
from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_lowerCamelCase       :				Any       =			10






def        a_		(       __lowercase   :	int       ,					__lowercase   :	int       ,					__lowercase   :	list[int]       ,					__lowercase   :	int ) ->  int:
			for i in range(__lowercase       ,					__lowercase ):
						if array[i] == target:
									return i
			return -1






def        a_		(       __lowercase   :	list[int]       ,					__lowercase   :	int ) ->  int:
			_snake_case		=			0
			_snake_case		=			len(__lowercase )
			while left <= right:
						if right - left < precision:
									return lin_search(__lowercase       ,					__lowercase       ,					__lowercase       ,					__lowercase )

						_snake_case		=			(left + right) // 3 + 1
						_snake_case		=			2 * (left + right) // 3 + 1

						if array[one_third] == target:
									return one_third
						elif array[two_third] == target:
									return two_third

						elif target < array[one_third]:
									_snake_case		=			one_third - 1
						elif array[two_third] < target:
									_snake_case		=			two_third + 1

						else:
									_snake_case		=			one_third + 1
									_snake_case		=			two_third - 1
			else:
						return -1






def        a_		(       __lowercase   :	int       ,					__lowercase   :	int       ,					__lowercase   :	list[int]       ,					__lowercase   :	int ) ->  int:
			if left < right:
						if right - left < precision:
									return lin_search(__lowercase       ,					__lowercase       ,					__lowercase       ,					__lowercase )
						_snake_case		=			(left + right) // 3 + 1
						_snake_case		=			2 * (left + right) // 3 + 1

						if array[one_third] == target:
									return one_third
						elif array[two_third] == target:
									return two_third

						elif target < array[one_third]:
									return rec_ternary_search(__lowercase       ,					one_third - 1       ,					__lowercase       ,					__lowercase )
						elif array[two_third] < target:
									return rec_ternary_search(two_third + 1       ,					__lowercase       ,					__lowercase       ,					__lowercase )
						else:
									return rec_ternary_search(one_third + 1       ,					two_third - 1       ,					__lowercase       ,					__lowercase )
			else:
						return -1


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			_lowerCamelCase       :				List[Any]       =			input('''Enter numbers separated by comma:\n''').strip()
			_lowerCamelCase       :				str       =			[int(item.strip()) for item in user_input.split(''',''')]
			assert collection == sorted(collection), F"List must be ordered.\n{collection}."
			_lowerCamelCase       :				Union[str, Any]       =			int(input('''Enter the number to be found in the list:\n''').strip())
			_lowerCamelCase       :				Tuple       =			ite_ternary_search(collection, target)
			_lowerCamelCase       :				List[Any]       =			rec_ternary_search(0, len(collection) - 1, collection, target)
			if resulta != -1:
						print(F'Iterative search: {target} found at positions: {resulta}')
						print(F'Recursive search: {target} found at positions: {resulta}')
			else:
						print('''Not found''')

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	)