




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_   		=     logging.get_logger(__name__)

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      def __init__(     self					: List[str] ,       snake_case					: Union[List[ControlNetModel], Tuple[ControlNetModel]]				):
          super().__init__()
          __UpperCamelCase  		=						nn.ModuleList(snake_case				)







      def       snake_case	(     self					: Union[str, Any] ,       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 ,       ):
          for i, (image, scale, controlnet) in enumerate(zip(snake_case ,       snake_case ,       self.nets				)				):
              __UpperCamelCase		,      __UpperCamelCase  		=						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:
                  __UpperCamelCase		,      __UpperCamelCase  		=						down_samples, mid_sample
              else:
                  __UpperCamelCase  		=						[
                      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       snake_case	(     self					: Tuple ,       snake_case					: Union[str, os.PathLike] ,       snake_case					: bool = True ,       snake_case					: Callable = None ,       snake_case					: bool = False ,       snake_case					: Optional[str] = None ,       ):
          __UpperCamelCase  		=						0
          __UpperCamelCase  		=						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
              __UpperCamelCase  		=						model_path_to_save + F"_{idx}"







      @classmethod
      def       snake_case	(     cls					: str ,       snake_case					: Optional[Union[str, os.PathLike]] ,       **snake_case					: str				):
          __UpperCamelCase  		=						0
          __UpperCamelCase  		=						[]

          # 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`, ...
          __UpperCamelCase  		=						pretrained_model_path
          while os.path.isdir(snake_case				):
              __UpperCamelCase  		=						ControlNetModel.from_pretrained(snake_case ,       **snake_case				)
              controlnets.append(snake_case				)

              idx += 1
              __UpperCamelCase  		=						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				)












from abc import ABC, abstractmethod
from argparse import ArgumentParser

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      @staticmethod
      @abstractmethod
      def       snake_case	(     snake_case					: ArgumentParser				):
          raise NotImplementedError()







      @abstractmethod
      def       snake_case	(     self					: Optional[Any]				):
          raise NotImplementedError()






