


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


snake_case    :   Any	    =					logging.get_logger(__name__)







class 		_snake_case  (				_snake_case							):




 def __init__(		self ,  _lowerCamelCase   ):
       super().__init__()
       a						:str										=	nn.ModuleList(_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase = None ,  _lowerCamelCase = None ,  _lowerCamelCase = None ,  _lowerCamelCase = None ,  _lowerCamelCase = False ,  _lowerCamelCase = True ,  ):
       for i, (image, scale, controlnet) in enumerate(zip(_lowerCamelCase ,  _lowerCamelCase ,  self.nets   )   ):
             a						:Any										=	controlnet(
                 _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  )

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

       return down_block_res_samples, mid_block_res_sample




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase = True ,  _lowerCamelCase = None ,  _lowerCamelCase = False ,  _lowerCamelCase = None ,  ):
       a						:Optional[Any]										=	0
       a						:Optional[int]										=	save_directory
       for controlnet in self.nets:
             controlnet.save_pretrained(
                 _lowerCamelCase ,  is_main_process=_lowerCamelCase ,  save_function=_lowerCamelCase ,  safe_serialization=_lowerCamelCase ,  variant=_lowerCamelCase ,  )

             idx += 1
             a						:Optional[Any]										=	model_path_to_save + F'''_{idx}'''




 @classmethod
 def 	SCREAMING_SNAKE_CASE__    (		cls ,  _lowerCamelCase ,  **_lowerCamelCase   ):
       a						:List[str]										=	0
       a						: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`, ...
       a						:Any										=	pretrained_model_path
       while os.path.isdir(_lowerCamelCase   ):
             a						:Dict										=	ControlNetModel.from_pretrained(_lowerCamelCase ,  **_lowerCamelCase   )
             controlnets.append(_lowerCamelCase   )

             idx += 1
             a						:List[str]										=	pretrained_model_path + F'''_{idx}'''

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

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

       return cls(_lowerCamelCase   )









def     __lowerCamelCase						(			UpperCAmelCase_						:		int						):




      """simple docstring"""





      if not isinstance(UpperCAmelCase_			,  UpperCAmelCase_						):
            raise TypeError('''Input value must be an \'int\' type'''						)

      a						:Optional[int]										=	0
      while number:
            position += 1
            number >>= 1

      return position


if __name__ == "__main__":
     import doctest

     doctest.testmod()





