import torch
import os
import requests
from tqdm import tqdm
from diffusers import DDPMScheduler, EulerDiscreteScheduler
from typing import Any, Optional, Union

# def make_1step_sched(pretrained_path, step=4):
#     noise_scheduler_1step = EulerDiscreteScheduler.from_pretrained(pretrained_path, subfolder="scheduler")
#     noise_scheduler_1step.set_timesteps(step, device="cuda")
#     noise_scheduler_1step.alphas_cumprod = noise_scheduler_1step.alphas_cumprod.cuda()
    # return noise_scheduler_1step


def make_1step_sched(pretrained_path):
    noise_scheduler_1step = DDPMScheduler.from_pretrained(pretrained_path, subfolder="scheduler")
    noise_scheduler_1step.set_timesteps(1, device="cuda")
    noise_scheduler_1step.alphas_cumprod = noise_scheduler_1step.alphas_cumprod.cuda()
    return noise_scheduler_1step


def my_lora_fwd(self, x: torch.Tensor, *args: Any, **kwargs: Any) -> torch.Tensor:
    self._check_forward_args(x, *args, **kwargs)
    adapter_names = kwargs.pop("adapter_names", None)

    if self.disable_adapters:
        if self.merged:
            self.unmerge()
        result = self.base_layer(x, *args, **kwargs)
    elif adapter_names is not None:
        result = self._mixed_batch_forward(x, *args, adapter_names=adapter_names, **kwargs)
    elif self.merged:
        result = self.base_layer(x, *args, **kwargs)
    else:
        result = self.base_layer(x, *args, **kwargs)
        torch_result_dtype = result.dtype
        for active_adapter in self.active_adapters:
            if active_adapter not in self.lora_A.keys():
                continue
            lora_A = self.lora_A[active_adapter]
            lora_B = self.lora_B[active_adapter]
            dropout = self.lora_dropout[active_adapter]
            scaling = self.scaling[active_adapter]
            x = x.to(lora_A.weight.dtype)

            if not self.use_dora[active_adapter]:
                _tmp = lora_A(dropout(x))
                if isinstance(lora_A, torch.nn.Conv2d):
                    _tmp = torch.einsum('...khw,...kr->...rhw', _tmp, self.de_mod)
                elif isinstance(lora_A, torch.nn.Linear):
                    _tmp = torch.einsum('...lk,...kr->...lr', _tmp, self.de_mod)
                else:
                    raise NotImplementedError('only conv and linear are supported yet.')

                result = result + lora_B(_tmp) * scaling
            else:
                x = dropout(x)
                result = result + self._apply_dora(x, lora_A, lora_B, scaling, active_adapter)

        result = result.to(torch_result_dtype)

    return result

def download_url(url, outf):
    if not os.path.exists(outf):
        print(f"Downloading checkpoint to {outf}")
        response = requests.get(url, stream=True)
        total_size_in_bytes = int(response.headers.get('content-length', 0))
        block_size = 1024  # 1 Kibibyte
        progress_bar = tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True)
        with open(outf, 'wb') as file:
            for data in response.iter_content(block_size):
                progress_bar.update(len(data))
                file.write(data)
        progress_bar.close()
        if total_size_in_bytes != 0 and progress_bar.n != total_size_in_bytes:
            print("ERROR, something went wrong")
        print(f"Downloaded successfully to {outf}")
    else:
        print(f"Skipping download, {outf} already exists")