"""Model adapter registration."""

import math
import os
import sys
from typing import Dict, List, Optional
import warnings

if sys.version_info >= (3, 9):
    from functools import cache
else:
    from functools import lru_cache as cache

import accelerate
import psutil
import torch
from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeq2SeqLM,
    AutoTokenizer,
    LlamaTokenizer,
    LlamaForCausalLM,
    T5Tokenizer,
)

from fastchat.modules.gptq import GptqConfig, load_gptq_quantized
from fastchat.modules.awq import AWQConfig, load_awq_quantized
from fastchat.conversation import Conversation, get_conv_template
from fastchat.model.compression import load_compress_model
from fastchat.model.llama_condense_monkey_patch import (
    replace_llama_with_condense,
)
from fastchat.model.model_chatglm import generate_stream_chatglm
from fastchat.model.model_codet5p import generate_stream_codet5p
from fastchat.model.model_falcon import generate_stream_falcon
from fastchat.model.monkey_patch_non_inplace import (
    replace_llama_attn_with_non_inplace_operations,
)
from fastchat.utils import get_gpu_memory

# Check an environment variable to check if we should be sharing Peft model
# weights.  When false we treat all Peft models as separate.
peft_share_base_weights = (
    os.environ.get("PEFT_SHARE_BASE_WEIGHTS", "false").lower() == "true"
)


class BaseModelAdapter:
    """The base and the default model adapter."""

    use_fast_tokenizer = True

    def match(self, model_path: str):
        return True

    def load_model(self, model_path: str, from_pretrained_kwargs: dict):
        revision = from_pretrained_kwargs.get("revision", "main")
        try:
            tokenizer = AutoTokenizer.from_pretrained(
                model_path,
                use_fast=self.use_fast_tokenizer,
                revision=revision,
                trust_remote_code=True,
            )
        except TypeError:
            tokenizer = AutoTokenizer.from_pretrained(
                model_path, use_fast=False, revision=revision, trust_remote_code=True
            )
        try:
            model = AutoModelForCausalLM.from_pretrained(
                model_path, low_cpu_mem_usage=True, **from_pretrained_kwargs
            )
        except NameError:
            model = AutoModel.from_pretrained(
                model_path, low_cpu_mem_usage=True, **from_pretrained_kwargs
            )
        return model, tokenizer

    def load_compress_model(self, model_path, device, torch_dtype, revision="main"):
        return load_compress_model(
            model_path,
            device,
            torch_dtype,
            use_fast=self.use_fast_tokenizer,
            revision=revision,
        )

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("one_shot")


# A global registry for all model adapters
# TODO (lmzheng): make it a priority queue.
model_adapters: List[BaseModelAdapter] = []


def register_model_adapter(cls):
    """Register a model adapter."""
    model_adapters.append(cls())


@cache
def get_model_adapter(model_path: str) -> BaseModelAdapter:
    """Get a model adapter for a model_path."""
    model_path_basename = os.path.basename(os.path.normpath(model_path))

    # Try the basename of model_path at first
    for adapter in model_adapters:
        if adapter.match(model_path_basename) and type(adapter) != BaseModelAdapter:
            return adapter

    # Then try the full path
    for adapter in model_adapters:
        if adapter.match(model_path):
            return adapter

    raise ValueError(f"No valid model adapter for {model_path}")


def raise_warning_for_incompatible_cpu_offloading_configuration(
    device: str, load_8bit: bool, cpu_offloading: bool
):
    if cpu_offloading:
        if not load_8bit:
            warnings.warn(
                "The cpu-offloading feature can only be used while also using 8-bit-quantization.\n"
                "Use '--load-8bit' to enable 8-bit-quantization\n"
                "Continuing without cpu-offloading enabled\n"
            )
            return False
        if not "linux" in sys.platform:
            warnings.warn(
                "CPU-offloading is only supported on linux-systems due to the limited compatability with the bitsandbytes-package\n"
                "Continuing without cpu-offloading enabled\n"
            )
            return False
        if device != "cuda":
            warnings.warn(
                "CPU-offloading is only enabled when using CUDA-devices\n"
                "Continuing without cpu-offloading enabled\n"
            )
            return False
    return cpu_offloading


def convert_llama_model_to_hf(loaded, config, model_size):
    n_heads = config.num_attention_heads
    num_shards = 1
    n_layers = config.num_hidden_layers
    n_heads_per_shard = n_heads // num_shards
    dim = config.hidden_size
    dims_per_head = dim // n_heads
    base = 10000.0
    inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head))

    # permute for sliced rotary
    def permute(w):
        return w.view(n_heads, dim // n_heads // 2, 2, dim).transpose(1, 2).reshape(dim, dim)

    state_dict = {}
    for layer_i in range(n_layers):
        filename = f"pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin"
        if model_size == "7B":
            # Unsharded
            state_dict.update({
                f"model.layers.{layer_i}.self_attn.q_proj.weight": permute(
                    loaded[f"layers.{layer_i}.attention.wq.weight"]
                ),
                f"model.layers.{layer_i}.self_attn.k_proj.weight": permute(
                    loaded[f"layers.{layer_i}.attention.wk.weight"]
                ),
                f"model.layers.{layer_i}.self_attn.v_proj.weight": loaded[f"layers.{layer_i}.attention.wv.weight"],
                f"model.layers.{layer_i}.self_attn.o_proj.weight": loaded[f"layers.{layer_i}.attention.wo.weight"],
                f"model.layers.{layer_i}.mlp.gate_proj.weight": loaded[f"layers.{layer_i}.feed_forward.w1.weight"],
                f"model.layers.{layer_i}.mlp.down_proj.weight": loaded[f"layers.{layer_i}.feed_forward.w2.weight"],
                f"model.layers.{layer_i}.mlp.up_proj.weight": loaded[f"layers.{layer_i}.feed_forward.w3.weight"],
                f"model.layers.{layer_i}.input_layernorm.weight": loaded[f"layers.{layer_i}.attention_norm.weight"],
                f"model.layers.{layer_i}.post_attention_layernorm.weight": loaded[f"layers.{layer_i}.ffn_norm.weight"],
            })
        else:
            raise NotImplementedError
            # Sharded
            # Note that in the 13B checkpoint, not cloning the two following weights will result in the checkpoint
            # becoming 37GB instead of 26GB for some reason.
            state_dict = {
                f"model.layers.{layer_i}.input_layernorm.weight": loaded[0][
                    f"layers.{layer_i}.attention_norm.weight"
                ].clone(),
                f"model.layers.{layer_i}.post_attention_layernorm.weight": loaded[0][
                    f"layers.{layer_i}.ffn_norm.weight"
                ].clone(),
            }
            state_dict[f"model.layers.{layer_i}.self_attn.q_proj.weight"] = permute(
                torch.cat(
                    [
                        loaded[i][f"layers.{layer_i}.attention.wq.weight"].view(n_heads_per_shard, dims_per_head, dim)
                        for i in range(num_shards)
                    ],
                    dim=0,
                ).reshape(dim, dim)
            )
            state_dict[f"model.layers.{layer_i}.self_attn.k_proj.weight"] = permute(
                torch.cat(
                    [
                        loaded[i][f"layers.{layer_i}.attention.wk.weight"].view(n_heads_per_shard, dims_per_head, dim)
                        for i in range(num_shards)
                    ],
                    dim=0,
                ).reshape(dim, dim)
            )
            state_dict[f"model.layers.{layer_i}.self_attn.v_proj.weight"] = torch.cat(
                [
                    loaded[i][f"layers.{layer_i}.attention.wv.weight"].view(n_heads_per_shard, dims_per_head, dim)
                    for i in range(num_shards)
                ],
                dim=0,
            ).reshape(dim, dim)

            state_dict[f"model.layers.{layer_i}.self_attn.o_proj.weight"] = torch.cat(
                [loaded[i][f"layers.{layer_i}.attention.wo.weight"] for i in range(num_shards)], dim=1
            )
            state_dict[f"model.layers.{layer_i}.mlp.gate_proj.weight"] = torch.cat(
                [loaded[i][f"layers.{layer_i}.feed_forward.w1.weight"] for i in range(num_shards)], dim=0
            )
            state_dict[f"model.layers.{layer_i}.mlp.down_proj.weight"] = torch.cat(
                [loaded[i][f"layers.{layer_i}.feed_forward.w2.weight"] for i in range(num_shards)], dim=1
            )
            state_dict[f"model.layers.{layer_i}.mlp.up_proj.weight"] = torch.cat(
                [loaded[i][f"layers.{layer_i}.feed_forward.w3.weight"] for i in range(num_shards)], dim=0
            )

        state_dict[f"model.layers.{layer_i}.self_attn.rotary_emb.inv_freq"] = inv_freq

    if model_size == "7B":
        # Unsharded
        state_dict.update({
            "model.embed_tokens.weight": loaded["tok_embeddings.weight"],
            "model.norm.weight": loaded["norm.weight"],
            "lm_head.weight": loaded["output.weight"],
        })
    else:
        raise NotImplementedError
        state_dict = {
            "model.norm.weight": loaded[0]["norm.weight"],
            "model.embed_tokens.weight": torch.cat(
                [loaded[i]["tok_embeddings.weight"] for i in range(num_shards)], dim=1
            ),
            "lm_head.weight": torch.cat([loaded[i]["output.weight"] for i in range(num_shards)], dim=0),
        }
    return state_dict

def load_model(
    model_path: str,
    device: str = "cuda",
    num_gpus: int = 1,
    max_gpu_memory: Optional[str] = None,
    load_8bit: bool = False,
    cpu_offloading: bool = False,
    gptq_config: Optional[GptqConfig] = None,
    awq_config: Optional[AWQConfig] = None,
    revision: str = "main",
    debug: bool = False,
):
    """Load a model from Hugging Face."""
    # get model adapter
    adapter = get_model_adapter(model_path)

    # Handle device mapping
    cpu_offloading = raise_warning_for_incompatible_cpu_offloading_configuration(
        device, load_8bit, cpu_offloading
    )
    if device == "cpu":
        kwargs = {"torch_dtype": torch.float32}
    elif device == "cuda":
        kwargs = {"torch_dtype": torch.float16}
        if num_gpus != 1:
            kwargs["device_map"] = "auto"
            if max_gpu_memory is None:
                kwargs[
                    "device_map"
                ] = "sequential"  # This is important for not the same VRAM sizes
                available_gpu_memory = get_gpu_memory(num_gpus)
                kwargs["max_memory"] = {
                    i: str(int(available_gpu_memory[i] * 0.85)) + "GiB"
                    for i in range(num_gpus)
                }
            else:
                kwargs["max_memory"] = {i: max_gpu_memory for i in range(num_gpus)}
    elif device == "mps":
        kwargs = {"torch_dtype": torch.float16}
        # Avoid bugs in mps backend by not using in-place operations.
        replace_llama_attn_with_non_inplace_operations()
    elif device == "xpu":
        kwargs = {"torch_dtype": torch.bfloat16}
        # Try to load ipex, while it looks unused, it links into torch for xpu support
        try:
            import intel_extension_for_pytorch as ipex
        except ImportError:
            warnings.warn(
                "Intel Extension for PyTorch is not installed, but is required for xpu inference."
            )
    else:
        raise ValueError(f"Invalid device: {device}")

    if cpu_offloading:
        # raises an error on incompatible platforms
        from transformers import BitsAndBytesConfig

        if "max_memory" in kwargs:
            kwargs["max_memory"]["cpu"] = (
                str(math.floor(psutil.virtual_memory().available / 2**20)) + "Mib"
            )
        kwargs["quantization_config"] = BitsAndBytesConfig(
            load_in_8bit_fp32_cpu_offload=cpu_offloading
        )
        kwargs["load_in_8bit"] = load_8bit
    elif load_8bit:
        if num_gpus != 1:
            warnings.warn(
                "8-bit quantization is not supported for multi-gpu inference."
            )
        else:
            model, tokenizer = adapter.load_compress_model(
                model_path=model_path,
                device=device,
                torch_dtype=kwargs["torch_dtype"],
                revision=revision,
            )
            if debug:
                print(model)
            return model, tokenizer
    elif awq_config and awq_config.wbits < 16:
        assert (
            awq_config.wbits == 4
        ), "Currently we only support 4-bit inference for AWQ."
        model, tokenizer = load_awq_quantized(model_path, awq_config, device)
        if num_gpus != 1:
            device_map = accelerate.infer_auto_device_map(
                model,
                max_memory=kwargs["max_memory"],
                no_split_module_classes=[
                    "OPTDecoderLayer",
                    "LlamaDecoderLayer",
                    "BloomBlock",
                    "MPTBlock",
                    "DecoderLayer",
                ],
            )
            model = accelerate.dispatch_model(
                model, device_map=device_map, offload_buffers=True
            )
        else:
            model.to(device)
        return model, tokenizer
    elif gptq_config and gptq_config.wbits < 16:
        model, tokenizer = load_gptq_quantized(model_path, gptq_config)
        if num_gpus != 1:
            device_map = accelerate.infer_auto_device_map(
                model,
                max_memory=kwargs["max_memory"],
                no_split_module_classes=["LlamaDecoderLayer"],
            )
            model = accelerate.dispatch_model(
                model, device_map=device_map, offload_buffers=True
            )
        else:
            model.to(device)
        return model, tokenizer
    kwargs["revision"] = revision

    # Load model
    model, tokenizer = adapter.load_model(model_path, kwargs)

    if (device == "cuda" and num_gpus == 1 and not cpu_offloading) or device in (
        "mps",
        "xpu",
    ):
        model.to(device)

    if device == "xpu":
        model = torch.xpu.optimize(model, dtype=kwargs["torch_dtype"], inplace=True)

    if debug:
        print(model)

    return model, tokenizer


def get_conversation_template(model_path: str) -> Conversation:
    """Get the default conversation template."""
    adapter = get_model_adapter(model_path)
    return adapter.get_default_conv_template(model_path)


def get_generate_stream_function(model: torch.nn.Module, model_path: str):
    """Get the generate_stream function for inference."""
    from fastchat.serve.inference import generate_stream

    model_type = str(type(model)).lower()
    is_chatglm = "chatglm" in model_type
    is_falcon = "rwforcausallm" in model_type
    is_codet5p = "codet5p" in model_type
    is_peft = "peft" in model_type

    if is_chatglm:
        return generate_stream_chatglm
    elif is_falcon:
        return generate_stream_falcon
    elif is_codet5p:
        return generate_stream_codet5p
    elif peft_share_base_weights and is_peft:
        # Return a curried stream function that loads the right adapter
        # according to the model_name available in this context.  This ensures
        # the right weights are available.
        @torch.inference_mode()
        def generate_stream_peft(
            model,
            tokenizer,
            params: Dict,
            device: str,
            context_len: int,
            stream_interval: int = 2,
            judge_sent_end: bool = False,
        ):
            model.set_adapter(model_path)
            for x in generate_stream(
                model,
                tokenizer,
                params,
                device,
                context_len,
                stream_interval,
                judge_sent_end,
            ):
                yield x

        return generate_stream_peft
    else:
        return generate_stream


def add_model_args(parser):
    parser.add_argument(
        "--model-path",
        type=str,
        default="lmsys/vicuna-7b-v1.3",
        help="The path to the weights. This can be a local folder or a Hugging Face repo ID.",
    )
    parser.add_argument(
        "--revision",
        type=str,
        default="main",
        help="Hugging Face Hub model revision identifier",
    )
    parser.add_argument(
        "--device",
        type=str,
        choices=["cpu", "cuda", "mps", "xpu"],
        default="cuda",
        help="The device type",
    )
    parser.add_argument(
        "--gpus",
        type=str,
        default=None,
        help="A single GPU like 1 or multiple GPUs like 0,2",
    )
    parser.add_argument("--num-gpus", type=int, default=1)
    parser.add_argument(
        "--max-gpu-memory",
        type=str,
        help="The maximum memory per GPU for storing model weights. Use a string like '13Gib'",
    )
    parser.add_argument(
        "--load-8bit", action="store_true", help="Use 8-bit quantization"
    )
    parser.add_argument(
        "--cpu-offloading",
        action="store_true",
        help="Only when using 8-bit quantization: Offload excess weights to the CPU that don't fit on the GPU",
    )
    parser.add_argument(
        "--gptq-ckpt",
        type=str,
        default=None,
        help="Used for GPTQ. The path to the local GPTQ checkpoint.",
    )
    parser.add_argument(
        "--gptq-wbits",
        type=int,
        default=16,
        choices=[2, 3, 4, 8, 16],
        help="Used for GPTQ. #bits to use for quantization",
    )
    parser.add_argument(
        "--gptq-groupsize",
        type=int,
        default=-1,
        help="Used for GPTQ. Groupsize to use for quantization; default uses full row.",
    )
    parser.add_argument(
        "--gptq-act-order",
        action="store_true",
        help="Used for GPTQ. Whether to apply the activation order GPTQ heuristic",
    )
    parser.add_argument(
        "--awq-ckpt",
        type=str,
        default=None,
        help="Used for AWQ. Load quantized model. The path to the local AWQ checkpoint.",
    )
    parser.add_argument(
        "--awq-wbits",
        type=int,
        default=16,
        choices=[4, 16],
        help="Used for AWQ. #bits to use for AWQ quantization",
    )
    parser.add_argument(
        "--awq-groupsize",
        type=int,
        default=-1,
        help="Used for AWQ. Groupsize to use for AWQ quantization; default uses full row.",
    )


def remove_parent_directory_name(model_path):
    """Remove parent directory name."""
    if model_path[-1] == "/":
        model_path = model_path[:-1]
    return model_path.split("/")[-1]


peft_model_cache = {}


class PeftModelAdapter:
    """Loads any "peft" model and it's base model."""

    def match(self, model_path: str):
        """Accepts any model path with "peft" in the name"""
        if os.path.exists(os.path.join(model_path, "adapter_config.json")):
            return True
        return "peft" in model_path.lower()

    def load_model(self, model_path: str, from_pretrained_kwargs: dict):
        """Loads the base model then the (peft) adapter weights"""
        from peft import PeftConfig, PeftModel

        config = PeftConfig.from_pretrained(model_path)
        base_model_path = config.base_model_name_or_path
        if "peft" in base_model_path:
            raise ValueError(
                f"PeftModelAdapter cannot load a base model with 'peft' in the name: {config.base_model_name_or_path}"
            )

        # Basic proof of concept for loading peft adapters that share the base
        # weights.  This is pretty messy because Peft re-writes the underlying
        # base model and internally stores a map of adapter layers.
        # So, to make this work we:
        #  1. Cache the first peft model loaded for a given base models.
        #  2. Call `load_model` for any follow on Peft models.
        #  3. Make sure we load the adapters by the model_path.  Why? This is
        #  what's accessible during inference time.
        #  4. In get_generate_stream_function, make sure we load the right
        #  adapter before doing inference.  This *should* be safe when calls
        #  are blocked the same semaphore.
        if peft_share_base_weights:
            if base_model_path in peft_model_cache:
                model, tokenizer = peft_model_cache[base_model_path]
                # Super important: make sure we use model_path as the
                # `adapter_name`.
                model.load_adapter(model_path, adapter_name=model_path)
            else:
                base_adapter = get_model_adapter(base_model_path)
                base_model, tokenizer = base_adapter.load_model(
                    base_model_path, from_pretrained_kwargs
                )
                # Super important: make sure we use model_path as the
                # `adapter_name`.
                model = PeftModel.from_pretrained(
                    base_model, model_path, adapter_name=model_path
                )
                peft_model_cache[base_model_path] = (model, tokenizer)
            return model, tokenizer

        # In the normal case, load up the base model weights again.
        base_adapter = get_model_adapter(base_model_path)
        base_model, tokenizer = base_adapter.load_model(
            base_model_path, from_pretrained_kwargs
        )
        model = PeftModel.from_pretrained(base_model, model_path)
        return model, tokenizer

    def get_default_conv_template(self, model_path: str) -> Conversation:
        """Uses the conv template of the base model"""
        from peft import PeftConfig, PeftModel

        config = PeftConfig.from_pretrained(model_path)
        if "peft" in config.base_model_name_or_path:
            raise ValueError(
                f"PeftModelAdapter cannot load a base model with 'peft' in the name: {config.base_model_name_or_path}"
            )
        base_model_path = config.base_model_name_or_path
        base_adapter = get_model_adapter(base_model_path)
        return base_adapter.get_default_conv_template(config.base_model_name_or_path)


class VicunaAdapter(BaseModelAdapter):
    "Model adapater for Vicuna models (e.g., lmsys/vicuna-7b-v1.3)" ""

    use_fast_tokenizer = False

    def match(self, model_path: str):
        return "vicuna" in model_path.lower()

    def load_model(self, model_path: str, from_pretrained_kwargs: dict):
        revision = from_pretrained_kwargs.get("revision", "main")
        tokenizer = AutoTokenizer.from_pretrained(
            model_path, use_fast=self.use_fast_tokenizer, revision=revision
        )
        model = AutoModelForCausalLM.from_pretrained(
            model_path,
            low_cpu_mem_usage=True,
            **from_pretrained_kwargs,
        )
        self.raise_warning_for_old_weights(model)
        return model, tokenizer

    def get_default_conv_template(self, model_path: str) -> Conversation:
        if "v0" in remove_parent_directory_name(model_path):
            return get_conv_template("one_shot")
        return get_conv_template("vicuna_v1.1")

    def raise_warning_for_old_weights(self, model):
        if isinstance(model, LlamaForCausalLM) and model.model.vocab_size > 32000:
            warnings.warn(
                "\nYou are probably using the old Vicuna-v0 model, "
                "which will generate unexpected results with the "
                "current fastchat.\nYou can try one of the following methods:\n"
                "1. Upgrade your weights to the new Vicuna-v1.3: https://github.com/lm-sys/FastChat#vicuna-weights.\n"
                "2. Use the old conversation template by `python3 -m fastchat.serve.cli --model-path /path/to/vicuna-v0 --conv-template conv_one_shot`\n"
                "3. Downgrade fschat to fschat==0.1.10 (Not recommonded).\n"
            )


class AiroborosAdapter(BaseModelAdapter):
    """The model adapter for jondurbin/airoboros-*"""

    def match(self, model_path: str):
        return "airoboros" in model_path.lower()

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("airoboros_v1")

    def load_model(self, model_path: str, from_pretrained_kwargs: dict):
        if "mpt" not in model_path.lower():
            return super().load_model(model_path, from_pretrained_kwargs)
        model = AutoModelForCausalLM.from_pretrained(
            model_path,
            low_cpu_mem_usage=True,
            trust_remote_code=True,
            max_seq_len=8192,
            **from_pretrained_kwargs,
        )
        tokenizer = AutoTokenizer.from_pretrained(
            model_path, trust_remote_code=True, use_fast=True
        )
        return model, tokenizer


class LongChatAdapter(BaseModelAdapter):
    "Model adapater for LongChat models (e.g., lmsys/longchat-7b-16k)."

    use_fast_tokenizer = False

    def match(self, model_path: str):
        return "longchat" in model_path.lower()

    def load_model(self, model_path: str, from_pretrained_kwargs: dict):
        revision = from_pretrained_kwargs.get("revision", "main")

        # Apply monkey patch, TODO(Dacheng): Add flash attention support
        config = AutoConfig.from_pretrained(model_path, revision=revision)
        replace_llama_with_condense(config.rope_scaling["factor"])

        tokenizer = AutoTokenizer.from_pretrained(
            model_path, use_fast=self.use_fast_tokenizer, revision=revision
        )
        model = AutoModelForCausalLM.from_pretrained(
            model_path,
            low_cpu_mem_usage=True,
            **from_pretrained_kwargs,
        )
        return model, tokenizer

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("vicuna_v1.1")


class CodeT5pAdapter(BaseModelAdapter):
    """The model adapter for Salesforce/codet5p-6b"""

    def match(self, model_path: str):
        return "codet5p" in model_path.lower()

    def load_model(self, model_path: str, from_pretrained_kwargs: dict):
        revision = from_pretrained_kwargs.get("revision", "main")
        tokenizer = AutoTokenizer.from_pretrained(model_path, revision=revision)
        model = AutoModelForSeq2SeqLM.from_pretrained(
            model_path,
            low_cpu_mem_usage=True,
            trust_remote_code=True,
            **from_pretrained_kwargs,
        )
        return model, tokenizer


class T5Adapter(BaseModelAdapter):
    """The model adapter for lmsys/fastchat-t5-3b-v1.0"""

    def match(self, model_path: str):
        return "t5" in model_path.lower()

    def load_model(self, model_path: str, from_pretrained_kwargs: dict):
        revision = from_pretrained_kwargs.get("revision", "main")
        tokenizer = T5Tokenizer.from_pretrained(model_path, revision=revision)
        model = AutoModelForSeq2SeqLM.from_pretrained(
            model_path, low_cpu_mem_usage=True, **from_pretrained_kwargs
        )
        return model, tokenizer


class KoalaAdapter(BaseModelAdapter):
    """The model adapter for koala"""

    use_fast_tokenizer = False

    def match(self, model_path: str):
        return "koala" in model_path.lower()

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("koala_v1")


class AlpacaAdapter(BaseModelAdapter):
    """The model adapter for alpaca"""

    use_fast_tokenizer = False

    def match(self, model_path: str):
        return "alpaca" in model_path.lower()

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("alpaca")


class ChatGLMAdapter(BaseModelAdapter):
    """The model adapter for THUDM/chatglm-6b, THUDM/chatglm2-6b"""

    def match(self, model_path: str):
        return "chatglm" in model_path.lower()

    def load_model(self, model_path: str, from_pretrained_kwargs: dict):
        revision = from_pretrained_kwargs.get("revision", "main")
        tokenizer = AutoTokenizer.from_pretrained(
            model_path, trust_remote_code=True, revision=revision
        )
        model = AutoModel.from_pretrained(
            model_path, trust_remote_code=True, **from_pretrained_kwargs
        )
        return model, tokenizer

    def get_default_conv_template(self, model_path: str) -> Conversation:
        model_path = model_path.lower()
        if "chatglm2" in model_path.lower():
            return get_conv_template("chatglm2")
        return get_conv_template("chatglm")


class DollyV2Adapter(BaseModelAdapter):
    """The model adapter for databricks/dolly-v2-12b"""

    def match(self, model_path: str):
        return "dolly-v2" in model_path.lower()

    def load_model(self, model_path: str, from_pretrained_kwargs: dict):
        revision = from_pretrained_kwargs.get("revision", "main")
        tokenizer = AutoTokenizer.from_pretrained(model_path, revision=revision)
        model = AutoModelForCausalLM.from_pretrained(
            model_path,
            low_cpu_mem_usage=True,
            **from_pretrained_kwargs,
        )
        # 50277 means "### End"
        tokenizer.eos_token_id = 50277
        model.config.eos_token_id = tokenizer.eos_token_id
        model.config.pad_token_id = tokenizer.pad_token_id
        return model, tokenizer

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("dolly_v2")


class OasstPythiaAdapter(BaseModelAdapter):
    """The model adapter for OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5"""

    def match(self, model_path: str):
        model_path = model_path.lower()
        return "oasst" in model_path and "pythia" in model_path

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("oasst_pythia")

    def load_model(self, model_path: str, from_pretrained_kwargs: dict):
        model, tokenizer = super().load_model(model_path, from_pretrained_kwargs)
        model.config.eos_token_id = tokenizer.eos_token_id
        model.config.pad_token_id = tokenizer.pad_token_id
        return model, tokenizer


class OasstLLaMAAdapter(BaseModelAdapter):
    """The model adapter for OpenAssistant/oasst-sft-7-llama-30b"""

    use_fast_tokenizer = False

    def match(self, model_path: str):
        model_path = model_path.lower()
        if "openassistant-sft-7-llama-30b-hf" in model_path:
            return True
        return "oasst" in model_path and "pythia" not in model_path

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("oasst_llama")


class PythiaAdapter(BaseModelAdapter):
    """The model adapter for any EleutherAI/pythia model"""

    def match(self, model_path: str):
        return "pythia" in model_path.lower()

    def load_model(self, model_path: str, from_pretrained_kwargs: dict):
        model, tokenizer = super().load_model(model_path, from_pretrained_kwargs)
        model.config.eos_token_id = tokenizer.eos_token_id
        model.config.pad_token_id = tokenizer.pad_token_id
        return model, tokenizer


class StableLMAdapter(BaseModelAdapter):
    """The model adapter for StabilityAI/stablelm-tuned-alpha-7b"""

    def match(self, model_path: str):
        return "stablelm" in model_path.lower()

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("stablelm")


class MPTAdapter(BaseModelAdapter):
    """The model adapter for MPT series (mosaicml/mpt-7b-chat, mosaicml/mpt-30b-chat)"""

    def match(self, model_path: str):
        model_path = model_path.lower()
        return "mpt" in model_path and not "airoboros" in model_path

    def load_model(self, model_path: str, from_pretrained_kwargs: dict):
        revision = from_pretrained_kwargs.get("revision", "main")
        model = AutoModelForCausalLM.from_pretrained(
            model_path,
            low_cpu_mem_usage=True,
            trust_remote_code=True,
            max_seq_len=8192,
            **from_pretrained_kwargs,
        )
        tokenizer = AutoTokenizer.from_pretrained(
            model_path, trust_remote_code=True, revision=revision
        )
        model.config.eos_token_id = tokenizer.eos_token_id
        model.config.pad_token_id = tokenizer.pad_token_id
        return model, tokenizer

    def get_default_conv_template(self, model_path: str) -> Conversation:
        model_path = model_path.lower()
        if "mpt-7b-chat" in model_path:
            return get_conv_template("mpt-7b-chat")
        elif "mpt-30b-chat" in model_path:
            return get_conv_template("mpt-30b-chat")
        elif "mpt-30b-instruct" in model_path:
            return get_conv_template("mpt-30b-instruct")
        else:
            print(
                "Warning: Loading base MPT model with `zero_shot` conversation configuration.  "
                "If this is not desired, inspect model configurations and names."
            )
            return get_conv_template("zero_shot")


class BaizeAdapter(BaseModelAdapter):
    """The model adapter for project-baize/baize-v2-7b"""

    use_fast_tokenizer = False

    def match(self, model_path: str):
        return "baize" in model_path.lower()

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("baize")


class RwkvAdapter(BaseModelAdapter):
    """The model adapter for BlinkDL/RWKV-4-Raven"""

    def match(self, model_path: str):
        return "rwkv-4" in model_path.lower()

    def load_model(self, model_path: str, from_pretrained_kwargs: dict):
        from fastchat.model.rwkv_model import RwkvModel

        model = RwkvModel(model_path)
        revision = from_pretrained_kwargs.get("revision", "main")
        tokenizer = AutoTokenizer.from_pretrained(
            "EleutherAI/pythia-160m", revision=revision
        )
        return model, tokenizer

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("rwkv")


class OpenBuddyAdapter(BaseModelAdapter):
    """The model adapter for OpenBuddy/openbuddy-7b-v1.1-bf16-enc"""

    use_fast_tokenizer = False

    def match(self, model_path: str):
        return "openbuddy" in model_path.lower()

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("openbuddy")


class PhoenixAdapter(BaseModelAdapter):
    """The model adapter for FreedomIntelligence/phoenix-inst-chat-7b"""

    def match(self, model_path: str):
        return "phoenix" in model_path.lower()

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("phoenix")


class ChatGPTAdapter(BaseModelAdapter):
    """The model adapter for ChatGPT"""

    def match(self, model_path: str):
        return model_path in ("gpt-3.5-turbo", "gpt-4")

    def load_model(self, model_path: str, from_pretrained_kwargs: dict):
        raise NotImplementedError()

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("chatgpt")


class ClaudeAdapter(BaseModelAdapter):
    """The model adapter for Claude"""

    def match(self, model_path: str):
        return model_path in ["claude-2", "claude-instant-1"]

    def load_model(self, model_path: str, from_pretrained_kwargs: dict):
        raise NotImplementedError()

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("claude")


class BardAdapter(BaseModelAdapter):
    """The model adapter for Bard"""

    def match(self, model_path: str):
        return model_path == "bard"

    def load_model(self, model_path: str, from_pretrained_kwargs: dict):
        raise NotImplementedError()

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("bard")


class PaLM2Adapter(BaseModelAdapter):
    """The model adapter for PaLM2"""

    def match(self, model_path: str):
        return model_path == "palm-2"

    def load_model(self, model_path: str, from_pretrained_kwargs: dict):
        raise NotImplementedError()

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("bard")


class BiLLaAdapter(BaseModelAdapter):
    """The model adapter for Neutralzz/BiLLa-7B-SFT"""

    def match(self, model_path: str):
        return "billa" in model_path.lower()

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("billa")


class RedPajamaINCITEAdapter(BaseModelAdapter):
    """The model adapter for togethercomputer/RedPajama-INCITE-7B-Chat"""

    def match(self, model_path: str):
        return "redpajama-incite" in model_path.lower()

    def load_model(self, model_path: str, from_pretrained_kwargs: dict):
        revision = from_pretrained_kwargs.get("revision", "main")
        tokenizer = AutoTokenizer.from_pretrained(model_path, revision=revision)
        model = AutoModelForCausalLM.from_pretrained(
            model_path,
            low_cpu_mem_usage=True,
            **from_pretrained_kwargs,
        )
        return model, tokenizer

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("redpajama-incite")


class H2OGPTAdapter(BaseModelAdapter):
    """The model adapter for h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b"""

    use_fast_tokenizer = False

    def match(self, model_path: str):
        return "h2ogpt" in model_path.lower()

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("h2ogpt")


class RobinAdapter(BaseModelAdapter):
    """The model adapter for LMFlow/Full-Robin-7b-v2"""

    use_fast_tokenizer = False

    def match(self, model_path: str):
        return "robin" in model_path.lower()

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("Robin")


class SnoozyAdapter(BaseModelAdapter):
    """The model adapter for nomic-ai/gpt4all-13b-snoozy"""

    use_fast_tokenizer = False

    def match(self, model_path: str):
        model_path = model_path.lower()
        return "gpt4all" in model_path and "snoozy" in model_path

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("snoozy")


class WizardLMAdapter(BaseModelAdapter):
    """The model adapter for WizardLM/WizardLM-13B-V1.0"""

    use_fast_tokenizer = False

    def match(self, model_path: str):
        return "wizardlm" in model_path.lower()

    def get_default_conv_template(self, model_path: str) -> Conversation:
        model_path = model_path.lower()
        if "13b" in model_path or "30b" in model_path:
            return get_conv_template("vicuna_v1.1")
        else:
            # TODO: use the recommended template for 7B
            # (https://huggingface.co/WizardLM/WizardLM-13B-V1.0)
            return get_conv_template("one_shot")


class ManticoreAdapter(BaseModelAdapter):
    """The model adapter for openaccess-ai-collective/manticore-13b-chat-pyg"""

    use_fast_tokenizer = False

    def match(self, model_path: str):
        return "manticore" in model_path.lower()

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("manticore")


class GuanacoAdapter(BaseModelAdapter):
    """The model adapter for timdettmers/guanaco-33b-merged"""

    use_fast_tokenizer = False

    def match(self, model_path: str):
        return "guanaco" in model_path.lower()

    def load_model(self, model_path: str, from_pretrained_kwargs: dict):
        revision = from_pretrained_kwargs.get("revision", "main")
        tokenizer = AutoTokenizer.from_pretrained(
            model_path, use_fast=self.use_fast_tokenizer, revision=revision
        )
        model = AutoModelForCausalLM.from_pretrained(
            model_path, low_cpu_mem_usage=True, **from_pretrained_kwargs
        )
        # Fix a bug in tokenizer config
        tokenizer.eos_token_id = model.config.eos_token_id
        return model, tokenizer

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("zero_shot")


class ChangGPTAdapter(BaseModelAdapter):
    """The model adapter for lcw99/polyglot-ko-12.8b-chang-instruct-chat"""

    def match(self, model_path: str):
        model_path = model_path.lower()
        return "polyglot" in model_path and "chang" in model_path

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("polyglot_changgpt")


class CamelAdapter(BaseModelAdapter):
    """The model adapter for camel-ai/CAMEL-13B-Combined-Data"""

    use_fast_tokenizer = False

    def match(self, model_path: str):
        return "camel" in model_path.lower()

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("vicuna_v1.1")


class TuluAdapter(BaseModelAdapter):
    """The model adapter for allenai/tulu-30b"""

    use_fast_tokenizer = False

    def match(self, model_path: str):
        return "tulu" in model_path.lower()

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("tulu")


class FalconAdapter(BaseModelAdapter):
    """The model adapter for tiiuae/falcon-40b"""

    def match(self, model_path: str):
        return "falcon" in model_path.lower()

    def load_model(self, model_path: str, from_pretrained_kwargs: dict):
        revision = from_pretrained_kwargs.get("revision", "main")
        # Strongly suggest using bf16, which is recommended by the author of Falcon
        tokenizer = AutoTokenizer.from_pretrained(model_path, revision=revision)
        model = AutoModelForCausalLM.from_pretrained(
            model_path,
            low_cpu_mem_usage=True,
            trust_remote_code=True,
            **from_pretrained_kwargs,
        )
        # In Falcon tokenizer config and special config there is not any pad token
        # Setting `pad_token_id` to 9, which corresponds to special token '>>SUFFIX<<'
        tokenizer.pad_token_id = 9
        return model, tokenizer

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("falcon")


class TigerBotAdapter(BaseModelAdapter):
    """The model adapter for TigerResearch/tigerbot-7b-sft"""

    def match(self, model_path: str):
        return "tigerbot" in model_path.lower()

    def load_model(self, model_path: str, from_pretrained_kwargs: dict):
        revision = from_pretrained_kwargs.get("revision", "main")
        tokenizer = AutoTokenizer.from_pretrained(
            model_path,
            trust_remote_code=True,
            revision=revision,
        )
        model = AutoModelForCausalLM.from_pretrained(
            model_path,
            trust_remote_code=True,
            low_cpu_mem_usage=True,
            **from_pretrained_kwargs,
        )
        return model, tokenizer

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("tigerbot")


class BaichuanAdapter(BaseModelAdapter):
    """The model adapter for Baichuan models (e.g., baichuan-inc/Baichuan-7B)"""

    def match(self, model_path: str):
        return "baichuan" in model_path.lower()

    def load_model(self, model_path: str, from_pretrained_kwargs: dict):
        revision = from_pretrained_kwargs.get("revision", "main")
        tokenizer = AutoTokenizer.from_pretrained(
            model_path, trust_remote_code=True, revision=revision
        )
        model = AutoModelForCausalLM.from_pretrained(
            model_path,
            trust_remote_code=True,
            low_cpu_mem_usage=True,
            **from_pretrained_kwargs,
        )
        return model, tokenizer

    def get_default_conv_template(self, model_path: str) -> Conversation:
        # for Baichuan-13B-Chat
        if "chat" in model_path.lower():
            return get_conv_template("baichuan-chat")
        return get_conv_template("zero_shot")


class XGenAdapter(BaseModelAdapter):
    """The model adapter for Salesforce/xgen-7b"""

    def match(self, model_path: str):
        return "xgen" in model_path.lower()

    def load_model(self, model_path: str, from_pretrained_kwargs: dict):
        revision = from_pretrained_kwargs.get("revision", "main")
        model = AutoModelForCausalLM.from_pretrained(
            model_path,
            low_cpu_mem_usage=True,
            trust_remote_code=True,
            **from_pretrained_kwargs,
        )
        tokenizer = AutoTokenizer.from_pretrained(
            model_path, trust_remote_code=True, revision=revision
        )
        model.config.eos_token_id = 50256
        return model, tokenizer

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("xgen")


class NousHermesAdapter(BaseModelAdapter):
    """The model adapter for NousResearch/Nous-Hermes-13b"""

    use_fast_tokenizer = False

    def match(self, model_path: str):
        return "nous-hermes" in model_path.lower()

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("alpaca")


class InternLMChatAdapter(BaseModelAdapter):
    """The model adapter for internlm/internlm-chat-7b"""

    def match(self, model_path: str):
        return "internlm" in model_path.lower()

    def load_model(self, model_path: str, from_pretrained_kwargs: dict):
        revision = from_pretrained_kwargs.get("revision", "main")
        model = AutoModelForCausalLM.from_pretrained(
            model_path,
            low_cpu_mem_usage=True,
            trust_remote_code=True,
            **from_pretrained_kwargs,
        )
        model = model.eval()
        if "8k" in model_path.lower():
            model.config.max_sequence_length = 8192
        tokenizer = AutoTokenizer.from_pretrained(
            model_path, trust_remote_code=True, revision=revision
        )
        return model, tokenizer

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("internlm-chat")


class StarChatAdapter(BaseModelAdapter):
    """The model adapter for HuggingFaceH4/starchat-beta"""

    def match(self, model_path: str):
        return "starchat" in model_path.lower()

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("starchat")


class Llama2Adapter(BaseModelAdapter):
    """The model adapter for llama-2"""

    def match(self, model_path: str):
        return "llama-2" in model_path.lower()

    def load_model(self, model_path: str, from_pretrained_kwargs: dict):
        model, tokenizer = super().load_model(model_path, from_pretrained_kwargs)
        model.config.eos_token_id = tokenizer.eos_token_id
        model.config.pad_token_id = tokenizer.pad_token_id
        return model, tokenizer

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("llama-2")


class CuteGPTAdapter(BaseModelAdapter):
    """The model adapter for llama-2"""

    def match(self, model_path: str):
        return "cutegpt" in model_path.lower()

    def load_model(self, model_path: str, from_pretrained_kwargs: dict):
        tokenizer = LlamaTokenizer.from_pretrained(model_path)
        model = AutoModelForCausalLM.from_pretrained(
            model_path, low_cpu_mem_usage=True, **from_pretrained_kwargs
        )
        tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids("<end>")
        model.config.eos_token_id = tokenizer.eos_token_id
        model.config.pad_token_id = tokenizer.eos_token_id
        return model, tokenizer

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("cutegpt")


class OpenOrcaAdapter(BaseModelAdapter):
    "Model adapater for Open-Orca models (e.g., Open-Orca/OpenOrcaxOpenChat-Preview2-13B)" ""

    use_fast_tokenizer = False

    def match(self, model_path: str):
        return "openorca" in model_path.lower()

    def load_model(self, model_path: str, from_pretrained_kwargs: dict):
        revision = from_pretrained_kwargs.get("revision", "main")
        tokenizer = AutoTokenizer.from_pretrained(
            model_path, use_fast=self.use_fast_tokenizer, revision=revision
        )
        model = AutoModelForCausalLM.from_pretrained(
            model_path,
            low_cpu_mem_usage=True,
            **from_pretrained_kwargs,
        ).eval()
        return model, tokenizer

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("open-orca")


class WizardCoderAdapter(BaseModelAdapter):
    """The model adapter for WizardCoder"""

    use_fast_tokenizer = False

    def match(self, model_path: str):
        return "wizardcoder" in model_path.lower()

    def get_default_conv_template(self, model_path: str) -> Conversation:
        # Same as Alpaca, see :
        # https://github.com/nlpxucan/WizardLM/blob/main/WizardCoder/src/inference_wizardcoder.py#L60
        return get_conv_template("alpaca")


class QwenChatAdapter(BaseModelAdapter):
    """The model adapter for Qwen/Qwen-7B-Chat
    To run this model, you need to ensure additional flash attention installation:
    ``` bash
    git clone https://github.com/Dao-AILab/flash-attention
    cd flash-attention && pip install .
    pip install csrc/layer_norm
    pip install csrc/rotary
    ```

    Since from 2.0, the following change happened
    - `flash_attn_unpadded_func` -> `flash_attn_varlen_func`
    - `flash_attn_unpadded_qkvpacked_func` -> `flash_attn_varlen_qkvpacked_func`
    - `flash_attn_unpadded_kvpacked_func` -> `flash_attn_varlen_kvpacked_func`
    You may need to revise the code in: https://huggingface.co/Qwen/Qwen-7B-Chat/blob/main/modeling_qwen.py#L69
    to from flash_attn.flash_attn_interface import flash_attn_varlen_func as flash_attn_unpadded_func
    """

    def match(self, model_path: str):
        return "qwen" in model_path.lower()

    def load_model(self, model_path: str, from_pretrained_kwargs: dict):
        from transformers.generation import GenerationConfig

        revision = from_pretrained_kwargs.get("revision", "main")
        config = AutoConfig.from_pretrained(
            model_path,
            trust_remote_code=True,
        )
        config.use_flash_attn = False
        config.fp16 = True
        generation_config = GenerationConfig.from_pretrained(
            model_path, trust_remote_code=True
        )
        model = AutoModelForCausalLM.from_pretrained(
            model_path,
            config=config,
            low_cpu_mem_usage=True,
            trust_remote_code=True,
            **from_pretrained_kwargs,
        ).eval()
        if hasattr(model.config, "use_dynamic_ntk") and model.config.use_dynamic_ntk:
            model.config.max_sequence_length = 16384
        tokenizer = AutoTokenizer.from_pretrained(
            model_path, trust_remote_code=True, revision=revision
        )
        tokenizer.eos_token_id = config.eos_token_id
        tokenizer.bos_token_id = config.bos_token_id
        tokenizer.pad_token_id = generation_config.pad_token_id
        model.config.eos_token_id = tokenizer.eos_token_id
        model.config.bos_token_id = tokenizer.bos_token_id
        model.config.pad_token_id = tokenizer.pad_token_id

        return model, tokenizer

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("qwen-7b-chat")


class BGEAdapter(BaseModelAdapter):
    """The model adapter for BGE"""

    use_fast_tokenizer = False

    def match(self, model_path: str):
        return "bge" in model_path.lower()

    def load_model(self, model_path: str, from_pretrained_kwargs: dict):
        revision = from_pretrained_kwargs.get("revision", "main")
        model = AutoModel.from_pretrained(
            model_path,
            **from_pretrained_kwargs,
        )
        tokenizer = AutoTokenizer.from_pretrained(
            model_path, trust_remote_code=True, revision=revision
        )
        return model, tokenizer

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("one_shot")


class AquilaChatAdapter(BaseModelAdapter):
    """The model adapter for BAAI/AquilaChat-7B"""

    def match(self, model_path: str):
        return "aquila" in model_path.lower()

    def load_model(self, model_path: str, from_pretrained_kwargs: dict):
        revision = from_pretrained_kwargs.get("revision", "main")
        model = AutoModelForCausalLM.from_pretrained(
            model_path,
            low_cpu_mem_usage=True,
            trust_remote_code=True,
            **from_pretrained_kwargs,
        )
        model = model.eval()
        tokenizer = AutoTokenizer.from_pretrained(
            model_path, trust_remote_code=True, revision=revision
        )
        return model, tokenizer

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("aquila-chat")


class Lamma2ChineseAdapter(BaseModelAdapter):
    """The model adapter for FlagAlpha/LLama2-Chinese sft"""

    def match(self, model_path: str):
        return "llama2-chinese" in model_path.lower()

    def load_model(self, model_path: str, from_pretrained_kwargs: dict):
        revision = from_pretrained_kwargs.get("revision", "main")
        tokenizer = AutoTokenizer.from_pretrained(
            model_path,
            trust_remote_code=True,
            revision=revision,
        )
        model = AutoModelForCausalLM.from_pretrained(
            model_path,
            trust_remote_code=True,
            low_cpu_mem_usage=True,
            **from_pretrained_kwargs,
        )
        return model, tokenizer

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("llama2-chinese")


class VigogneInstructAdapter(BaseModelAdapter):
    """The model adapter for Vigogne-Instruct"""

    use_fast_tokenizer = False

    def match(self, model_path: str):
        return "vigogne" in model_path.lower() and "instruct" in model_path.lower()

    def load_model(self, model_path: str, from_pretrained_kwargs: dict):
        revision = from_pretrained_kwargs.get("revision", "main")
        tokenizer = AutoTokenizer.from_pretrained(
            model_path,
            use_fast=self.use_fast_tokenizer,
            trust_remote_code=True,
            revision=revision,
        )
        model = AutoModelForCausalLM.from_pretrained(
            model_path,
            trust_remote_code=True,
            low_cpu_mem_usage=True,
            **from_pretrained_kwargs,
        ).eval()
        return model, tokenizer

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("alpaca")


class VigogneChatAdapter(BaseModelAdapter):
    """The model adapter for Vigogne-Chat"""

    use_fast_tokenizer = False

    def match(self, model_path: str):
        return "vigogne" in model_path.lower() and "chat" in model_path.lower()

    def load_model(self, model_path: str, from_pretrained_kwargs: dict):
        revision = from_pretrained_kwargs.get("revision", "main")
        tokenizer = AutoTokenizer.from_pretrained(
            model_path,
            use_fast=self.use_fast_tokenizer,
            trust_remote_code=True,
            revision=revision,
        )
        model = AutoModelForCausalLM.from_pretrained(
            model_path,
            trust_remote_code=True,
            low_cpu_mem_usage=True,
            **from_pretrained_kwargs,
        ).eval()
        return model, tokenizer

    def get_default_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("vigogne-chat")


# Note: the registration order matters.
# The one registered earlier has a higher matching priority.
register_model_adapter(PeftModelAdapter)
register_model_adapter(VicunaAdapter)
register_model_adapter(AiroborosAdapter)
register_model_adapter(LongChatAdapter)
register_model_adapter(CodeT5pAdapter)
register_model_adapter(T5Adapter)
register_model_adapter(KoalaAdapter)
register_model_adapter(AlpacaAdapter)
register_model_adapter(ChatGLMAdapter)
register_model_adapter(DollyV2Adapter)
register_model_adapter(OasstPythiaAdapter)
register_model_adapter(OasstLLaMAAdapter)
register_model_adapter(StableLMAdapter)
register_model_adapter(BaizeAdapter)
register_model_adapter(RwkvAdapter)
register_model_adapter(OpenBuddyAdapter)
register_model_adapter(PhoenixAdapter)
register_model_adapter(BardAdapter)
register_model_adapter(PaLM2Adapter)
register_model_adapter(ChatGPTAdapter)
register_model_adapter(ClaudeAdapter)
register_model_adapter(MPTAdapter)
register_model_adapter(BiLLaAdapter)
register_model_adapter(RedPajamaINCITEAdapter)
register_model_adapter(H2OGPTAdapter)
register_model_adapter(RobinAdapter)
register_model_adapter(SnoozyAdapter)
register_model_adapter(WizardLMAdapter)
register_model_adapter(ManticoreAdapter)
register_model_adapter(GuanacoAdapter)
register_model_adapter(CamelAdapter)
register_model_adapter(ChangGPTAdapter)
register_model_adapter(TuluAdapter)
register_model_adapter(FalconAdapter)
register_model_adapter(TigerBotAdapter)
register_model_adapter(BaichuanAdapter)
register_model_adapter(XGenAdapter)
register_model_adapter(NousHermesAdapter)
register_model_adapter(PythiaAdapter)
register_model_adapter(InternLMChatAdapter)
register_model_adapter(StarChatAdapter)
register_model_adapter(Llama2Adapter)
register_model_adapter(CuteGPTAdapter)
register_model_adapter(OpenOrcaAdapter)
register_model_adapter(WizardCoderAdapter)
register_model_adapter(QwenChatAdapter)
register_model_adapter(AquilaChatAdapter)
register_model_adapter(BGEAdapter)
register_model_adapter(Lamma2ChineseAdapter)
register_model_adapter(VigogneInstructAdapter)
register_model_adapter(VigogneChatAdapter)

# After all adapters, try the default base adapter.
register_model_adapter(BaseModelAdapter)
