# Copyright 2025 HuggingFace Inc., the KVCache.AI team, Approaching AI, and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's transformers library.
# https://github.com/huggingface/transformers/blob/v4.40.0/examples/pytorch/language-modeling/run_clm.py
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import json
import os
from dataclasses import asdict, dataclass, field, fields
from typing import Any, Literal, Self

import torch
from omegaconf import OmegaConf
from transformers.training_args import _convert_str_dict

from ..extras.constants import AttentionFunction, EngineName, QuantizationMethod, RopeScaling
from ..extras.logging import get_logger


logger = get_logger(__name__)


@dataclass
class BaseModelArguments:
    r"""Arguments pertaining to the model."""

    model_name_or_path: str | None = field(
        default=None,
        metadata={
            "help": "Path to the model weight or identifier from huggingface.co/models or modelscope.cn/models."
        },
    )
    adapter_name_or_path: str | None = field(
        default=None,
        metadata={
            "help": (
                "Path to the adapter weight or identifier from huggingface.co/models. "
                "Use commas to separate multiple adapters."
            )
        },
    )
    adapter_folder: str | None = field(
        default=None,
        metadata={"help": "The folder containing the adapter weights to load."},
    )
    cache_dir: str | None = field(
        default=None,
        metadata={"help": "Where to store the pre-trained models downloaded from huggingface.co or modelscope.cn."},
    )
    use_fast_tokenizer: bool = field(
        default=True,
        metadata={"help": "Whether or not to use one of the fast tokenizer (backed by the tokenizers library)."},
    )
    resize_vocab: bool = field(
        default=False,
        metadata={"help": "Whether or not to resize the tokenizer vocab and the embedding layers."},
    )
    split_special_tokens: bool = field(
        default=False,
        metadata={"help": "Whether or not the special tokens should be split during the tokenization process."},
    )
    add_tokens: str | None = field(
        default=None,
        metadata={
            "help": "Non-special tokens to be added into the tokenizer. Use commas to separate multiple tokens."
        },
    )
    add_special_tokens: str | None = field(
        default=None,
        metadata={"help": "Special tokens to be added into the tokenizer. Use commas to separate multiple tokens."},
    )
    new_special_tokens_config: str | None = field(
        default=None,
        metadata={
            "help": (
                "Path to YAML config with special token descriptions for semantic initialization. "
                "If set, this takes precedence over add_special_tokens. "
                "YAML format: {'<token>': 'description text', ...}"
            )
        },
    )
    init_special_tokens: Literal["noise_init", "desc_init", "desc_init_w_noise"] = field(
        default="noise_init",
        metadata={
            "help": (
                "Initialization method for new special tokens: "
                "'noise_init' (default, random noise around mean), "
                "'desc_init' (semantic initialization from descriptions), "
                "'desc_init_w_noise' (semantic + random noise). "
                "Note: 'desc_init' methods require new_special_tokens_config."
            )
        },
    )
    model_revision: str = field(
        default="main",
        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
    )
    low_cpu_mem_usage: bool = field(
        default=True,
        metadata={"help": "Whether or not to use memory-efficient model loading."},
    )
    rope_scaling: RopeScaling | None = field(
        default=None,
        metadata={"help": "Which scaling strategy should be adopted for the RoPE embeddings."},
    )
    flash_attn: AttentionFunction = field(
        default=AttentionFunction.AUTO,
        metadata={"help": "Enable FlashAttention for faster training and inference."},
    )
    shift_attn: bool = field(
        default=False,
        metadata={"help": "Enable shift short attention (S^2-Attn) proposed by LongLoRA."},
    )
    mixture_of_depths: Literal["convert", "load"] | None = field(
        default=None,
        metadata={"help": "Convert the model to mixture-of-depths (MoD) or load the MoD model."},
    )
    use_unsloth: bool = field(
        default=False,
        metadata={"help": "Whether or not to use unsloth's optimization for the LoRA training."},
    )
    use_unsloth_gc: bool = field(
        default=False,
        metadata={"help": "Whether or not to use unsloth's gradient checkpointing (no need to install unsloth)."},
    )
    enable_liger_kernel: bool = field(
        default=False,
        metadata={"help": "Whether or not to enable liger kernel for faster training."},
    )
    moe_aux_loss_coef: float | None = field(
        default=None,
        metadata={"help": "Coefficient of the auxiliary router loss in mixture-of-experts model."},
    )
    disable_gradient_checkpointing: bool = field(
        default=False,
        metadata={"help": "Whether or not to disable gradient checkpointing."},
    )
    use_reentrant_gc: bool = field(
        default=True,
        metadata={"help": "Whether or not to use reentrant gradient checkpointing."},
    )
    upcast_layernorm: bool = field(
        default=False,
        metadata={"help": "Whether or not to upcast the layernorm weights in fp32."},
    )
    upcast_lmhead_output: bool = field(
        default=False,
        metadata={"help": "Whether or not to upcast the output of lm_head in fp32."},
    )
    train_from_scratch: bool = field(
        default=False,
        metadata={"help": "Whether or not to randomly initialize the model weights."},
    )
    infer_backend: EngineName = field(
        default=EngineName.HF,
        metadata={"help": "Backend engine used at inference."},
    )
    offload_folder: str = field(
        default="offload",
        metadata={"help": "Path to offload model weights."},
    )
    use_kv_cache: bool = field(
        default=True,
        metadata={"help": "Whether or not to use KV cache in generation."},
    )
    use_v1_kernels: bool | None = field(
        default=False,
        metadata={"help": "Whether or not to use high-performance kernels in training."},
    )
    infer_dtype: Literal["auto", "float16", "bfloat16", "float32"] = field(
        default="auto",
        metadata={"help": "Data type for model weights and activations at inference."},
    )
    hf_hub_token: str | None = field(
        default=None,
        metadata={"help": "Auth token to log in with Hugging Face Hub."},
    )
    ms_hub_token: str | None = field(
        default=None,
        metadata={"help": "Auth token to log in with ModelScope Hub."},
    )
    om_hub_token: str | None = field(
        default=None,
        metadata={"help": "Auth token to log in with Modelers Hub."},
    )
    print_param_status: bool = field(
        default=False,
        metadata={"help": "For debugging purposes, print the status of the parameters in the model."},
    )
    trust_remote_code: bool = field(
        default=False,
        metadata={"help": "Whether to trust the execution of code from datasets/models defined on the Hub or not."},
    )

    def __post_init__(self):
        if self.model_name_or_path is None:
            raise ValueError("Please provide `model_name_or_path`.")

        if self.adapter_name_or_path is not None:  # support merging multiple lora weights
            self.adapter_name_or_path = [path.strip() for path in self.adapter_name_or_path.split(",")]

        if self.add_tokens is not None:  # support multiple tokens
            self.add_tokens = [token.strip() for token in self.add_tokens.split(",")]

        # Process special tokens with priority: new_special_tokens_config > add_special_tokens
        if self.new_special_tokens_config is not None:
            # Priority 1: Load from YAML config (extracts both tokens and descriptions)
            try:
                cfg = OmegaConf.load(self.new_special_tokens_config)
                token_descriptions = OmegaConf.to_container(cfg)

                if not isinstance(token_descriptions, dict):
                    raise ValueError(
                        f"YAML config must be a dictionary mapping tokens to descriptions. "
                        f"Got: {type(token_descriptions)}"
                    )

                # Extract token list from config keys
                extracted_tokens = list(token_descriptions.keys())

                # Warn if both are set
                if self.add_special_tokens is not None:
                    logger.warning_rank0(
                        "Both 'new_special_tokens_config' and 'add_special_tokens' are set. "
                        f"Using tokens from config: {extracted_tokens}"
                    )

                # Override add_special_tokens with extracted tokens (as list)
                self.add_special_tokens = extracted_tokens

                # Store descriptions internally for later use (internal attribute)
                self._special_token_descriptions = token_descriptions

                logger.info_rank0(
                    f"Loaded {len(extracted_tokens)} special tokens with descriptions from: "
                    f"{self.new_special_tokens_config}"
                )

            except Exception as e:
                logger.error_rank0(
                    f"Failed to load special tokens config from '{self.new_special_tokens_config}': {e}"
                )
                raise

        elif self.add_special_tokens is not None:
            # Priority 2: Use simple comma-separated string (no descriptions)
            self.add_special_tokens = [token.strip() for token in self.add_special_tokens.split(",")]
            self._special_token_descriptions = None

        else:
            # No special tokens to add
            self._special_token_descriptions = None

        # Validate init method
        if self.init_special_tokens in ["desc_init", "desc_init_w_noise"]:
            if self._special_token_descriptions is None:
                logger.warning_rank0(
                    f"init_special_tokens='{self.init_special_tokens}' requires new_special_tokens_config. "
                    "Falling back to 'noise_init'"
                )
                self.init_special_tokens = "noise_init"


@dataclass
class QuantizationArguments:
    r"""Arguments pertaining to the quantization method."""

    quantization_method: QuantizationMethod = field(
        default=QuantizationMethod.BNB,
        metadata={"help": "Quantization method to use for on-the-fly quantization."},
    )
    quantization_bit: int | None = field(
        default=None,
        metadata={"help": "The number of bits to quantize the model using on-the-fly quantization."},
    )
    quantization_type: Literal["fp4", "nf4"] = field(
        default="nf4",
        metadata={"help": "Quantization data type to use in bitsandbytes int4 training."},
    )
    double_quantization: bool = field(
        default=True,
        metadata={"help": "Whether or not to use double quantization in bitsandbytes int4 training."},
    )
    quantization_device_map: Literal["auto"] | None = field(
        default=None,
        metadata={"help": "Device map used to infer the 4-bit quantized model, needs bitsandbytes>=0.43.0."},
    )


@dataclass
class ProcessorArguments:
    r"""Arguments pertaining to the image processor."""

    image_max_pixels: int = field(
        default=768 * 768,
        metadata={"help": "The maximum number of pixels of image inputs."},
    )
    image_min_pixels: int = field(
        default=32 * 32,
        metadata={"help": "The minimum number of pixels of image inputs."},
    )
    image_do_pan_and_scan: bool = field(
        default=False,
        metadata={"help": "Use pan and scan to process image for gemma3."},
    )
    crop_to_patches: bool = field(
        default=False,
        metadata={"help": "Whether to crop the image to patches for internvl."},
    )
    video_max_pixels: int = field(
        default=256 * 256,
        metadata={"help": "The maximum number of pixels of video inputs."},
    )
    video_min_pixels: int = field(
        default=16 * 16,
        metadata={"help": "The minimum number of pixels of video inputs."},
    )
    video_fps: float = field(
        default=2.0,
        metadata={"help": "The frames to sample per second for video inputs."},
    )
    video_maxlen: int = field(
        default=128,
        metadata={"help": "The maximum number of sampled frames for video inputs."},
    )
    use_audio_in_video: bool = field(
        default=False,
        metadata={"help": "Whether or not to use audio in video inputs."},
    )
    audio_sampling_rate: int = field(
        default=16000,
        metadata={"help": "The sampling rate of audio inputs."},
    )

    def __post_init__(self):
        if self.image_max_pixels < self.image_min_pixels:
            raise ValueError("`image_max_pixels` cannot be smaller than `image_min_pixels`.")

        if self.video_max_pixels < self.video_min_pixels:
            raise ValueError("`video_max_pixels` cannot be smaller than `video_min_pixels`.")


@dataclass
class ExportArguments:
    r"""Arguments pertaining to the model export."""

    export_dir: str | None = field(
        default=None,
        metadata={"help": "Path to the directory to save the exported model."},
    )
    export_size: int = field(
        default=5,
        metadata={"help": "The file shard size (in GB) of the exported model."},
    )
    export_device: Literal["cpu", "auto"] = field(
        default="cpu",
        metadata={"help": "The device used in model export, use `auto` to accelerate exporting."},
    )
    export_quantization_bit: int | None = field(
        default=None,
        metadata={"help": "The number of bits to quantize the exported model."},
    )
    export_quantization_dataset: str | None = field(
        default=None,
        metadata={"help": "Path to the dataset or dataset name to use in quantizing the exported model."},
    )
    export_quantization_nsamples: int = field(
        default=128,
        metadata={"help": "The number of samples used for quantization."},
    )
    export_quantization_maxlen: int = field(
        default=1024,
        metadata={"help": "The maximum length of the model inputs used for quantization."},
    )
    export_legacy_format: bool = field(
        default=False,
        metadata={"help": "Whether or not to save the `.bin` files instead of `.safetensors`."},
    )
    export_hub_model_id: str | None = field(
        default=None,
        metadata={"help": "The name of the repository if push the model to the Hugging Face hub."},
    )

    def __post_init__(self):
        if self.export_quantization_bit is not None and self.export_quantization_dataset is None:
            raise ValueError("Quantization dataset is necessary for exporting.")


@dataclass
class VllmArguments:
    r"""Arguments pertaining to the vLLM worker."""

    vllm_maxlen: int = field(
        default=4096,
        metadata={"help": "Maximum sequence (prompt + response) length of the vLLM engine."},
    )
    vllm_gpu_util: float = field(
        default=0.7,
        metadata={"help": "The fraction of GPU memory in (0,1) to be used for the vLLM engine."},
    )
    vllm_enforce_eager: bool = field(
        default=False,
        metadata={"help": "Whether or not to disable CUDA graph in the vLLM engine."},
    )
    vllm_max_lora_rank: int = field(
        default=32,
        metadata={"help": "Maximum rank of all LoRAs in the vLLM engine."},
    )
    vllm_config: dict | str | None = field(
        default=None,
        metadata={"help": "Config to initialize the vllm engine. Please use JSON strings."},
    )

    def __post_init__(self):
        if isinstance(self.vllm_config, str) and self.vllm_config.startswith("{"):
            self.vllm_config = _convert_str_dict(json.loads(self.vllm_config))


@dataclass
class SGLangArguments:
    r"""Arguments pertaining to the SGLang worker."""

    sglang_maxlen: int = field(
        default=4096,
        metadata={"help": "Maximum sequence (prompt + response) length of the SGLang engine."},
    )
    sglang_mem_fraction: float = field(
        default=0.7,
        metadata={"help": "The memory fraction (0-1) to be used for the SGLang engine."},
    )
    sglang_tp_size: int = field(
        default=-1,
        metadata={"help": "Tensor parallel size for the SGLang engine."},
    )
    sglang_config: dict | str | None = field(
        default=None,
        metadata={"help": "Config to initialize the SGLang engine. Please use JSON strings."},
    )
    sglang_lora_backend: Literal["triton", "flashinfer"] = field(
        default="triton",
        metadata={
            "help": "The backend of running GEMM kernels for Lora modules. Recommend using the Triton LoRA backend for better performance and stability."
        },
    )

    def __post_init__(self):
        if isinstance(self.sglang_config, str) and self.sglang_config.startswith("{"):
            self.sglang_config = _convert_str_dict(json.loads(self.sglang_config))


@dataclass
class KTransformersArguments:
    r"""Arguments pertaining to KTransformers AMX MoE SFT training.

    These fields are normalized into the transformers/accelerate KT config before training starts.
    """

    use_kt: bool = field(
        default=False,
        metadata={"help": "Whether to use KTransformers AMX MoE backend for SFT training."},
    )
    kt_weight_path: str | None = field(
        default=None,
        metadata={"help": "Path to pre-quantized INT8 expert weights (.kt files)."},
    )
    kt_expert_checkpoint_path: str | None = field(
        default=None,
        metadata={"help": "Path to expert checkpoint (safetensors) for online conversion."},
    )
    kt_use_lora_experts: bool | None = field(
        default=None,
        metadata={"help": "Whether to use GPU-side LoRA Experts."},
    )
    kt_lora_expert_num: int | None = field(
        default=None,
        metadata={"help": "Number of GPU-side LoRA Experts."},
    )
    kt_lora_expert_intermediate_size: int | None = field(
        default=None,
        metadata={"help": "Intermediate size for GPU-side LoRA Experts."},
    )

    def get_kt_config_dict(self, finetuning_args: Any, model_max_length: int | None) -> dict[str, Any]:
        r"""Build KT config values from LLaMA-Factory model and LoRA arguments."""
        kt_config = {
            "kt_lora_rank": getattr(finetuning_args, "lora_rank", None),
            "kt_lora_alpha": getattr(finetuning_args, "lora_alpha", None),
            "kt_weight_path": self.kt_weight_path,
            "kt_expert_checkpoint_path": self.kt_expert_checkpoint_path,
            "kt_model_max_length": model_max_length,
            "kt_use_lora_experts": self.kt_use_lora_experts,
            "kt_lora_expert_num": self.kt_lora_expert_num,
            "kt_lora_expert_intermediate_size": self.kt_lora_expert_intermediate_size,
        }
        return {key: value for key, value in kt_config.items() if value is not None}

    def apply_kt_config(self, finetuning_args: Any, training_args: Any, model_max_length: int | None) -> None:
        r"""Apply LLaMA-Factory KT args to transformers/accelerate KT integration points."""
        if not self.use_kt:
            return

        kt_config = self.get_kt_config_dict(finetuning_args, model_max_length)
        env_mapping = {
            "kt_weight_path": "ACCELERATE_KT_WEIGHT_PATH",
            "kt_expert_checkpoint_path": "ACCELERATE_KT_EXPERT_CHECKPOINT_PATH",
            "kt_model_max_length": "ACCELERATE_KT_MODEL_MAX_LENGTH",
            "kt_lora_rank": "ACCELERATE_KT_LORA_RANK",
            "kt_lora_alpha": "ACCELERATE_KT_LORA_ALPHA",
            "kt_use_lora_experts": "ACCELERATE_KT_USE_LORA_EXPERTS",
            "kt_lora_expert_num": "ACCELERATE_KT_LORA_EXPERT_NUM",
            "kt_lora_expert_intermediate_size": "ACCELERATE_KT_LORA_EXPERT_INTERMEDIATE_SIZE",
        }
        for key, env_key in env_mapping.items():
            value = kt_config.get(key)
            if value is not None:
                os.environ[env_key] = str(value)

        hf_kt = getattr(training_args, "hf_kt_config", None)
        if hf_kt is None or not hasattr(hf_kt, "_kt_config") or not isinstance(hf_kt._kt_config, dict):
            return

        hf_kt._kt_config.update(kt_config)
        gc_enabled = getattr(training_args, "gradient_checkpointing", False) or not getattr(
            self, "disable_gradient_checkpointing", True
        )
        if gc_enabled:
            hf_kt._kt_config.setdefault("kt_share_cache_pool", True)


@dataclass
class ModelArguments(
    SGLangArguments,
    VllmArguments,
    KTransformersArguments,
    ExportArguments,
    ProcessorArguments,
    QuantizationArguments,
    BaseModelArguments,
):
    r"""Arguments pertaining to which model/config/tokenizer we are going to fine-tune or infer.

    The class on the most right will be displayed first.
    """

    compute_dtype: torch.dtype | None = field(
        default=None,
        init=False,
        metadata={"help": "Torch data type for computing model outputs, derived from `fp/bf16`. Do not specify it."},
    )
    device_map: str | dict[str, Any] | None = field(
        default=None,
        init=False,
        metadata={"help": "Device map for model placement, derived from training stage. Do not specify it."},
    )
    model_max_length: int | None = field(
        default=None,
        init=False,
        metadata={"help": "The maximum input length for model, derived from `cutoff_len`. Do not specify it."},
    )
    block_diag_attn: bool = field(
        default=False,
        init=False,
        metadata={"help": "Whether use block diag attention or not, derived from `neat_packing`. Do not specify it."},
    )

    def __post_init__(self):
        BaseModelArguments.__post_init__(self)
        ProcessorArguments.__post_init__(self)
        ExportArguments.__post_init__(self)
        VllmArguments.__post_init__(self)
        SGLangArguments.__post_init__(self)

    @classmethod
    def copyfrom(cls, source: "Self", **kwargs) -> "Self":
        init_args, lazy_args = {}, {}
        for attr in fields(source):
            if attr.init:
                init_args[attr.name] = getattr(source, attr.name)
            else:
                lazy_args[attr.name] = getattr(source, attr.name)

        init_args.update(kwargs)
        result = cls(**init_args)
        for name, value in lazy_args.items():
            setattr(result, name, value)

        return result

    def to_dict(self) -> dict[str, Any]:
        args = asdict(self)
        args = {k: f"<{k.upper()}>" if k.endswith("token") else v for k, v in args.items()}
        return args
