# Copyright (c) ModelScope Contributors. All rights reserved.
import os
import torch
import torch.distributed as dist
from dataclasses import dataclass
from typing import Literal, Optional

from swift.utils import HfConfigFactory, get_logger, init_process_group, set_default_ddp_config, to_abspath
from .base_args import BaseArguments
from .merge_args import MergeArguments

logger = get_logger()


@dataclass
class ExportArguments(MergeArguments, BaseArguments):
    """ExportArguments is a dataclass that inherits from BaseArguments and MergeArguments.

    Args:
        output_dir (Optional[str]): Directory to save the exported results. Defaults to None, which automatically sets
            a path with an appropriate suffix.
        quant_method (Optional[str]): The quantization method. Can be 'awq', 'gptq', 'bnb', 'fp8', or 'gptq_v2'.
            Defaults to None. See examples for more details.
        quant_n_samples (int): Number of samples for GPTQ/AWQ calibration. Defaults to 256.
        quant_batch_size (int): The batch size for quantization. Defaults to 1.
        group_size (int): The group size for quantization. Defaults to 128.
        to_cached_dataset (bool): Whether to tokenize and export the dataset in advance as a cached dataset. Defaults
            to False. Note: You can specify the validation set content through
            `--split_dataset_ratio` or `--val_dataset`.
        to_ollama (bool): Whether to generate the `Modelfile` required by Ollama. Defaults to False.
        to_mcore (bool): Whether to convert Hugging Face format weights to Megatron-Core format. Defaults to False.
        to_hf (bool): Whether to convert Megatron-Core format weights to Hugging Face format. Defaults to False.
        mcore_model (Optional[str]): The path to the Megatron-Core format model. Defaults to None.
        mcore_adapter (Optional[str]): A list of adapter paths for the Megatron-Core format model. Defaults to [].
        thread_count (Optional[int]): The number of model shards when `to_mcore` is True. Defaults to None, which
            automatically sets the number based on the model size to keep the largest shard under 10GB.
        test_convert_precision (bool): Whether to test the precision error of weight conversion between Hugging Face
            and Megatron-Core formats. Defaults to False.
        test_convert_dtype (str): The dtype to use for the conversion precision test. Defaults to 'float32'.
        push_to_hub (bool): Whether to push the output to the Model Hub. Defaults to False. See examples for more
            details.
        hub_model_id (Optional[str]): The model ID for pushing to the Hub (e.g., 'user_name/repo_name' or 'repo_name').
            Defaults to None.
        hub_private_repo (bool): Whether the Hub repository is private. Defaults to False.
        commit_message (str): The commit message for pushing to the Hub. Defaults to 'update files'.
        to_peft_format (bool): Whether to export in PEFT format. This argument is for compatibility and currently has
            no effect. Defaults to False.
        exist_ok (bool): If the output_dir exists, do not raise an exception and overwrite its contents. Defaults to
            False.
    """
    output_dir: Optional[str] = None

    # awq/gptq
    quant_method: Literal['awq', 'gptq', 'bnb', 'fp8', 'gptq_v2'] = None
    quant_n_samples: int = 256
    quant_batch_size: int = 1
    group_size: int = 128

    # cached_dataset
    to_cached_dataset: bool = False
    template_mode: Literal['train', 'rlhf', 'kto'] = 'train'

    # ollama
    to_ollama: bool = False

    # megatron
    to_mcore: bool = False
    to_hf: bool = False
    mcore_model: Optional[str] = None
    mcore_adapter: Optional[str] = None
    thread_count: Optional[int] = None
    test_convert_precision: bool = False
    test_convert_dtype: str = 'float32'

    # push to ms hub
    push_to_hub: bool = False
    # 'user_name/repo_name' or 'repo_name'
    hub_model_id: Optional[str] = None
    hub_private_repo: bool = False
    commit_message: str = 'update files'
    # compat
    to_peft_format: bool = False
    exist_ok: bool = False

    def load_args_from_ckpt(self) -> None:
        if self.to_cached_dataset:
            return
        super().load_args_from_ckpt()

    def _init_output_dir(self):
        if self.output_dir is None:
            ckpt_dir = self.ckpt_dir or f'./{self.model_suffix}'
            ckpt_dir, ckpt_name = os.path.split(ckpt_dir)
            if self.to_peft_format:
                suffix = 'peft'
            elif self.quant_method:
                suffix = f'{self.quant_method}'
                if self.quant_bits is not None:
                    suffix += f'-int{self.quant_bits}'
            elif self.to_ollama:
                suffix = 'ollama'
            elif self.merge_lora:
                suffix = 'merged'
            elif self.to_mcore:
                suffix = 'mcore'
            elif self.to_hf:
                suffix = 'hf'
            elif self.to_cached_dataset:
                suffix = 'cached_dataset'
            else:
                return

            self.output_dir = os.path.join(ckpt_dir, f'{ckpt_name}-{suffix}')

        self.output_dir = to_abspath(self.output_dir)
        if not self.exist_ok and os.path.exists(self.output_dir):
            raise FileExistsError(f'args.output_dir: `{self.output_dir}` already exists.')
        logger.info(f'args.output_dir: `{self.output_dir}`')

    def __post_init__(self):
        if self.quant_batch_size == -1:
            self.quant_batch_size = None
        if self.quant_bits and self.quant_method is None:
            raise ValueError('Please specify the quantization method using `--quant_method awq/gptq/bnb`.')
        if self.quant_method and self.quant_bits is None and self.quant_method != 'fp8':
            raise ValueError('Please specify `--quant_bits`.')
        if self.quant_method in {'gptq', 'awq'} and self.torch_dtype is None:
            self.torch_dtype = torch.float16
        if self.to_mcore or self.to_hf:
            if self.merge_lora:
                self.merge_lora = False
                logger.warning('`swift export --to_mcore/to_hf` does not support the `--merge_lora` parameter. '
                               'To export LoRA delta weights, please use `megatron export`')

            self.mcore_model = to_abspath(self.mcore_model, check_path_exist=True)
            if not dist.is_initialized():
                set_default_ddp_config()
                init_process_group(backend=self.ddp_backend, timeout=self.ddp_timeout)

        BaseArguments.__post_init__(self)
        self._init_output_dir()
        self.test_convert_dtype = HfConfigFactory.to_torch_dtype(self.test_convert_dtype)
        if self.quant_method in {'gptq', 'awq'} and len(self.dataset) == 0:
            raise ValueError(f'self.dataset: {self.dataset}, Please input the quant dataset.')
        if self.to_cached_dataset:
            self.lazy_tokenize = False
            if self.packing:
                raise ValueError('Packing will be handled during training; here we only perform tokenization '
                                 'in advance, so you do not need to set up packing separately.')
            assert not self.streaming, 'not supported'
