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# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
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from dataclasses import dataclass
from typing import Any, Dict, Optional

from transformers import TrainingArguments


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

    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 (`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.
        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.
        prompt_sample_size (`int`, *optional*, defaults to `1024`):
            Number of prompts that are fed to density ratio classifier.
        min_density_ratio (`float`, *optional*, defaults to `0.5`):
            Minimum value of the density ratio. The estimated density ratio is clamped to this value.
        max_density_ratio (`float`, *optional*, defaults to `10.0`):
            Maximum value of the density ratio. The estimated density ratio is clamped to this value.
    """

    max_length: Optional[int] = None
    max_prompt_length: Optional[int] = None
    max_completion_length: Optional[int] = None
    beta: float = 0.1
    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
    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
    prompt_sample_size: int = 1024
    min_density_ratio: float = 0.5
    max_density_ratio: float = 10.0