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"""Model weight loading configuration."""

import enum
import json
from dataclasses import dataclass, field

from tokenspeed.runtime.utils import get_colorful_logger

logger = get_colorful_logger(__name__)


class LoadFormat(str, enum.Enum):
    AUTO = "auto"
    PT = "pt"
    SAFETENSORS = "safetensors"
    NPCACHE = "npcache"
    DUMMY = "dummy"
    SHARDED_STATE = "sharded_state"
    MISTRAL = "mistral"
    EXTENSIBLE = "extensible"


@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.
    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).
    """

    load_format: str | LoadFormat = LoadFormat.AUTO
    download_dir: str | None = None
    model_loader_extra_config: str | dict | None = field(default_factory=dict)
    ignore_patterns: list[str] | str | None = None
    decryption_key_file: str | None = None
    weight_loader_prefetch_checkpoints: bool = False
    weight_loader_prefetch_num_threads: int = 4

    ext_yaml: str | None = None

    def __post_init__(self) -> None:
        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)