from typing import Optional, TypeVar, Union, overload

import torch
import torch.nn.functional as F
from torch import Tensor, device, dtype, nn

import bitsandbytes as bnb
from bitsandbytes.optim import GlobalOptimManager
from bitsandbytes.utils import OutlierTracer, find_outlier_dims

T = TypeVar("T", bound="torch.nn.Module")


class LinearFP8Mixed(nn.Linear):
    def __init__(self, input_features, output_features, bias=True):
        super().__init__(input_features, output_features, bias)
        self.bw_code = None
        self.fw_code = None
        array = [4096, 2048, 1024, 512, 256, 128, 64, 0]
        for i, k in enumerate(array):
            if input_features > array[i + 1]:
                self.bsz = k
                break
        for i, k in enumerate(array):
            if output_features > array[i + 1]:
                self.bsz2 = k
                break

    def forward(self, x: torch.Tensor):
        if self.fw_code is None:
            self.bw_code = bnb.functional.create_fp8_map(True, 5, 2, 8).to(x.device)
            self.fw_code = bnb.functional.create_fp8_map(True, 4, 3, 8).to(x.device)

        out = bnb.research.matmul_fp8_mixed(x, self.weight.t(), fw_code=self.fw_code, bw_code=self.bw_code, bsz=self.bsz, bsz2=self.bsz2)
        if self.bias is not None:
            out += self.bias

        return out

class LinearFP8Global(nn.Linear):
    def __init__(self, input_features, output_features, bias=True):
        super().__init__(input_features, output_features, bias)
        self.bw_code = None
        self.fw_code = None
        array = [4096, 2048, 1024, 512, 256, 128, 64, 0]
        for i, k in enumerate(array):
            if input_features > array[i + 1]:
                self.bsz = k
                break
        for i, k in enumerate(array):
            if output_features > array[i + 1]:
                self.bsz2 = k
                break

    def forward(self, x: torch.Tensor):
        if self.fw_code is None:
            self.bw_code = bnb.functional.create_fp8_map(True, 5, 2, 8).to(x.device)
            self.fw_code = bnb.functional.create_fp8_map(True, 4, 3, 8).to(x.device)

        out = bnb.matmul_fp8_global(x, self.weight.t(), fw_code=self.fw_code, bw_code=self.bw_code, bsz=self.bsz, bsz2=self.bsz2)
        if self.bias is not None:
            out += self.bias

        return out
