





'''simple docstring'''






import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


A__    :       Dict              =logging.get_logger(__name__)




class 			UpperCAmelCase					(      snake_case_       ):




      def __init__(      self      :			Dict      ,      __snake_case      :			Union[List[ControlNetModel], Tuple[ControlNetModel]]  )    ->				int:
            super().__init__()
            _lowerCAmelCase												=							nn.ModuleList(__snake_case  )




      def     lowercase__   (      self      :			Dict      ,      __snake_case      :			torch.FloatTensor      ,      __snake_case      :			Union[torch.Tensor, float, int]      ,      __snake_case      :			torch.Tensor      ,      __snake_case      :			List[torch.tensor]      ,      __snake_case      :			List[float]      ,      __snake_case      :			Optional[torch.Tensor] = None      ,      __snake_case      :			Optional[torch.Tensor] = None      ,      __snake_case      :			Optional[torch.Tensor] = None      ,      __snake_case      :			Optional[Dict[str, Any]] = None      ,      __snake_case      :			bool = False      ,      __snake_case      :			bool = True      ,      )    ->				Union[ControlNetOutput, Tuple]:
            for i, (image, scale, controlnet) in enumerate(zip(__snake_case      ,      __snake_case      ,      self.nets  )  ):
                  _lowerCAmelCase					,							_lowerCAmelCase												=							controlnet(
                      __snake_case      ,      __snake_case      ,      __snake_case      ,      __snake_case      ,      __snake_case      ,      __snake_case      ,      __snake_case      ,      __snake_case      ,      __snake_case      ,      __snake_case      ,      __snake_case      ,      )

                  # merge samples
                  if i == 0:
                        _lowerCAmelCase					,							_lowerCAmelCase												=							down_samples, mid_sample
                  else:
                        _lowerCAmelCase												=							[
                            samples_prev + samples_curr
                            for samples_prev, samples_curr in zip(__snake_case      ,      __snake_case  )
                        ]
                        mid_block_res_sample += mid_sample

            return down_block_res_samples, mid_block_res_sample




      def     lowercase__   (      self      :			List[Any]      ,      __snake_case      :			Union[str, os.PathLike]      ,      __snake_case      :			bool = True      ,      __snake_case      :			Callable = None      ,      __snake_case      :			bool = False      ,      __snake_case      :			Optional[str] = None      ,      )    ->				List[Any]:
            _lowerCAmelCase												=							0
            _lowerCAmelCase												=							save_directory
            for controlnet in self.nets:
                  controlnet.save_pretrained(
                      __snake_case      ,      is_main_process=__snake_case      ,      save_function=__snake_case      ,      safe_serialization=__snake_case      ,      variant=__snake_case      ,      )

                  idx += 1
                  _lowerCAmelCase												=							model_path_to_save + f"_{idx}"




      @classmethod
      def     lowercase__   (      cls      :			Optional[Any]      ,      __snake_case      :			Optional[Union[str, os.PathLike]]      ,      **__snake_case      :			Dict  )    ->				List[Any]:
            _lowerCAmelCase												=							0
            _lowerCAmelCase												=							[]

            # load controlnet and append to list until no controlnet directory exists anymore
            # first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
            # second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
            _lowerCAmelCase												=							pretrained_model_path
            while os.path.isdir(__snake_case  ):
                  _lowerCAmelCase												=							ControlNetModel.from_pretrained(__snake_case      ,      **__snake_case  )
                  controlnets.append(__snake_case  )

                  idx += 1
                  _lowerCAmelCase												=							pretrained_model_path + f"_{idx}"

            logger.info(f"{len(__snake_case  )} controlnets loaded from {pretrained_model_path}."  )

            if len(__snake_case  ) == 0:
                  raise ValueError(
                      f"No ControlNets found under {os.path.dirname(__snake_case  )}. Expected at least {pretrained_model_path + '_0'}."  )

            return cls(__snake_case  )













'''simple docstring'''






def        UpperCamelCase__				(		lowerCAmelCase = 4_00_00_00  ):





      """simple docstring"""


      _lowerCAmelCase												=							[]
      _lowerCAmelCase					,							_lowerCAmelCase												=							0, 1
      while b <= n:
            if b % 2 == 0:
                  even_fibs.append(lowerCAmelCase  )
            _lowerCAmelCase					,							_lowerCAmelCase												=							b, a + b
      return sum(lowerCAmelCase  )


if __name__ == "__main__":
      print(F"""{solution() = }""")






