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


SCREAMING_SNAKE_CASE__		:					List[str]						=					logging.get_logger(__name__)

class       snake_case					(      UpperCamelCase_						):




		def __init__(      self     :	Optional[int]     , a_     :	Union[List[ControlNetModel], Tuple[ControlNetModel]]				)->       int:




							"""simple docstring"""
							super().__init__()
							SCREAMING_SNAKE_CASE__				:				List[str]			=		nn.ModuleList(a_				)




		def 	__lowercase(      self     :	Tuple     , a_     :	torch.FloatTensor     , a_     :	Union[torch.Tensor, float, int]     , a_     :	torch.Tensor     , a_     :	List[torch.tensor]     , a_     :	List[float]     , a_     :	Optional[torch.Tensor] = None     , a_     :	Optional[torch.Tensor] = None     , a_     :	Optional[torch.Tensor] = None     , a_     :	Optional[Dict[str, Any]] = None     , a_     :	bool = False     , a_     :	bool = True     , )->       Union[ControlNetOutput, Tuple]:




							"""simple docstring"""
							for i, (image, scale, controlnet) in enumerate(zip(a_     , a_     , self.nets				)				):
												SCREAMING_SNAKE_CASE__				:				str			=		controlnet(
												    a_     , a_     , a_     , a_     , a_     , a_     , a_     , a_     , a_     , a_     , a_     , )

												# merge samples
												if i == 0:
																	SCREAMING_SNAKE_CASE__				:				Optional[int]			=		down_samples, mid_sample
												else:
																	SCREAMING_SNAKE_CASE__				:				List[str]			=		[
																	    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 	__lowercase(      self     :	Optional[Any]     , a_     :	Union[str, os.PathLike]     , a_     :	bool = True     , a_     :	Callable = None     , a_     :	bool = False     , a_     :	Optional[str] = None     , )->       Union[str, Any]:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Optional[int]			=		0
							SCREAMING_SNAKE_CASE__				:				int			=		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
												SCREAMING_SNAKE_CASE__				:				List[str]			=		model_path_to_save + F'''_{idx}'''




		@classmethod
		def 	__lowercase(      cls     :	str     , a_     :	Optional[Union[str, os.PathLike]]     , **a_     :	Optional[int]				)->       Any:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		0
							SCREAMING_SNAKE_CASE__				:				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`, ...
							SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		pretrained_model_path
							while os.path.isdir(a_				):
												SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		ControlNetModel.from_pretrained(a_     , **a_				)
												controlnets.append(a_				)

												idx += 1
												SCREAMING_SNAKE_CASE__				:				Tuple			=		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_				)




from __future__ import annotations







def     _a				(      lowercase__  :			list[int | float]     ,  lowercase__  :			int     ,  lowercase__  :			int ):







     '''simple docstring'''


     if len(lowercase__ ) == 0:
          raise ValueError('find_max() arg is an empty sequence' )
     if (
         left >= len(lowercase__ )
         or left < -len(lowercase__ )
         or right >= len(lowercase__ )
         or right < -len(lowercase__ )
     ):
          raise IndexError('list index out of range' )
     if left == right:
          return nums[left]
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		(left + right) >> 1  # the middle
     SCREAMING_SNAKE_CASE__				:				int			=		find_max(lowercase__     ,  lowercase__     ,  lowercase__ )  # find max in range[left, mid]
     SCREAMING_SNAKE_CASE__				:				Tuple			=		find_max(lowercase__     ,  mid + 1     ,  lowercase__ )  # find max in range[mid + 1, right]

     return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
      import doctest

      doctest.testmod(verbose=True)



