import importlib
import json
import os
import random

from abc import abstractmethod
from dataclasses import dataclass
from functools import wraps
from typing import Any
from typing import Dict
from typing import List
from typing import Optional
from typing import Tuple

import numpy as np
import torch
import torch.nn as nn
import transformers

from packaging import version
from tokenizers import AddedToken
from tokenizers import Tokenizer
from tokenizers.models import BPE
from tokenizers.pre_tokenizers import Whitespace
from tokenizers.trainers import BpeTrainer
from transformers import PretrainedConfig
from transformers import PreTrainedModel
from transformers.tokenization_utils_base import BatchEncoding

from liger_kernel.utils import infer_device

device = infer_device()

# =============================================================================
# Transformers Version Compatibility Utilities
# =============================================================================
# These utilities help maintain backward compatibility across different
# versions of the transformers library (v4.52.0, v4.57.6, v5.0.0+).

TRANSFORMERS_VERSION = version.parse(transformers.__version__)
TRANSFORMERS_V5 = version.parse("5.0.0")


def is_transformers_v5_or_later() -> bool:
    """Check if the installed transformers version is 5.0.0 or later."""
    return TRANSFORMERS_VERSION >= TRANSFORMERS_V5


def get_mllama_rope_config() -> dict:
    """
    Get the correct rope configuration for MLlama models.

    In transformers v4.x: requires explicit rope_scaling with llama3 rope_type
    In transformers v5.0+: uses defaults, no explicit config needed

    Returns:
        dict: Configuration dictionary with rope_scaling for v4.x, empty for v5.0+
    """
    if is_transformers_v5_or_later():
        return {}
    return {
        "rope_scaling": {
            "factor": 8.0,
            "high_freq_factor": 4.0,
            "low_freq_factor": 1.0,
            "original_max_position_embeddings": 8192,
            "rope_type": "llama3",
        },
    }


def get_qwen3_vl_rope_config() -> dict:
    """
    Get the correct rope configuration for Qwen3-VL models.

    In transformers v4.x: requires rope_scaling with type="mrope"
    In transformers v5.0+: uses defaults, no explicit config needed

    Returns:
        dict: Configuration dictionary with rope_scaling for v4.x, empty for v5.0+
    """
    if is_transformers_v5_or_later():
        return {}
    return {
        "rope_theta": 1000000.0,
        "rope_scaling": {
            "type": "mrope",
            "mrope_section": [16, 24, 24],
        },
    }


def set_seed(seed=42):
    """
    Fix all random seeds we use for reproducibility.
    """
    # Python random seed
    random.seed(seed)
    # Numpy random seed
    np.random.seed(0)
    # PyTorch random seed
    torch.manual_seed(seed)

    if device == "cuda":
        # If you are using CUDA
        torch.cuda.manual_seed(seed)
        torch.cuda.manual_seed_all(seed)  # if you are using multi-GPU.

        # PyTorch backend settings
        torch.backends.cudnn.deterministic = True
        torch.backends.cudnn.benchmark = False
    elif device == "xpu":
        # If you are using XPU
        torch.xpu.manual_seed(seed)
        torch.xpu.manual_seed_all(seed)
    elif device == "npu":
        torch.npu.manual_seed(seed)
        torch.npu.manual_seed_all(seed)

    # Python hash seed
    os.environ["PYTHONHASHSEED"] = str(seed)


def require_deterministic(test_case):
    @wraps(test_case)
    def wrapper(*args, **kwargs):
        original_state = torch.are_deterministic_algorithms_enabled()
        try:
            torch.use_deterministic_algorithms(True, warn_only=True)
            return test_case(*args, **kwargs)
        finally:
            torch.use_deterministic_algorithms(original_state)

    return wrapper


@torch.no_grad
def get_logprobs(tensor):
    return torch.nn.functional.log_softmax(tensor, dim=-1, dtype=torch.float32)


@torch.no_grad
def get_topk(tensor, k=20):
    topk = torch.topk(tensor, k, dim=-1)
    return topk


def assert_verbose_allclose(tensor1, tensor2, rtol=1e-05, atol=1e-08, max_print=5, extra_info=""):
    """
    Assert that two tensors are element-wise equal within a tolerance, providing detailed information about mismatches.

    Parameters:
    tensor1 (torch.Tensor): First tensor to compare.
    tensor2 (torch.Tensor): Second tensor to compare.
    rtol (float): Relative tolerance.
    atol (float): Absolute tolerance.
    max_print (int): Maximum number of mismatched elements to print.
    extra_info (str): Extra information to show at the start of the error message.

    Raises:
    AssertionError: If the tensors are not all close within the given tolerance.
    """
    # Check if the shapes of the tensors match
    if tensor1.shape != tensor2.shape:
        raise AssertionError("Input tensors must have the same shape.")

    # Calculate the difference between the tensors
    diff = torch.abs(tensor1 - tensor2)

    # Determine the tolerance
    tolerance = atol + rtol * torch.abs(tensor2)

    # Find tolerance mismatched elements
    tol_mismatched = diff > tolerance

    # Find nan mismatched elements
    nan_mismatched = torch.logical_xor(torch.isnan(tensor1), torch.isnan(tensor2))

    # Find +inf mismatched elements
    posinf_mismatched = torch.logical_xor(torch.isposinf(tensor1), torch.isposinf(tensor2))
    # Find -inf mismatched elements
    neginf_mismatched = torch.logical_xor(torch.isneginf(tensor1), torch.isneginf(tensor2))

    # Find all mismatched elements
    mismatched = torch.logical_or(
        torch.logical_or(tol_mismatched, nan_mismatched),
        torch.logical_or(posinf_mismatched, neginf_mismatched),
    )

    mismatched_indices = torch.nonzero(mismatched)

    # Count the number of mismatched elements
    num_mismatched = mismatched.sum().item()

    # Check if all elements are close
    all_close = num_mismatched == 0

    # Raise AssertionError with detailed information if there are mismatches
    if not all_close and num_mismatched >= 1:
        mismatch_details = [f"Number of mismatched elements: {num_mismatched}"]
        print_count = min(max_print, num_mismatched)
        for index in mismatched_indices[:print_count]:
            i = tuple(index.tolist())
            mismatch_details.append(f"Mismatch at index {i}: tensor1[{i}] = {tensor1[i]}, tensor2[{i}] = {tensor2[i]}")
        if num_mismatched > max_print:
            mismatch_details.append(f"... and {num_mismatched - max_print} more mismatched elements.")

        raise AssertionError(extra_info + "\n".join(mismatch_details))


# Pre-tokenized dataset using Mistral-7B tokenizer used for convergence tests
DEFAULT_DATASET_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "resources/tiny_shakespeare_tokenized")

UNTOKENIZED_DATASET_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "resources/tiny_shakespeare.txt")

FAKE_CONFIGS_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "resources/fake_configs")


@dataclass
class MiniModelConfig:
    liger_kernel_patch_func: callable
    liger_kernel_patch_revert_func: callable
    model_class: PreTrainedModel
    mini_model_config: PretrainedConfig


def simple_collate_fn(data: List[Dict[str, Any]]):
    """A basic collate function to use for DataLoader"""
    batch = {}

    input_ids = torch.stack([torch.tensor(item["input_ids"]) for item in data])
    attention_mask = torch.stack([torch.tensor(item["attention_mask"]) for item in data])
    labels = input_ids.clone()
    batch["input_ids"] = input_ids
    batch["attention_mask"] = attention_mask
    batch["labels"] = labels
    if version.parse("4.54.1") <= version.parse(transformers.__version__):
        shift_labels = nn.functional.pad(labels, (0, 1), value=-100)
        shift_labels = shift_labels[..., 1:].contiguous()
        batch["shift_labels"] = shift_labels

    return BatchEncoding(batch)


def multimodal_collate_fn(data: List[Dict[str, Any]]):
    """A collate function to use for DataLoader for multimodal models"""
    batch = {}
    keys = set(data[0].keys())

    input_ids = torch.cat([torch.tensor(item["input_ids"]) for item in data])
    keys.remove("input_ids")
    batch["input_ids"] = input_ids

    labels = input_ids.clone()
    batch["labels"] = labels
    if version.parse("4.54.1") <= version.parse(transformers.__version__):
        shift_labels = nn.functional.pad(labels, (0, 1), value=-100)
        shift_labels = shift_labels[..., 1:].contiguous()
        batch["shift_labels"] = shift_labels

    # Collate all other keys, e.g. pixel_values, attention_mask, image_grid_thw, etc
    for key in keys:
        batch[key] = torch.cat([item[key] for item in data])

    return BatchEncoding(batch)


def load_tokenizer_config(config_path: str) -> dict:
    """Load and process tokenizer configuration from a JSON file."""
    with open(config_path) as reader:
        tokenizer_config = json.load(reader)
    tokenizer_config["added_tokens_decoder"] = {
        k: AddedToken(**v) for k, v in tokenizer_config["added_tokens_decoder"].items()
    }
    return tokenizer_config


def load_image_processing_config(config_path: str) -> dict:
    """Load and process image processing configuration from a JSON file."""
    with open(config_path) as reader:
        image_processing_config = json.load(reader)
    return image_processing_config


def load_processor_config(config_path: str) -> dict:
    """Load and process processor configuration from a JSON file."""
    with open(config_path) as reader:
        processor_config = json.load(reader)
    return processor_config


def train_bpe_tokenizer(special_tokens: List[str], unk_token: str = "<|unk|>"):
    """
    Train a tokenizer using the BPE algorithm.

    Parameters:
    unk_token (str): The token to use for unknown tokens.
    special_tokens (List[str]): A list of special tokens to use.

    Returns:
    Tokenizer: The trained tokenizer.
    """
    # Add unk_token to special_tokens if not already present
    if unk_token not in special_tokens:
        special_tokens.append(unk_token)

    tokenizer = Tokenizer(BPE(unk_token=unk_token))
    trainer = BpeTrainer(special_tokens=special_tokens)

    tokenizer.pre_tokenizer = Whitespace()
    file = [UNTOKENIZED_DATASET_PATH]
    tokenizer.train(file, trainer)

    return tokenizer


def supports_bfloat16():
    if device == "cuda":
        return torch.cuda.get_device_capability() >= (8, 0)  # Ampere and newer
    elif device == "xpu":
        return True
    elif device == "npu":
        return True
    else:
        return False


def is_torchvision_available():
    if importlib.util.find_spec("torchvision") is not None:
        return True
    else:
        return False


def revert_liger_kernel_to_granite(model_config: MiniModelConfig):
    """
    Revert all Liger kernel patches applied to Granite.
    """

    from transformers.models.granite import modeling_granite

    importlib.reload(modeling_granite)
    model_config.model_class = modeling_granite.GraniteForCausalLM
    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_llama(model_config: MiniModelConfig):
    """
    Revert all Liger kernel patches applied to Llama.
    """

    from transformers.models.llama import modeling_llama

    importlib.reload(modeling_llama)
    model_config.model_class = modeling_llama.LlamaForCausalLM
    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_smollm3(model_config: MiniModelConfig):
    """
    Revert all Liger kernel patches applied to SmolLM3.
    """

    from transformers.models.smollm3 import modeling_smollm3

    importlib.reload(modeling_smollm3)
    model_config.model_class = modeling_smollm3.SmolLM3ForCausalLM
    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_mllama(model_config: MiniModelConfig, model_type: str = "causal_lm"):
    """
    Revert all Liger kernel patches applied to MLlama.
    """

    assert model_type in [
        "causal_lm",
        "conditional_generation",
    ], f'model_type must be "causal_lm" or "conditional_generation", Got: {model_type}'
    import torch.nn as nn

    from transformers.models.mllama import modeling_mllama

    importlib.reload(nn)
    importlib.reload(modeling_mllama)
    if model_type == "causal_lm":
        model_config.model_class = modeling_mllama.MllamaForCausalLM
    else:
        model_config.model_class = modeling_mllama.MllamaForConditionalGeneration

    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_llama4(model_config: MiniModelConfig, model_type: str = "causal_lm"):
    """
    Revert all Liger kernel patches applied to Llama4.
    """

    assert model_type in [
        "causal_lm",
        "conditional_generation",
    ], f'model_type must be "causal_lm" or "conditional_generation", Got: {model_type}'
    import torch.nn as nn

    from transformers.models.llama4 import modeling_llama4

    importlib.reload(nn)
    importlib.reload(modeling_llama4)
    if model_type == "causal_lm":
        model_config.model_class = modeling_llama4.Llama4ForCausalLM
    else:
        model_config.model_class = modeling_llama4.Llama4ForConditionalGeneration

    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_mistral(model_config: MiniModelConfig):
    """
    Revert all Liger kernel patches applied to Mistral.
    """

    from transformers.models.mistral import modeling_mistral

    importlib.reload(modeling_mistral)
    model_config.model_class = modeling_mistral.MistralForCausalLM
    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_mixtral(model_config: MiniModelConfig):
    """
    Revert all Liger kernel patches applied to Mixtral.
    """

    from transformers.models.mixtral import modeling_mixtral

    importlib.reload(modeling_mixtral)
    model_config.model_class = modeling_mixtral.MixtralForCausalLM
    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_gemma(model_config: MiniModelConfig):
    """
    Revert all Liger kernel patches applied to Gemma.
    """

    from transformers.models.gemma import modeling_gemma

    importlib.reload(modeling_gemma)
    model_config.model_class = modeling_gemma.GemmaForCausalLM
    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_gemma2(model_config: MiniModelConfig):
    """
    Revert all Liger kernel patches applied to Gemma2.
    """

    from transformers.models.gemma2 import modeling_gemma2

    importlib.reload(modeling_gemma2)
    model_config.model_class = modeling_gemma2.Gemma2ForCausalLM
    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_gemma3_text(model_config: MiniModelConfig):
    """
    Revert all Liger kernel patches applied to Gemma3.
    """

    from transformers.models.gemma3 import modeling_gemma3

    importlib.reload(modeling_gemma3)

    model_config.model_class = modeling_gemma3.Gemma3ForCausalLM

    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_gemma3(model_config: MiniModelConfig):
    """
    Revert all Liger kernel patches applied to Gemma3.
    """

    from transformers.models.gemma3 import modeling_gemma3
    from transformers.models.siglip import modeling_siglip

    importlib.reload(modeling_gemma3)
    importlib.reload(modeling_siglip)
    model_config.model_class = modeling_gemma3.Gemma3ForConditionalGeneration
    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_Paligemma(model_config: MiniModelConfig):
    """
    Revert all Liger kernel patches applied to Paligemma.
    """

    from transformers.models.gemma import modeling_gemma
    from transformers.models.gemma2 import modeling_gemma2
    from transformers.models.paligemma import modeling_paligemma
    from transformers.models.siglip import modeling_siglip

    importlib.reload(modeling_gemma)
    importlib.reload(modeling_gemma2)
    importlib.reload(modeling_paligemma)
    importlib.reload(modeling_siglip)
    model_config.model_class = modeling_paligemma.PaliGemmaForConditionalGeneration
    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_qwen2(model_config: MiniModelConfig):
    """
    Revert all Liger kernel patches applied to Qwen2.
    """

    from transformers.models.qwen2 import modeling_qwen2

    importlib.reload(modeling_qwen2)
    model_config.model_class = modeling_qwen2.Qwen2ForCausalLM

    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_qwen3(model_config: MiniModelConfig):
    """
    Revert all Liger kernel patches applied to Qwen3.
    """
    from transformers.models.qwen3 import modeling_qwen3

    importlib.reload(modeling_qwen3)
    model_config.model_class = modeling_qwen3.Qwen3ForCausalLM

    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_qwen3_moe(model_config: MiniModelConfig):
    """
    Revert all Liger kernel patches applied to Qwen3 MoE.
    """
    from transformers.models.qwen3_moe import modeling_qwen3_moe

    importlib.reload(modeling_qwen3_moe)
    model_config.model_class = modeling_qwen3_moe.Qwen3MoeForCausalLM

    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_gpt_oss(model_config: MiniModelConfig):
    """
    Revert all Liger kernel patches applied to GPT-OSS.
    """
    from transformers.models.gpt_oss import modeling_gpt_oss

    importlib.reload(modeling_gpt_oss)
    model_config.model_class = modeling_gpt_oss.GptOssForCausalLM

    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_qwen2_vl(model_config: MiniModelConfig):
    """
    Revert all Liger kernel patches applied to Qwen2-VL.
    """
    from transformers.models.qwen2_vl import modeling_qwen2_vl

    importlib.reload(modeling_qwen2_vl)
    model_config.model_class = modeling_qwen2_vl.Qwen2VLForConditionalGeneration
    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_qwen2_5_vl(model_config: MiniModelConfig):
    """
    Revert all Liger kernel patches applied to Qwen2.5-VL.
    """
    from transformers.models.qwen2_5_vl import modeling_qwen2_5_vl

    importlib.reload(modeling_qwen2_5_vl)
    model_config.model_class = modeling_qwen2_5_vl.Qwen2_5_VLForConditionalGeneration
    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_qwen3_vl(model_config: MiniModelConfig):
    """
    Revert all Liger kernel patches applied to Qwen3-VL.
    """
    from transformers.models.qwen3_vl import modeling_qwen3_vl

    importlib.reload(modeling_qwen3_vl)
    model_config.model_class = modeling_qwen3_vl.Qwen3VLForConditionalGeneration
    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_qwen3_vl_moe(model_config: MiniModelConfig):
    """
    Revert all Liger kernel patches applied to Qwen3-VL-MoE.
    """
    from transformers.models.qwen3_vl_moe import modeling_qwen3_vl_moe

    importlib.reload(modeling_qwen3_vl_moe)
    model_config.model_class = modeling_qwen3_vl_moe.Qwen3VLMoeForConditionalGeneration
    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_phi3(model_config: MiniModelConfig):
    """
    Revert all Liger kernel patches applied to Phi3.
    """

    from transformers.models.phi3 import modeling_phi3

    importlib.reload(modeling_phi3)
    model_config.model_class = modeling_phi3.Phi3ForCausalLM
    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_pixtral(model_config: MiniModelConfig):
    """
    Revert all Liger kernel patches applied to Pixtral.
    """
    from transformers.models.pixtral import modeling_pixtral

    importlib.reload(modeling_pixtral)
    model_config.model_class = modeling_pixtral.PixtralVisionModel
    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_olmo2(model_config: MiniModelConfig):
    """
    Revert all Liger kernel patches applied to Olmo2.
    """

    from transformers.models.olmo2 import modeling_olmo2

    importlib.reload(modeling_olmo2)
    model_config.model_class = modeling_olmo2.Olmo2ForCausalLM
    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_olmo3(model_config: MiniModelConfig):
    """
    Revert all Liger kernel patches applied to Olmo3.
    """

    from transformers.models.olmo3 import modeling_olmo3

    importlib.reload(modeling_olmo3)
    model_config.model_class = modeling_olmo3.Olmo3ForCausalLM
    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_glm4(model_config: MiniModelConfig):
    """
    Revert all Liger kernel patches applied to Glm4.
    """

    from transformers.models.glm4 import modeling_glm4

    importlib.reload(modeling_glm4)
    model_config.model_class = modeling_glm4.Glm4ForCausalLM
    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_glm4v(model_config: MiniModelConfig):
    """
    Revert all Liger kernel patches applied to Glm4v.
    """

    from transformers.models.glm4v import modeling_glm4v

    importlib.reload(modeling_glm4v)
    model_config.model_class = modeling_glm4v.Glm4vForConditionalGeneration
    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_glm4v_moe(model_config: MiniModelConfig):
    """
    Revert all Liger kernel patches applied to Glm4v_MoE.
    """

    from transformers.models.glm4v_moe import modeling_glm4v_moe

    importlib.reload(modeling_glm4v_moe)
    model_config.model_class = modeling_glm4v_moe.Glm4vMoeForConditionalGeneration
    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_llava(model_config: MiniModelConfig):
    """
    Revert all Liger kernel patches applied to llava.
    """

    from transformers.models.llama import modeling_llama
    from transformers.models.llava import modeling_llava

    # Note: Do NOT reload modeling_clip as it breaks CLIPVisionModel's
    # output_hidden_states functionality in transformers v5.
    # Liger kernel does not patch modeling_clip when model=None.
    importlib.reload(modeling_llava)
    importlib.reload(modeling_llama)

    model_config.model_class = modeling_llava.LlavaForConditionalGeneration
    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_internvl(model_config: MiniModelConfig):
    """
    Revert all Liger kernel patches applied to InternVL.
    """
    import torch.nn as nn

    from transformers.models.internvl import modeling_internvl
    from transformers.models.qwen2 import modeling_qwen2

    importlib.reload(nn)
    importlib.reload(modeling_internvl)
    importlib.reload(modeling_qwen2)

    model_config.model_class = modeling_internvl.InternVLForConditionalGeneration
    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_smolvlm2(model_config: MiniModelConfig):
    """
    Revert all Liger kernel patches applied to SmolVLM2.
    """
    import torch.nn as nn

    from transformers.models.llama import modeling_llama
    from transformers.models.smolvlm import modeling_smolvlm

    importlib.reload(nn)
    importlib.reload(modeling_smolvlm)
    importlib.reload(modeling_llama)

    model_config.model_class = modeling_smolvlm.SmolVLMForConditionalGeneration
    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_falcon_h1(model_config: MiniModelConfig):
    """
    Revert all Liger kernel patches applied to FalconH1.
    """

    from transformers.models.falcon_h1 import modeling_falcon_h1

    importlib.reload(modeling_falcon_h1)
    model_config.model_class = modeling_falcon_h1.FalconH1ForCausalLM
    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_qwen3_next(model_config: MiniModelConfig):
    """
    Revert all Liger kernel patches applied to Qwen3Next.
    """

    from transformers.models.qwen3_next import modeling_qwen3_next

    importlib.reload(modeling_qwen3_next)
    model_config.model_class = modeling_qwen3_next.Qwen3NextForCausalLM
    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_qwen3_5(model_config: MiniModelConfig, model_type: str = "causal_lm"):
    """
    Revert all Liger kernel patches applied to Qwen3.5 dense.
    """

    assert model_type in [
        "causal_lm",
        "conditional_generation",
    ], f'model_type must be "causal_lm" or "conditional_generation", Got: {model_type}'

    import torch.nn as nn

    from transformers.models.qwen3_5 import modeling_qwen3_5

    importlib.reload(nn)
    importlib.reload(modeling_qwen3_5)
    if model_type == "causal_lm":
        model_config.model_class = modeling_qwen3_5.Qwen3_5ForCausalLM
    else:
        model_config.model_class = modeling_qwen3_5.Qwen3_5ForConditionalGeneration

    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_qwen3_5_moe(model_config: MiniModelConfig):
    """
    Revert all Liger kernel patches applied to Qwen3.5 MoE.
    """

    from transformers.models.qwen3_5_moe import modeling_qwen3_5_moe

    importlib.reload(modeling_qwen3_5_moe)
    model_config.model_class = modeling_qwen3_5_moe.Qwen3_5MoeForCausalLM
    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_hunyuan_v1(model_config: MiniModelConfig):
    """
    Revert all Liger kernel patches applied to Hunyuanv1.
    """
    from transformers.models.hunyuan_v1_dense import modeling_hunyuan_v1_dense

    importlib.reload(modeling_hunyuan_v1_dense)
    model_config.model_class = modeling_hunyuan_v1_dense.HunYuanDenseV1ForCausalLM

    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_hunyuan_v1_moe(model_config: MiniModelConfig):
    """
    Revert all Liger kernel patches applied to Hunyuanv1 MoE.
    """
    from transformers.models.hunyuan_v1_moe import modeling_hunyuan_v1_moe

    importlib.reload(modeling_hunyuan_v1_moe)
    model_config.model_class = modeling_hunyuan_v1_moe.HunYuanMoEV1ForCausalLM

    print("Liger kernel patches have been reverted.")


def revert_liger_kernel_to_exaone4(model_config: MiniModelConfig):
    """
    Revert all Liger kernel patches applied to EXAONE4.
    """
    from transformers.models.exaone4 import modeling_exaone4

    importlib.reload(modeling_exaone4)
    model_config.model_class = modeling_exaone4.Exaone4ForCausalLM
    print("Liger kernel patches have been reverted.")


class HFAlignmentLoss:
    def __init__(
        self,
        alpha: float = 1.0,
        beta: float = 0.1,
        ignore_index: int = -100,
        use_ref_model: bool = False,
        unpaired: bool = False,
        compute_nll_loss: bool = True,
        **kwargs,
    ):
        self.alpha = alpha
        self.beta = beta
        self.ignore_index = ignore_index
        self.use_ref_model = use_ref_model
        self.unpaired = unpaired
        self.compute_nll_loss = compute_nll_loss

    @abstractmethod
    def alignment_loss(self):
        pass

    def get_batch_logps(
        self,
        logits: torch.FloatTensor,
        labels: torch.LongTensor,
        average_log_prob: bool = False,
    ) -> torch.FloatTensor:
        """Compute the log probabilities of the given labels under the given logits.

        Args:
            logits: Logits of the model (unnormalized). Shape: (batch_size, sequence_length, vocab_size)
            labels: Labels for which to compute the log probabilities. Label tokens with a value of ignore_index are ignored. Shape: (batch_size, sequence_length)
            average_log_prob: If True, return the average log probability per (non-masked) token. Otherwise, return the sum of the log probabilities of the (non-masked) tokens.
            is_encoder_decoder: Whether the model is an encoder-decoder model.
        Returns:
            A tensor of shape (batch_size,) containing the average/sum log probabilities of the given labels under the given logits.
        """
        if logits.shape[:-1] != labels.shape:
            raise ValueError("Logits (batch and sequence length dim) and labels must have the same shape.")

        loss_mask = labels != self.ignore_index

        # dummy token; we'll ignore the losses on these tokens later
        labels = torch.where(labels == self.ignore_index, 0, labels)

        per_token_logps = torch.gather(logits.log_softmax(-1), dim=2, index=labels.unsqueeze(2)).squeeze(2)

        if average_log_prob:
            return (per_token_logps * loss_mask).sum(-1) / loss_mask.sum(-1)
        else:
            return (per_token_logps * loss_mask).sum(-1)

    def get_ref_logps(
        self,
        ref_input: torch.FloatTensor,
        ref_weight: torch.FloatTensor,
        target: torch.LongTensor,
        ref_bias: torch.FloatTensor,
        average_log_prob: bool = True,
        preference_labels: torch.Tensor = None,
    ):
        """Compute the log probabilities of the given labels under the given reference model."""

        with torch.no_grad():
            ref_logits = ref_input @ ref_weight.t()
            if ref_bias is not None:
                ref_logits = ref_logits + ref_bias
            ref_all_logps = self.get_batch_logps(ref_logits, target, average_log_prob=average_log_prob)

            if self.unpaired and preference_labels is not None:
                # Split based on preference labels
                return (
                    ref_all_logps[preference_labels],
                    ref_all_logps[~preference_labels],
                )
            else:
                # Original paired behavior - split in half
                return (
                    ref_all_logps[: ref_input.shape[0] // 2],
                    ref_all_logps[ref_input.shape[0] // 2 :],
                )

    def concatenated_forward(
        self,
        _input: torch.FloatTensor,
        weight: torch.FloatTensor,
        target: torch.LongTensor,
        bias: Optional[torch.FloatTensor] = None,
        average_log_prob: bool = True,
        preference_labels: torch.Tensor = None,
        nll_target: Optional[torch.LongTensor] = None,
    ) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
        """Run the given model on the given batch of inputs, concatenating the chosen and rejected inputs together.

        We do this to avoid doing two forward passes, because it's faster for FSDP.
        """
        len_chosen = _input.shape[0] // 2

        outputs = _input @ weight.t()
        if bias is not None:
            outputs = outputs + bias
        all_logits = outputs.float()

        def cross_entropy_loss(logits, labels):
            # Flatten the tokens
            loss_fct = nn.CrossEntropyLoss(ignore_index=self.ignore_index)
            logits = logits.view(-1, logits.shape[-1])
            labels = labels.view(-1)
            # Enable model parallelism
            labels = labels.to(logits.device)
            loss = loss_fct(logits, labels)
            return loss

        labels = nll_target if nll_target is not None else target
        chosen_nll_loss = torch.tensor(0.0, device=all_logits.device)
        if self.compute_nll_loss:
            chosen_nll_loss = cross_entropy_loss(all_logits[:len_chosen], labels[:len_chosen])

        all_logps = self.get_batch_logps(
            all_logits,
            target,
            average_log_prob=average_log_prob,
        )

        if self.unpaired and preference_labels is not None:
            # Split based on labels tensor
            chosen_logps = all_logps[preference_labels]
            rejected_logps = all_logps[~preference_labels]
            chosen_logits = all_logits[preference_labels]
            rejected_logits = all_logits[~preference_labels]
        else:
            # Original paired behavior - split in half
            len_chosen = _input.shape[0] // 2
            chosen_logps = all_logps[:len_chosen]
            rejected_logps = all_logps[len_chosen:]
            chosen_logits = all_logits[:len_chosen]
            rejected_logits = all_logits[len_chosen:]

        return (
            chosen_logps,
            rejected_logps,
            chosen_logits,
            rejected_logits,
            chosen_nll_loss,
        )

    def get_batch_loss_metrics(
        self,
        weight: torch.FloatTensor,
        _input: torch.FloatTensor,
        target: torch.LongTensor,
        bias: torch.FloatTensor = None,
        ref_input: torch.FloatTensor = None,
        ref_weight: torch.FloatTensor = None,
        ref_bias: torch.FloatTensor = None,
        average_log_prob: bool = True,
        preference_labels: torch.Tensor = None,
        nll_target: torch.LongTensor = None,
        **loss_kwargs,
    ):
        """Compute the loss metrics for the given batch of inputs for train or test."""
        forward_output = self.concatenated_forward(
            _input, weight, target, bias, average_log_prob, preference_labels, nll_target
        )
        (
            policy_chosen_logps,
            policy_rejected_logps,
            policy_chosen_logits,
            policy_rejected_logits,
            policy_nll_loss,
        ) = forward_output[:5]

        if self.use_ref_model:
            ref_chosen_logps, ref_rejected_logps = self.get_ref_logps(
                ref_input,
                ref_weight,
                target,
                ref_bias,
                average_log_prob,
                preference_labels,
            )
            loss_kwargs["ref_chosen_logps"] = ref_chosen_logps
            loss_kwargs["ref_rejected_logps"] = ref_rejected_logps
        alignment_loss_outputs = self.alignment_loss(policy_chosen_logps, policy_rejected_logps, **loss_kwargs)
        if isinstance(alignment_loss_outputs, tuple):
            losses, *aggregated_aux_outputs = alignment_loss_outputs
        else:
            losses, aggregated_aux_outputs = alignment_loss_outputs, []

        loss = policy_nll_loss * self.alpha + losses.mean()

        if not self.unpaired:
            return_vars = (
                policy_chosen_logps,
                policy_rejected_logps,
                policy_chosen_logits.detach().mean(),
                policy_rejected_logits.detach().mean(),
                policy_nll_loss,
            )
            return loss, (*return_vars, *aggregated_aux_outputs)
        else:
            return_vars = (
                policy_chosen_logps.detach().sum(),
                policy_rejected_logps.detach().sum(),
                policy_chosen_logits.detach().sum(),
                policy_rejected_logits.detach().sum(),
            )
            return loss, (*return_vars, *aggregated_aux_outputs)


class HFDistillationLoss:
    def __init__(
        self,
        weight_hard_loss: float = 0.5,
        weight_soft_loss: float = 0.5,
        ignore_index: int = -100,
        temperature: float = 1,
    ):
        self.weight_hard_loss = weight_hard_loss
        self.weight_soft_loss = weight_soft_loss
        self.ignore_index = ignore_index
        self.temperature = temperature

    @abstractmethod
    def distillation_loss(self, student_logits, teacher_logits, **loss_kwargs):
        """Abstract method for computing distillation loss."""
        pass

    def concatenated_forward(
        self,
        student_input: torch.FloatTensor,
        student_weight: torch.FloatTensor,
        teacher_input: torch.FloatTensor,
        teacher_weight: torch.FloatTensor,
        target: torch.LongTensor,
        student_bias: torch.FloatTensor = None,
        teacher_bias: torch.FloatTensor = None,
    ) -> Tuple[
        torch.FloatTensor,
        torch.FloatTensor,
        torch.FloatTensor,
        torch.FloatTensor,
        torch.FloatTensor,
    ]:
        """Compute forward pass for both student and teacher models."""

        student_batch_seq_len_size, student_hidden_size = student_input.shape
        student_input_reshaped = student_input.view(-1, student_hidden_size)
        teacher_batch_seq_len_size, teacher_hidden_size = teacher_input.shape
        teacher_input_reshaped = teacher_input.view(-1, teacher_hidden_size)

        student_outputs = student_input_reshaped @ student_weight.t()
        if student_bias is not None:
            student_outputs = student_outputs + student_bias

        with torch.no_grad():
            teacher_outputs = teacher_input_reshaped @ teacher_weight.t()
            if teacher_bias is not None:
                teacher_outputs = teacher_outputs + teacher_bias

        student_logits = student_outputs.view(student_batch_seq_len_size, -1).float()
        teacher_logits = teacher_outputs.view(teacher_batch_seq_len_size, -1).float()

        if torch.all(target == self.ignore_index):
            return torch.tensor(0.0)

        def cross_entropy_loss(logits, labels):
            # Flatten the tokens
            loss_fct = nn.CrossEntropyLoss(ignore_index=self.ignore_index)
            logits = logits.view(-1, logits.shape[-1])
            labels = labels.view(-1)
            # Enable model parallelism
            labels = labels.to(logits.device)
            loss = loss_fct(logits, labels)
            return loss

        labels = target
        ce_loss = cross_entropy_loss(
            student_logits.view(-1, student_logits.shape[-1]),
            labels.view(-1),
        )

        return (
            student_logits,
            teacher_logits,
            ce_loss,
        )

    def get_batch_loss_metrics(
        self,
        student_input: torch.FloatTensor,
        student_weight: torch.FloatTensor,
        teacher_input: torch.FloatTensor,
        teacher_weight: torch.FloatTensor,
        target: torch.LongTensor,
        student_bias: torch.FloatTensor = None,
        teacher_bias: torch.FloatTensor = None,
        **loss_kwargs,
    ):
        """Compute the distillation loss metrics for the given batch."""
        forward_output = self.concatenated_forward(
            student_input,
            student_weight,
            teacher_input,
            teacher_weight,
            target,
            student_bias,
            teacher_bias,
        )
        (
            student_logits,
            teacher_logits,
            hard_loss,
        ) = forward_output

        student_logits /= self.temperature
        teacher_logits /= self.temperature

        soft_loss = self.distillation_loss(
            student_logits, teacher_logits, target=target, ignore_index=self.ignore_index, **loss_kwargs
        )
        # full loss
        loss = self.weight_hard_loss * hard_loss + self.weight_soft_loss * soft_loss
        return loss