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from dataclasses import dataclass
from typing import Any, Dict, Literal, Optional

from transformers import TrainingArguments


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

    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:
        learning_rate (`float`, *optional*, defaults to `5e-7`):
            Initial learning rate for [`AdamW`] optimizer. The default value replaces that of
            [`~transformers.TrainingArguments`].
        max_length (`Optional[int]`, *optional*, defaults to `None`):
            Maximum length of the sequences (prompt + completion) in the batch. This argument is required if you want
            to use the default data collator.
        max_prompt_length (`Optional[int]`, *optional*, defaults to `None`):
            Maximum length of the prompt. This argument is required if you want to use the default data collator.
        max_completion_length (`Optional[int]`, *optional*, defaults to `None`):
            Maximum length of the completion. This argument is required if you want to use the default data collator
            and your model is an encoder-decoder.
        beta (`float`, *optional*, defaults to `0.1`):
            Parameter controlling the deviation from the reference model. Higher β means less deviation from the
            reference model.
        loss_type (`str`, *optional*, defaults to `"kto"`):
            Type of loss to use. Possible values are:

                - `"kto"`: KTO loss from the [KTO](https://huggingface.co/papers/2402.01306) paper.
                - `"apo_zero_unpaired"`: Unpaired variant of APO-zero loss from the [APO](https://huggingface.co/papers/2408.06266) paper.

        desirable_weight (`float`, *optional*, defaults to `1.0`):
            Desirable losses are weighed by this factor to counter unequal number of desirable and undesirable paris.
        undesirable_weight (`float`, *optional*, defaults to `1.0`):
            Undesirable losses are weighed by this factor to counter unequal number of desirable and undesirable pairs.
        label_pad_token_id (`int`, *optional*, defaults to `-100`):
            Label pad token id. This argument is required if you want to use the default data collator.
        padding_value (`Optional[int]`, *optional*, defaults to `None`):
            Padding value to use. If `None`, the padding value of the tokenizer is used.
        truncation_mode (`str`, *optional*, defaults to `"keep_end"`):
            Truncation mode to use when the prompt is too long. Possible values are `"keep_end"` or `"keep_start"`.
            This argument is required if you want to use the default data collator.
        generate_during_eval (`bool`, *optional*, defaults to `False`):
            If `True`, generates and logs completions from both the model and the reference model to W&B during
            evaluation.
        is_encoder_decoder (`Optional[bool]`, *optional*, defaults to `None`):
            When using the `model_init` argument (callable) to instantiate the model instead of the `model` argument,
            you need to specify if the model returned by the callable is an encoder-decoder model.
        precompute_ref_log_probs (`bool`, *optional*, defaults to `False`):
            Whether to precompute reference model log probabilities for training and evaluation datasets. This is
            useful when training without the reference model to reduce the total GPU memory needed.
        model_init_kwargs (`Optional[Dict[str, Any]]`, *optional*, defaults to `None`):
            Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the model from a
            string.
        ref_model_init_kwargs (`Optional[Dict[str, Any]]`, *optional*, defaults to `None`):
            Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the reference model
            from a string.
        dataset_num_proc: (`Optional[int]`, *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.
    """

    learning_rate: float = 1e-6
    max_length: Optional[int] = None
    max_prompt_length: Optional[int] = None
    max_completion_length: Optional[int] = None
    beta: float = 0.1
    loss_type: Literal["kto", "apo_zero_unpaired"] = "kto"
    desirable_weight: float = 1.0
    undesirable_weight: float = 1.0
    label_pad_token_id: int = -100
    padding_value: Optional[int] = None
    truncation_mode: str = "keep_end"
    generate_during_eval: bool = False
    is_encoder_decoder: Optional[bool] = None
    disable_dropout: bool = True
    precompute_ref_log_probs: bool = False
    model_init_kwargs: Optional[Dict[str, Any]] = None
    ref_model_init_kwargs: Optional[Dict[str, Any]] = None
    dataset_num_proc: Optional[int] = None