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from __future__ import annotations

import logging
from typing import Any

import torch

from tokenspeed.runtime.layers.quantization.base_config import QuantizationConfig
from tokenspeed.runtime.utils import log_info_on_rank0

ACTIVATION_SCHEMES = ["static", "dynamic"]

logger = logging.getLogger(__name__)


class Fp8Config(QuantizationConfig):
    """Config class for FP8."""

    def __init__(
        self,
        is_checkpoint_fp8_serialized: bool = False,
        activation_scheme: str = "dynamic",
        ignored_layers: list[str] | None = None,
        weight_block_size: list[int] = None,
        scale_fmt: str | None = None,
    ) -> None:
        super().__init__(ignored_layers=ignored_layers)
        self.is_checkpoint_fp8_serialized = is_checkpoint_fp8_serialized
        if is_checkpoint_fp8_serialized:
            log_info_on_rank0(logger, "Detected fp8 checkpoint.")
        if activation_scheme not in ACTIVATION_SCHEMES:
            raise ValueError(f"Unsupported activation scheme {activation_scheme}")
        self.activation_scheme = activation_scheme
        if weight_block_size is not None:
            if not is_checkpoint_fp8_serialized:
                raise ValueError(
                    "The block-wise quantization only supports fp8-serialized checkpoint for now."
                )
            if len(weight_block_size) != 2:
                raise ValueError(
                    f"The quantization block size of weight must have 2 dimensions, but got {len(weight_block_size)} dimensions."
                )
            if activation_scheme != "dynamic":
                raise ValueError(
                    f"The block-wise quantization only supports dynamic activation scheme for now, but got {activation_scheme} activation scheme."
                )
        self.weight_block_size = weight_block_size
        self.scale_fmt = scale_fmt.lower() if scale_fmt is not None else None

    @classmethod
    def get_name(cls) -> str:
        return "fp8"

    @classmethod
    def get_supported_act_dtypes(cls) -> list[torch.dtype]:
        return [torch.bfloat16, torch.half]

    @classmethod
    def get_min_capability(cls) -> int:
        return 90

    @classmethod
    def get_config_filenames(cls) -> list[str]:
        return []

    @classmethod
    def from_config(cls, config: dict[str, Any]) -> Fp8Config:
        quant_method = cls.get_from_keys(config, ["quant_method"])
        is_checkpoint_fp8_serialized = "fp8" in quant_method
        activation_scheme = cls.get_from_keys(config, ["activation_scheme"])
        ignored_layers = cls.get_from_keys_or(config, ["ignored_layers"], None)
        weight_block_size = cls.get_from_keys_or(config, ["weight_block_size"], None)
        scale_fmt = cls.get_from_keys_or(config, ["scale_fmt"], None)
        return cls(
            is_checkpoint_fp8_serialized=is_checkpoint_fp8_serialized,
            activation_scheme=activation_scheme,
            ignored_layers=ignored_layers,
            weight_block_size=weight_block_size,
            scale_fmt=scale_fmt,
        )

    def get_scaled_act_names(self) -> list[str]:
        return []