"""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       =  logging.get_logger(__name__)







class      UpperCAmelCase_       (						__SCREAMING_SNAKE_CASE):






							def __init__(		self :      Union[str, Any]     ,						__UpperCamelCase :      Optional[int]  )       ->       int:
												super().__init__()
												_UpperCamelCase      						=	nn.ModuleList(_a  )






							def       _UpperCamelCase			(		self :      Tuple     ,						__UpperCamelCase :      Optional[int]     ,						__UpperCamelCase :      List[str]     ,						__UpperCamelCase :      Optional[int]     ,						__UpperCamelCase :      Optional[int]     ,						__UpperCamelCase :      Dict     ,						__UpperCamelCase :      List[Any] = None     ,						__UpperCamelCase :      int = None     ,						__UpperCamelCase :      int = None     ,						__UpperCamelCase :      Tuple = None     ,						__UpperCamelCase :      Any = False     ,						__UpperCamelCase :      str = True     ,						)       ->       List[str]:
												for i, (image, scale, controlnet) in enumerate(zip(_a     ,						_a     ,						self.nets  )  ):
																	_UpperCamelCase						,							_UpperCamelCase      						=	controlnet(
																	    _a     ,						_a     ,						_a     ,						_a     ,						_a     ,						_a     ,						_a     ,						_a     ,						_a     ,						_a     ,						_a     ,						)

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

												return down_block_res_samples, mid_block_res_sample






							def       _UpperCamelCase			(		self :      Tuple     ,						__UpperCamelCase :      Dict     ,						__UpperCamelCase :      Dict = True     ,						__UpperCamelCase :      Dict = None     ,						__UpperCamelCase :      int = False     ,						__UpperCamelCase :      Optional[Any] = None     ,						)       ->       Dict:
												_UpperCamelCase      						=	0
												_UpperCamelCase      						=	save_directory
												for controlnet in self.nets:
																	controlnet.save_pretrained(
																	    _a     ,						is_main_process=_a     ,						save_function=_a     ,						safe_serialization=_a     ,						variant=_a     ,						)

																	idx += 1
																	_UpperCamelCase      						=	model_path_to_save + F'''_{idx}'''






							@classmethod
							def       _UpperCamelCase			(		cls :      Optional[int]     ,						__UpperCamelCase :      str     ,						**__UpperCamelCase :      int  )       ->       str:
												_UpperCamelCase      						=	0
												_UpperCamelCase      						=	[]

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

																	idx += 1
																	_UpperCamelCase      						=	pretrained_model_path + F'''_{idx}'''

												logger.info(F'''{len(_a  )} controlnets loaded from {pretrained_model_path}.'''  )

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

												return cls(_a  )




"""simple docstring"""




import numpy as np

def        lowercase							(		lowerCAmelCase__ :     np.ndarray    ,					lowerCAmelCase__ :     float			) ->      np.ndarray:
		return np.where(vector > 0    ,					lowerCAmelCase__    ,					(alpha * (np.exp(lowerCAmelCase__			) - 1))			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()






