





import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union


@dataclass
class     __magic_name__     :





     """simple docstring"""



     __UpperCamelCase    			=   None
     __UpperCamelCase    			=   False
     __UpperCamelCase    			=   False
     __UpperCamelCase    			=   False
     __UpperCamelCase    			=   None
     __UpperCamelCase    			=   None
     __UpperCamelCase    			=   False
     __UpperCamelCase    			=   False
     __UpperCamelCase    			=   False
     __UpperCamelCase    			=   True
     __UpperCamelCase    			=   None
     __UpperCamelCase    			=   1
     __UpperCamelCase    			=   None
     __UpperCamelCase    			=   False
     __UpperCamelCase    			=   None
     __UpperCamelCase    			=   None



     def   SCREAMING_SNAKE_CASE						(							self				:int     ):





        '''simple docstring'''




        return self.__class__(**{k: copy.deepcopy(snake_case     ) for k, v in self.__dict__.items()}     )


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


_lowerCamelCase       :				List[str]       =			logging.get_logger(__name__)




class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	def __init__(  self	:    Dict			,  lowercase	:    Union[List[ControlNetModel], Tuple[ControlNetModel]]	):


				'''simple docstring'''


				super().__init__()
				_snake_case		=			nn.ModuleList(lowercase	)



	def  A							(  self	:    Optional[int]			,  lowercase	:    torch.FloatTensor			,  lowercase	:    Union[torch.Tensor, float, int]			,  lowercase	:    torch.Tensor			,  lowercase	:    List[torch.tensor]			,  lowercase	:    List[float]			,  lowercase	:    Optional[torch.Tensor] = None			,  lowercase	:    Optional[torch.Tensor] = None			,  lowercase	:    Optional[torch.Tensor] = None			,  lowercase	:    Optional[Dict[str, Any]] = None			,  lowercase	:    bool = False			,  lowercase	:    bool = True			,  ):


				'''simple docstring'''


				for i, (image, scale, controlnet) in enumerate(zip(lowercase			,  lowercase			,  self.nets	)	):
							_snake_case	,					_snake_case		=			controlnet(
							    lowercase			,  lowercase			,  lowercase			,  lowercase			,  lowercase			,  lowercase			,  lowercase			,  lowercase			,  lowercase			,  lowercase			,  lowercase			,  )

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

				return down_block_res_samples, mid_block_res_sample



	def  A							(  self	:    Dict			,  lowercase	:    Union[str, os.PathLike]			,  lowercase	:    bool = True			,  lowercase	:    Callable = None			,  lowercase	:    bool = False			,  lowercase	:    Optional[str] = None			,  ):


				'''simple docstring'''


				_snake_case		=			0
				_snake_case		=			save_directory
				for controlnet in self.nets:
							controlnet.save_pretrained(
							    lowercase			,  is_main_process=lowercase			,  save_function=lowercase			,  safe_serialization=lowercase			,  variant=lowercase			,  )

							idx += 1
							_snake_case		=			model_path_to_save + f'''_{idx}'''





	@classmethod
	def  A							(  cls	:    Any			,  lowercase	:    Optional[Union[str, os.PathLike]]			,  **lowercase	:    List[str]	):


				'''simple docstring'''


				_snake_case		=			0
				_snake_case		=			[]

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

							idx += 1
							_snake_case		=			pretrained_model_path + f'''_{idx}'''

				logger.info(f'''{len(lowercase	)} controlnets loaded from {pretrained_model_path}.'''	)

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

				return cls(lowercase	)