






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							:   Union[str, Any]							=  logging.get_logger(__name__)


class 					_snake_case						(			_A ):






	def __init__(		self					,UpperCamelCase       )						->    List[Any]:
					super().__init__()
					snake_case__      :Union[str, Any]         =   nn.ModuleList(UpperCamelCase       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = False					,UpperCamelCase = True					,)						->    Union[ControlNetOutput, Tuple]:
					for i, (image, scale, controlnet) in enumerate(zip(UpperCamelCase					,UpperCamelCase					,self.nets       )       ):
									snake_case__      :List[str]         =   controlnet(
									    UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,)

									# merge samples
									if i == 0:
													snake_case__      :Optional[int]         =   down_samples, mid_sample
									else:
													snake_case__      :Optional[int]         =   [
													    samples_prev + samples_curr
													    for samples_prev, samples_curr in zip(UpperCamelCase					,UpperCamelCase       )
													]
													mid_block_res_sample += mid_sample

					return down_block_res_samples, mid_block_res_sample






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase = True					,UpperCamelCase = None					,UpperCamelCase = False					,UpperCamelCase = None					,)						->    Union[str, Any]:
					snake_case__      :List[str]         =   0
					snake_case__      :Optional[int]         =   save_directory
					for controlnet in self.nets:
									controlnet.save_pretrained(
									    UpperCamelCase					,is_main_process=UpperCamelCase					,save_function=UpperCamelCase					,safe_serialization=UpperCamelCase					,variant=UpperCamelCase					,)

									idx += 1
									snake_case__      :List[Any]         =   model_path_to_save + f'_{idx}'






	@classmethod
	def    lowerCAmelCase_							(		cls					,UpperCamelCase					,**UpperCamelCase       )						->    Dict:
					snake_case__      :List[Any]         =   0
					snake_case__      :int         =   []

					# 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__      :Any         =   pretrained_model_path
					while os.path.isdir(UpperCamelCase       ):
									snake_case__      :List[str]         =   ControlNetModel.from_pretrained(UpperCamelCase					,**UpperCamelCase       )
									controlnets.append(UpperCamelCase       )

									idx += 1
									snake_case__      :Optional[Any]         =   pretrained_model_path + f'_{idx}'

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

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

					return cls(UpperCamelCase       )







from __future__ import annotations

import math





def 					lowercase_ (    __snake_case     :							int	,	__snake_case     :							int	,	__snake_case     :							bool	,	__snake_case     :							list[int]	,	__snake_case     :							float      )     -> int:





				'''simple docstring'''


				if depth < 0:
								raise ValueError("Depth cannot be less than 0"      )

				if len(__snake_case      ) == 0:
								raise ValueError("Scores cannot be empty"      )

				if depth == height:
								return scores[node_index]

				if is_max:
								return max(
								    minimax(depth + 1	,	node_index * 2	,	__snake_case	,	__snake_case	,	__snake_case      )	,	minimax(depth + 1	,	node_index * 2 + 1	,	__snake_case	,	__snake_case	,	__snake_case      )	,	)

				return min(
				    minimax(depth + 1	,	node_index * 2	,	__snake_case	,	__snake_case	,	__snake_case      )	,	minimax(depth + 1	,	node_index * 2 + 1	,	__snake_case	,	__snake_case	,	__snake_case      )	,	)





def 					lowercase_ (    )     -> None:





				'''simple docstring'''


				snake_case__      :List[Any]         =   [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
				snake_case__      :int         =   math.log(len(__snake_case      )	,	2      )
				print("Optimal value : "	,	end=""      )
				print(minimax(0	,	0	,	__snake_case	,	__snake_case	,	__snake_case      )      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()