# temporarily adapted from vLLM
# FIXME: in progress of refactoring the model loader
"""Utilities for selecting and loading models."""
import contextlib
import fnmatch
import hashlib
import json
import logging
import os
import tempfile
from typing import Any, Generator, Iterable, List, Optional, Tuple, Type

import filelock
import huggingface_hub.constants
import torch
from huggingface_hub import HfFileSystem, hf_hub_download, snapshot_download
from safetensors.torch import load_file, safe_open, save_file
from torch import nn
from tqdm.auto import tqdm
from transformers.utils import SAFE_WEIGHTS_INDEX_NAME
from vllm.config import LoadConfig, ModelConfig
from vllm.model_executor.layers.quantization.base_config import QuantizationConfig

from sglang.srt.layers.quantization import get_quantization_config

logger = logging.getLogger("srt.model_loader")
temp_dir = tempfile.gettempdir()


@contextlib.contextmanager
def set_default_torch_dtype(dtype: torch.dtype):
    """Sets the default torch dtype to the given dtype."""
    old_dtype = torch.get_default_dtype()
    torch.set_default_dtype(dtype)
    yield
    torch.set_default_dtype(old_dtype)


def get_model_architecture(model_config: ModelConfig) -> Tuple[Type[nn.Module], str]:
    architectures = getattr(model_config.hf_config, "architectures", [])
    # Special handling for quantized Mixtral.
    # FIXME(woosuk): This is a temporary hack.
    if (
        model_config.quantization is not None
        and model_config.quantization != "fp8"
        and "MixtralForCausalLM" in architectures
    ):
        architectures = ["QuantMixtralForCausalLM"]

    for arch in architectures:
        model_cls = ModelRegistry.load_model_cls(arch)
        if model_cls is not None:
            return (model_cls, arch)
    raise ValueError(
        f"Model architectures {architectures} are not supported for now. "
        f"Supported architectures: {ModelRegistry.get_supported_archs()}"
    )


class DisabledTqdm(tqdm):

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs, disable=True)


def get_lock(model_name_or_path: str, cache_dir: Optional[str] = None):
    lock_dir = cache_dir or temp_dir
    os.makedirs(os.path.dirname(lock_dir), exist_ok=True)
    model_name = model_name_or_path.replace("/", "-")
    hash_name = hashlib.sha256(model_name.encode()).hexdigest()
    # add hash to avoid conflict with old users' lock files
    lock_file_name = hash_name + model_name + ".lock"
    # mode 0o666 is required for the filelock to be shared across users
    lock = filelock.FileLock(os.path.join(lock_dir, lock_file_name), mode=0o666)
    return lock


def download_weights_from_hf(
    model_name_or_path: str,
    cache_dir: Optional[str],
    allow_patterns: List[str],
    revision: Optional[str] = None,
) -> str:
    """Download model weights from Hugging Face Hub.

    Args:
        model_name_or_path (str): The model name or path.
        cache_dir (Optional[str]): The cache directory to store the model
            weights. If None, will use HF defaults.
        allow_patterns (List[str]): The allowed patterns for the
            weight files. Files matched by any of the patterns will be
            downloaded.
        revision (Optional[str]): The revision of the model.

    Returns:
        str: The path to the downloaded model weights.
    """
    if not huggingface_hub.constants.HF_HUB_OFFLINE:
        # Before we download we look at that is available:
        fs = HfFileSystem()
        file_list = fs.ls(model_name_or_path, detail=False, revision=revision)

        # depending on what is available we download different things
        for pattern in allow_patterns:
            matching = fnmatch.filter(file_list, pattern)
            if len(matching) > 0:
                allow_patterns = [pattern]
                break

    logger.info("Using model weights format %s", allow_patterns)
    # Use file lock to prevent multiple processes from
    # downloading the same model weights at the same time.
    with get_lock(model_name_or_path, cache_dir):
        hf_folder = snapshot_download(
            model_name_or_path,
            allow_patterns=allow_patterns,
            cache_dir=cache_dir,
            tqdm_class=DisabledTqdm,
            revision=revision,
            local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
        )
    return hf_folder


def download_safetensors_index_file_from_hf(
    model_name_or_path: str,
    cache_dir: Optional[str],
    revision: Optional[str] = None,
) -> None:
    """Download hf safetensors index file from Hugging Face Hub.

    Args:
        model_name_or_path (str): The model name or path.
        cache_dir (Optional[str]): The cache directory to store the model
            weights. If None, will use HF defaults.
        revision (Optional[str]): The revision of the model.
    """
    # Use file lock to prevent multiple processes from
    # downloading the same model weights at the same time.
    with get_lock(model_name_or_path, cache_dir):
        try:
            # Download the safetensors index file.
            hf_hub_download(
                repo_id=model_name_or_path,
                filename=SAFE_WEIGHTS_INDEX_NAME,
                cache_dir=cache_dir,
                revision=revision,
                local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
            )
        # If file not found on remote or locally, we should not fail since
        # only some models will have SAFE_WEIGHTS_INDEX_NAME.
        except huggingface_hub.utils.EntryNotFoundError:
            logger.info("No %s found in remote.", SAFE_WEIGHTS_INDEX_NAME)
        except huggingface_hub.utils.LocalEntryNotFoundError:
            logger.info("No %s found in local cache.", SAFE_WEIGHTS_INDEX_NAME)


# For models like Mistral-7B-v0.3, there are both sharded
# safetensors files and a consolidated safetensors file.
# Passing both of these to the weight loader functionality breaks.
# So, we use the SAFE_WEIGHTS_INDEX_NAME to
# look up which safetensors files should be used.
def filter_duplicate_safetensors_files(
    hf_weights_files: List[str], hf_folder: str
) -> List[str]:
    # model.safetensors.index.json is a mapping from keys in the
    # torch state_dict to safetensors file holding that weight.
    index_file_name = os.path.join(hf_folder, SAFE_WEIGHTS_INDEX_NAME)
    if not os.path.isfile(index_file_name):
        return hf_weights_files

    # Iterate through the weight_map (weight_name: safetensors files)
    # to identify weights that we should use.
    with open(index_file_name) as index_file:
        weight_map = json.load(index_file)["weight_map"]
    weight_files_in_index = set()
    for weight_name in weight_map:
        weight_files_in_index.add(os.path.join(hf_folder, weight_map[weight_name]))
    # Filter out any fields that are not found in the index file.
    hf_weights_files = [f for f in hf_weights_files if f in weight_files_in_index]
    return hf_weights_files


def safetensors_weights_iterator(
    hf_weights_files: List[str],
) -> Generator[Tuple[str, torch.Tensor], None, None]:
    """Iterate over the weights in the model safetensor files."""
    for st_file in hf_weights_files:
        with safe_open(st_file, framework="pt") as f:
            for name in f.keys():  # noqa: SIM118
                param = f.get_tensor(name)
                yield name, param


def get_quant_config(
    model_config: ModelConfig, load_config: LoadConfig
) -> QuantizationConfig:
    quant_cls = get_quantization_config(model_config.quantization)
    # Read the quantization config from the HF model config, if available.
    hf_quant_config = getattr(model_config.hf_config, "quantization_config", None)
    if hf_quant_config is None:
        # compressed-tensors uses a compressions_config
        hf_quant_config = getattr(model_config.hf_config, "compression_config", None)
    if hf_quant_config is not None:
        return quant_cls.from_config(hf_quant_config)
    # In case of bitsandbytes/QLoRA, get quant config from the adapter model.
    if model_config.quantization == "bitsandbytes":
        if (
            not load_config.model_loader_extra_config
            or "qlora_adapter_name_or_path" not in load_config.model_loader_extra_config
        ):
            return quant_cls.from_config({"adapter_name_or_path": ""})
        model_name_or_path = load_config.model_loader_extra_config[
            "qlora_adapter_name_or_path"
        ]

    else:
        model_name_or_path = model_config.model
    is_local = os.path.isdir(model_name_or_path)
    if not is_local:
        # Download the config files.
        with get_lock(model_name_or_path, load_config.download_dir):
            hf_folder = snapshot_download(
                model_name_or_path,
                revision=model_config.revision,
                allow_patterns="*.json",
                cache_dir=load_config.download_dir,
                local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
                tqdm_class=DisabledTqdm,
            )
    else:
        hf_folder = model_name_or_path

    possible_config_filenames = quant_cls.get_config_filenames()

    # If the quantization config is not found, use the default config.
    if not possible_config_filenames:
        return quant_cls()

    config_files = glob.glob(os.path.join(hf_folder, "*.json"))

    quant_config_files = [
        f for f in config_files if any(f.endswith(x) for x in possible_config_filenames)
    ]
    if len(quant_config_files) == 0:
        raise ValueError(f"Cannot find the config file for {model_config.quantization}")
    if len(quant_config_files) > 1:
        raise ValueError(
            f"Found multiple config files for {model_config.quantization}: "
            f"{quant_config_files}"
        )

    quant_config_file = quant_config_files[0]
    with open(quant_config_file, "r") as f:
        config = json.load(f)

        if model_config.quantization == "bitsandbytes":
            config["adapter_name_or_path"] = model_name_or_path

    return quant_cls.from_config(config)