# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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from __future__ import annotations

from dataclasses import dataclass, field
from typing import Literal, Optional, Union

from peft.config import PeftConfig
from peft.utils import PeftType


@dataclass
class PolyConfig(PeftConfig):
    """
    This is the configuration class to store the configuration of a [`PolyModel`].
        - [Polytropon (Poly)](https://arxiv.org/abs/2202.13914)
        - [Multi-Head Routing (MHR)](https://arxiv.org/abs/2211.03831)

    Args:
        r (`int`): Attention dimension of each Lora in Poly.
        target_modules (`Union[List[str],str]`): The names of the modules to apply Poly to.
        exclude_modules (`Optional[Union[List[str], str]]`):
            The names of the modules to not apply the adapter. When passing a string, a regex match will be performed.
            When passing a list of strings, either an exact match will be performed or it is checked if the name of the
            module ends with any of the passed strings.
        modules_to_save (`List[str]`): List of modules apart from Poly layers to be set as trainable
            and saved in the final checkpoint.
        init_weights (bool): Whether to perform initialization of Poly weights.
        poly_type (`Literal["poly"]`): The variant of the Poly module to use. Currently, only "poly"
            is supported.
        n_tasks (`int`): The number of tasks in a multitasking scenario.
        n_skills (`int`): The number of skills (LoRA) in each Poly layer.
        n_splits (`int`): The number of splits within each LoRA of a Poly layer. A value greater
            than 1 indicates the use of Multi-Head Routing (MHR).
    """

    r: int = field(default=8, metadata={"help": "Lora attention dimension"})
    target_modules: Optional[Union[list[str], str]] = field(
        default=None,
        metadata={
            "help": "List of module names or regex expression of the module names to replace with Poly."
            "For example, ['q', 'v'] or '.*decoder.*(SelfAttention|EncDecAttention).*(q|v)$' "
        },
    )
    exclude_modules: Optional[Union[list[str], str]] = field(
        default=None,
        metadata={"help": "List of module names or regex expression of the module names to exclude from Poly."},
    )
    modules_to_save: Optional[list[str]] = field(
        default=None,
        metadata={
            "help": "List of modules apart from Poly layers to be set as trainable and saved in the final checkpoint. "
            "For example, in Sequence Classification or Token Classification tasks, "
            "the final layer `classifier/score` are randomly initialized and as such need to be trainable and saved."
        },
    )
    init_weights: bool = field(
        default=True,
        metadata={
            "help": (
                "Whether to initialize the weights of the Poly layers with their default initialization. Don't change "
                "this setting, except if you know exactly what you're doing."
            ),
        },
    )
    poly_type: Literal["poly"] = field(
        default="poly",
        metadata={"help": 'Type of Poly modules to be used. Currently only "poly" is supported.'},
    )
    n_tasks: int = field(
        default=1,
        metadata={"help": "Number of tasks in multitasking scenario."},
    )
    n_skills: int = field(
        default=4,
        metadata={"help": "Number of skills (LoRA) in each Poly layer."},
    )
    n_splits: int = field(
        default=1,
        metadata={"help": "Number of splits within each LoRA of a Poly layer."},
    )

    def __post_init__(self):
        super().__post_init__()
        self.peft_type = PeftType.POLY
        self.target_modules = (
            set(self.target_modules) if isinstance(self.target_modules, list) else self.target_modules
        )
        self.exclude_modules = (
            set(self.exclude_modules) if isinstance(self.exclude_modules, list) else self.exclude_modules
        )
