






'''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


_A     :      List[Any]   					=logging.get_logger(__name__)

class _lowercase     (    _lowercase		):




							def __init__(							self:    Dict      ,							UpperCamelCase__:    Union[List[ControlNetModel], Tuple[ControlNetModel]]					):
														super().__init__()
														lowerCamelCase__ :     Optional[int]	  =	nn.ModuleList(UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    Any      ,							UpperCamelCase__:    torch.FloatTensor      ,							UpperCamelCase__:    Union[torch.Tensor, float, int]      ,							UpperCamelCase__:    torch.Tensor      ,							UpperCamelCase__:    List[torch.tensor]      ,							UpperCamelCase__:    List[float]      ,							UpperCamelCase__:    Optional[torch.Tensor] = None      ,							UpperCamelCase__:    Optional[torch.Tensor] = None      ,							UpperCamelCase__:    Optional[torch.Tensor] = None      ,							UpperCamelCase__:    Optional[Dict[str, Any]] = None      ,							UpperCamelCase__:    bool = False      ,							UpperCamelCase__:    bool = True      ,							):
														for i, (image, scale, controlnet) in enumerate(zip(UpperCamelCase__      ,							UpperCamelCase__      ,							self.nets					)					):
																					lowerCamelCase__ :     Optional[Any]	  =	controlnet(
																					    UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							)

																					# merge samples
																					if i == 0:
																												lowerCamelCase__ :     int	  =	down_samples, mid_sample
																					else:
																												lowerCamelCase__ :     Tuple	  =	[
																												    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:    int      ,							UpperCamelCase__:    Union[str, os.PathLike]      ,							UpperCamelCase__:    bool = True      ,							UpperCamelCase__:    Callable = None      ,							UpperCamelCase__:    bool = False      ,							UpperCamelCase__:    Optional[str] = None      ,							):
														lowerCamelCase__ :     Optional[Any]	  =	0
														lowerCamelCase__ :     Any	  =	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
																					lowerCamelCase__ :     int	  =	model_path_to_save + F'''_{idx}'''




							@classmethod
							def 		lowerCamelCase_    (							cls:    Tuple      ,							UpperCamelCase__:    Optional[Union[str, os.PathLike]]      ,							**UpperCamelCase__:    int					):
														lowerCamelCase__ :     str	  =	0
														lowerCamelCase__ :     List[str]	  =	[]

														# 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__ :     Optional[int]	  =	pretrained_model_path
														while os.path.isdir(UpperCamelCase__					):
																					lowerCamelCase__ :     List[str]	  =	ControlNetModel.from_pretrained(UpperCamelCase__      ,							**UpperCamelCase__					)
																					controlnets.append(UpperCamelCase__					)

																					idx += 1
																					lowerCamelCase__ :     Optional[int]	  =	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__					)









'''simple docstring'''







from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

_A     :      Optional[Any]   					=typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
_A     :      Optional[int]   					=typing.Union[np.floataa, int, float]  # noqa: UP007





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	VectorOut:
							return np.sqrt(np.sum((np.asarray(UpperCamelCase				) - np.asarray(UpperCamelCase				)) ** 2				)				)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	VectorOut:
							return sum((va - va) ** 2 for va, va in zip(UpperCamelCase    ,      UpperCamelCase				)				) ** (1 / 2)


if __name__ == "__main__":





			def 			SCREAMING_SNAKE_CASE_	()				->	None:
										from timeit import timeit

										print("""Without Numpy"""				)
										print(
										    timeit(
										        """euclidean_distance_no_np([1, 2, 3], [4, 5, 6])"""    ,      number=10000    ,      globals=globals()    ,      )				)
										print("""With Numpy"""				)
										print(
										    timeit(
										        """euclidean_distance([1, 2, 3], [4, 5, 6])"""    ,      number=10000    ,      globals=globals()    ,      )				)



			benchmark()

