# Adapted from https://github.com/vllm-project/vllm/blob/v0.6.4.post1/vllm/config.py
import enum
import json
import logging
from dataclasses import dataclass, field
from typing import List, Optional, Union

from sglang.srt.utils import is_hip

logger = logging.getLogger(__name__)


class LoadFormat(str, enum.Enum):
    AUTO = "auto"
    PT = "pt"
    SAFETENSORS = "safetensors"
    NPCACHE = "npcache"
    DUMMY = "dummy"
    SHARDED_STATE = "sharded_state"
    GGUF = "gguf"
    BITSANDBYTES = "bitsandbytes"
    MISTRAL = "mistral"
    LAYERED = "layered"
    JAX = "jax"
    REMOTE = "remote"
    REMOTE_INSTANCE = "remote_instance"
    RDMA = "rdma"
    LOCAL_CACHED = "local_cached"


@dataclass
class LoadConfig:
    """
    download_dir: Directory to download and load the weights, default to the
        default cache directory of huggingface.
    load_format: The format of the model weights to load:
        "auto" will try to load the weights in the safetensors format and
            fall back to the pytorch bin format if safetensors format is
            not available.
        "pt" will load the weights in the pytorch bin format.
        "safetensors" will load the weights in the safetensors format.
        "npcache" will load the weights in pytorch format and store
            a numpy cache to speed up the loading.
        "dummy" will initialize the weights with random values, which is
            mainly for profiling.
        "bitsandbytes" will load nf4 type weights.
    ignore_patterns: The list of patterns to ignore when loading the model.
        Default to "original/**/*" to avoid repeated loading of llama's
        checkpoints.
    decryption_key_file: If set, decrypts the output files with a password read
        from this file (after PBKDF2).
    decrypt_max_concurrency: The maximum number of concurrent processes to decrypt the safetensor files. -1 means no limit.
    """

    load_format: Union[str, LoadFormat] = LoadFormat.AUTO
    download_dir: Optional[str] = None
    model_loader_extra_config: Optional[Union[str, dict]] = field(default_factory=dict)
    ignore_patterns: Optional[Union[List[str], str]] = None
    decryption_key_file: Optional[str] = None
    decrypt_max_concurrency: int = -1

    def __post_init__(self):
        model_loader_extra_config = self.model_loader_extra_config or {}
        if isinstance(model_loader_extra_config, str):
            self.model_loader_extra_config = json.loads(model_loader_extra_config)
        self._verify_load_format()

        if self.ignore_patterns is not None and len(self.ignore_patterns) > 0:
            logger.info(
                "Ignoring the following patterns when downloading weights: %s",
                self.ignore_patterns,
            )
        else:
            self.ignore_patterns = ["original/**/*"]

    def _verify_load_format(self) -> None:
        if not isinstance(self.load_format, str):
            return

        load_format = self.load_format.lower()
        self.load_format = LoadFormat(load_format)

        rocm_not_supported_load_format: List[str] = []
        if is_hip() and load_format in rocm_not_supported_load_format:
            rocm_supported_load_format = [
                f
                for f in LoadFormat.__members__
                if (f not in rocm_not_supported_load_format)
            ]
            raise ValueError(
                f"load format '{load_format}' is not supported in ROCm. "
                f"Supported load formats are "
                f"{rocm_supported_load_format}"
            )
