
'''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


UpperCAmelCase__ :			Dict											=		logging.get_logger(__name__)






class  A  (						SCREAMING_SNAKE_CASE__     ):





	def __init__(       self      :							Optional[int]   ,				__magic_name__      :							Union[List[ControlNetModel], Tuple[ControlNetModel]]				):


				"""simple docstring"""

				super().__init__()
				lowerCAmelCase__										=				nn.ModuleList(__magic_name__				)





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


				"""simple docstring"""

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

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

				return down_block_res_samples, mid_block_res_sample





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[Any]   ,				__magic_name__      :							Union[str, os.PathLike]   ,				__magic_name__      :							bool = True   ,				__magic_name__      :							Callable = None   ,				__magic_name__      :							bool = False   ,				__magic_name__      :							Optional[str] = None   ,				):


				"""simple docstring"""

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

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







	@classmethod
	def 				__SCREAMING_SNAKE_CASE	(       cls      :							str   ,				__magic_name__      :							Optional[Union[str, os.PathLike]]   ,				**__magic_name__      :							Tuple				):


				"""simple docstring"""

				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(__magic_name__				):
							lowerCAmelCase__										=				ControlNetModel.from_pretrained(__magic_name__   ,				**__magic_name__				)
							controlnets.append(__magic_name__				)

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

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

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

				return cls(__magic_name__				)









"""simple docstring"""


from __future__ import annotations
class     _UpperCamelCase    :
							'''simple docstring'''







							def __init__(	self       ,  __a=None      ):
										__lowerCAmelCase							       =     data
										__lowerCAmelCase							       =     None



							def __repr__(	self      ):
										__lowerCAmelCase							       =     []
										__lowerCAmelCase							       =     self
										while temp:
													string_rep.append(f"{temp.data}"      )
													__lowerCAmelCase							       =     temp.next
										return "->".join(__a      )


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			if not elements_list:
						raise Exception("The Elements List is empty" )

			__lowerCAmelCase							       =     __lowerCAmelCase							       =     Node(elements_list[0] )
			for i in range(1       ,					len(_UpperCamelCase ) ):
						__lowerCAmelCase							       =     Node(elements_list[i] )
						__lowerCAmelCase							       =     current.next
			return head


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			if head_node is not None and isinstance(_UpperCamelCase       ,					_UpperCamelCase ):
						print_reverse(head_node.next )
						print(head_node.data )


def _lowerCamelCase      (						):

			'''simple docstring'''




			from doctest import testmod

			testmod()

			__lowerCAmelCase							       =     make_linked_list([14, 52, 14, 12, 43] )
			print("Linked List:" )
			print(_UpperCamelCase )
			print("Elements in Reverse:" )
			print_reverse(_UpperCamelCase )


if __name__ == "__main__":
	main()




