. For the IPO loss (`loss_type="ipo"`), β is the regularization parameter denoted by τ in
            the [paper](https://huggingface.co/papers/2310.12036). If a list of floats is provided then the β is
            selected for each new epoch and the last β is used for the rest of the epochs.
        loss_type (`str`, *optional*, defaults to `"sigmoid"`):
            Type of loss to use. Possible values are:

                - `"sigmoid"`: sigmoid loss from the original [DPO](https://huggingface.co/papers/2305.18290) paper.
                - `"ipo"`: IPO loss from the [IPO](https://huggingface.co/papers/2310.12036) paper.

        dataset_num_proc (`int` or `None`, *optional*, defaults to `None`):
            Number of processes to use for processing the dataset.
        disable_dropout (`bool`, *optional*, defaults to `True`):
            Whether to disable dropout in the model and reference model.
        use_vllm (`bool`, *optional*, defaults to `False`):
            Whether to use vLLM for generating completions. Requires vLLM to be installed (`pip install vllm`).
        gpu_memory_utilization (`float`, *optional*, defaults to `0.55`):
            The vLLM memory utilization. The default value is 0.55.
        ds3_gather_for_generation (`bool`, *optional*, defaults to `True`):
            This setting applies to DeepSpeed ZeRO-3. If enabled, the policy model weights are gathered for generation,
            improving generation speed. However, disabling this option allows training models that exceed the VRAM
            capacity of a single GPU, albeit at the cost of slower generation.
    """

    # Parameters whose default values are overridden from TrainingArguments
    learning_rate: float = field(
        default=5e-7,
        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."
        },
    )
    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."
        },
    )

    reward_model_path: Optional[str] = field(
        default=None,
        metadata={
            "help": "Path to the reward model. Either `judge` or `reward_model_path` must be set, but not both."
        },
    )
    judge: Optional[str] = field(
        default=None,
        metadata={
            "help": "Name of the judge to use. Either `judge` or `reward_model_path` must be set, but not both."
        },
    )
    max_new_tokens: int = field(
        default=64,
        metadata={"help": "Maximum number of tokens to generate per completion."},
    )
    max_length: int = field(
        default=512,
        metadata={
            "help": "Maximum total length of the sequence (prompt + completion) used to compute log probabilities. If "
            "the sequence exceeds this limit, the leftmost tokens will be truncated to preserve as much of the "
            "completion as possible."
        },
    )
    temperature: float = field(
        default=0.9,
        metadata={"help": "Temperature for sampling. The higher the temperature, the more random the completions."},
    )
    missing_eos_penalty: Optional[float] = field(
        default=None,
        metadata={
            "help": "Penalty applied to the score when the model fails to generate an EOS token. This is useful to "
            "encourage to generate completions shorter than the maximum length (`max_new_tokens`). The penalty must be "
            "a positive value."
        },
    )
    beta: list[float] = field(
        default_factory=lambda: [0.1],
        metadata={
            "help": "Parameter controlling the deviation from the reference model. Higher β means less deviation from "
            "the reference model. For the IPO loss (`loss_type='ipo'`), β is the regularization parameter denoted by "
            "τ in the [paper](https://huggingface.co/papers/2310.12036). If a list of floats is provided then the β "
            "is selected for each new epoch and the last β is used for the rest of the epochs."
        },
    )
    loss_type: str = field(
        default="sigmoid",
        metadata={
            "help": "Type of loss to use.",
            "choices": ["sigmoid", "ipo"],
        },
    )
    dataset_num_proc: Optional[int] = field(
        default=None,
        metadata={"help": "Number of processes to use for processing the dataset."},
    )
    disable_dropout: bool = field(
        default=True,
        metadata={"help": "Whether to disable dropout in the model."},
    )
    use_vllm: bool = field(
        default=False,
        metadata={
            "help": "Whether to use vLLM for generating completions. Requires vLLM to be installed "
            "(`pip install vllm`)."
        },
    )
    gpu_memory_utilization: Optional[float] = field(
        default=0.55,
        metadata={
            "help": "The vLLM memory utilization. The default value is 0.55.",
        },
    )
    ds3_gather_for_generation: bool = field(
        default=True,
        metadata={
            "help": "This setting applies to DeepSpeed ZeRO-3. If enabled, the policy model weights are gathered for "
            "generation, improving generation speed. However, disabling this option allows training models that "
            "exceed the VRAM capacity of a single GPU, albeit at the cost of slower generation."
        },
    )

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

        super().__post_init__()

        if hasattr(self.beta, "__len__") and len(self.beta) == 1:
            self.beta = self.beta[0]