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from dataclasses import dataclass, field
from typing import Any, Optional

from transformers import TrainingArguments


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

    This class includes only the parameters that are specific to SFT 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:
        > Parameters that control the model

        model_init_kwargs (`dict[str, Any]`, *optional*):
            Keyword arguments for [`~transformers.AutoModelForCausalLM.from_pretrained`], used when the `model`
            argument of the [`SFTTrainer`] is provided as a string. If you're training a MoE architecture and want to
            include the load balancing/auxilliary loss as a part of the final loss, remember to set
            `output_router_logits=True` in this dictionary.
        chat_template_path (`str`, *optional*):
            If specified, sets the model's chat template. This can either be the path to a tokenizer (local directory
            or Hugging Face Hub model) or a direct path to a Jinja template file. When using a Jinja file, you must
            ensure that any special tokens referenced in the template are added to the tokenizer and that the model's
            embedding layer is resized accordingly.

        > Parameters that control the data preprocessing

        dataset_text_field (`str`, *optional*, defaults to `"text"`):
            Name of the column that contains text data in the dataset.
        dataset_kwargs (`dict[str, Any]`, *optional*):
            Dictionary of optional keyword arguments for the dataset preparation. The only supported key is
            `skip_prepare_dataset`. When the model is a VLM, `skip_prepare_dataset` is automatically treated as `True`
            regardless of the provided value, since preprocessing is done on the fly.
        dataset_num_proc (`int`, *optional*):
            Number of processes to use for processing the dataset.
        eos_token (`str`, *optional*):
            Token used to indicate the end of a turn or sequence. If `None`, it defaults to
            `processing_class.eos_token`.
        pad_token (`str`, *optional*):
            Token used for padding. If `None`, it defaults to `processing_class.pad_token`, or if that is also `None`,
            it falls back to `processing_class.eos_token`.
        max_length (`int` or `None`, *optional*, defaults to `1024`):
            Maximum length of the tokenized sequence. Sequences longer than `max_length` are truncated from the right.
            If `None`, no truncation is applied. When packing is enabled, this value sets the sequence length.
        packing (`bool`, *optional*, defaults to `False`):
            Whether to group multiple sequences into fixed-length blocks to improve computational efficiency and reduce
            padding. Uses `max_length` to define sequence length.
        packing_strategy (`str`, *optional*, defaults to `"bfd"`):
            Strategy for packing sequences. Can be either `"bfd"` (best-fit decreasing, default), or `"wrapped"`.
        padding_free (`bool`, *optional*, defaults to `False`):
            Whether to perform forward passes without padding by flattening all sequences in the batch into a single
            continuous sequence. This reduces memory usage by eliminating padding overhead. Currently, this is only
            supported with the FlashAttention 2 or 3, which can efficiently handle the flattened batch structure. When
            packing is enabled with strategy `"bfd"`, padding-free is enabled, regardless of the value of this
            parameter.
        pad_to_multiple_of (`int`, *optional*):
            If set, the sequences will be padded to a multiple of this value.
        eval_packing (`bool`, *optional*):
            Whether to pack the eval dataset. If `None`, uses the same value as `packing`.

        > Parameters that control the training

        completion_only_loss (`bool`, *optional*):
            Whether to compute loss only on the completion part of the sequence. If set to `True`, loss is computed
            only on the completion, which is supported only for [prompt-completion](#prompt-completion) datasets. If
            `False`, loss is computed on the entire sequence. If `None` (default), the behavior depends on the dataset:
            loss is computed on the completion for [prompt-completion](#prompt-completion) datasets, and on the full
            sequence for [language modeling](#language-modeling) datasets.
        assistant_only_loss (`bool`, *optional*, defaults to `False`):
            Whether to compute loss only on the assistant part of the sequence. If set to `True`, loss is computed only
            on the assistant responses, which is supported only for [conversational](#conversational) datasets. If
            `False`, loss is computed on the entire sequence.
        loss_type (`str`, *optional*, defaults to `"nll"`):
            Type of loss to use. Possible values are `"nll"` (negative log-likelihood, default) and `"dft"` (Dynamic
            Fine-Tuning, as described in [this paper](https://huggingface.co/papers/2508.05629)).
        activation_offloading (`bool`, *optional*, defaults to `False`):
            Whether to offload the activations to the CPU.
    """

    _VALID_DICT_FIELDS = TrainingArguments._VALID_DICT_FIELDS + ["model_init_kwargs"]

    # Parameters whose default values are overridden from TrainingArguments
    learning_rate: float = field(
        default=2e-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."
        },
    )

    # Parameters that control the model
    model_init_kwargs: Optional[dict[str, Any]] = field(
        default=None,
        metadata={
            "help": "Keyword arguments for `AutoModelForCausalLM.from_pretrained`, used when the `model` argument of "
            "the `SFTTrainer` is provided as a string. If you're training a MoE architecture and want to include the "
            "load balancing/auxilliary loss as a part of the final loss, remember to set `output_router_logits=True` "
            "in this dictionary."
        },
    )
    chat_template_path: Optional[str] = field(
        default=None,
        metadata={
            "help": "If specified, sets the model's chat template. This can either be the path to a tokenizer (local "
            "directory or Hugging Face Hub model) or a direct path to a Jinja template file. When using a Jinja file, "
            "you must ensure that any special tokens referenced in the template are added to the tokenizer and "
            "that the model's embedding layer is resized accordingly."
        },
    )

    # Parameters that control the data preprocessing
    dataset_text_field: str = field(
        default="text",
        metadata={"help": "Name of the column that contains text data in the dataset."},
    )
    dataset_kwargs: Optional[dict[str, Any]] = field(
        default=None,
        metadata={
            "help": "Dictionary of optional keyword arguments for the dataset preparation. The only supported key is "
            "`skip_prepare_dataset`. If the model is a VLM, `skip_prepare_dataset` value is ignored. When the model "
            "is a VLM, `skip_prepare_dataset` is automatically treated as `True` regardless of the provided value, "
            "since preprocessing is done on the fly."
        },
    )
    dataset_num_proc: Optional[int] = field(
        default=None,
        metadata={"help": "Number of processes to use for processing the dataset."},
    )
    eos_token: Optional[str] = field(
        default=None,
        metadata={
            "help": "Token used to indicate the end of a turn or sequence. If `None`, it defaults to `processing_class.eos_token`."
        },
    )
    pad_token: Optional[str] = field(
        default=None,
        metadata={
            "help": "Token used for padding. If `None`, it defaults to `processing_class.pad_token`, or if that "
            "is also `None`, it falls back to `processing_class.eos_token`."
        },
    )
    max_length: Optional[int] = field(
        default=1024,
        metadata={
            "help": "Maximum length of the tokenized sequence. Sequences longer than `max_length` are truncated from"
            "the right. If `None`, no truncation is applied. When packing is enabled, this value sets the "
            "sequence length."
        },
    )
    packing: bool = field(
        default=False,
        metadata={
            "help": "Whether to group multiple sequences into fixed-length blocks to improve computational efficiency "
            "and reduce padding. Uses `max_length` to define sequence length."
        },
    )
    packing_strategy: str = field(
        default="bfd",
        metadata={
            "help": "Strategy for packing sequences. Can be either `'bfd'` (best-fit decreasing, default), or "
            "`'wrapped'`."
        },
    )
    padding_free: bool = field(
        default=False,
        metadata={
            "help": "Whether to perform forward passes without padding by flattening all sequences in the batch into "
            "a single continuous sequence. This reduces memory usage by eliminating padding overhead. Currently, this "
            "is only supported with the FlashAttention 2 or 3, which can efficiently handle the flattened batch "
            "structure. When packing is enabled with strategy `'bfd'`, padding-free is enabled, regardless of the "
            "value of this parameter."
        },
    )
    pad_to_multiple_of: Optional[int] = field(
        default=None,
        metadata={"help": "If set, the sequences will be padded to a multiple of this value."},
    )
    eval_packing: Optional[bool] = field(
        default=None,
        metadata={"help": "Whether to pack the eval dataset. If `None`, uses the same value as `packing`."},
    )

    # Parameters that control the training
    completion_only_loss: Optional[bool] = field(
        default=None,
        metadata={
            "help": (
                "Whether to compute loss only on the completion part of the sequence. If set to `True`, loss is "
                "computed only on the completion, which is supported only for prompt-completion datasets. If `False`, "
                "loss is computed on the entire sequence. If `None` (default), the behavior depends on the dataset: "
                "loss is computed on the completion for prompt-completion datasets, and on the full sequence for "
                "language modeling datasets."
            )
        },
    )
    assistant_only_loss: bool = field(
        default=False,
        metadata={
            "help": (
                "Whether to compute loss only on the assistant part of the sequence. If set to `True`, loss is "
                "computed only on the assistant responses, which is supported only for conversational datasets. If `False`, "
                "loss is computed on the entire sequence."
            )
        },
    )
    loss_type: str = field(
        default="nll",
        metadata={
            "help": (
                'Type of loss to use. Possible values are `"nll"` (negative log-likelihood, default) and `"dft"` '
                "(Dynamic Fine-Tuning, as described in https://huggingface.co/papers/2508.05629)."
            )
        },
    )
    activation_offloading: bool = field(
        default=False,
        metadata={"help": "Whether to offload the activations to the CPU."},
    )

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