


"""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


lowerCamelCase_   :   int									=  logging.get_logger(__name__)






class       __A				(       _SCREAMING_SNAKE_CASE			):






 """simple docstring"""




 def __init__(		self ,    __A						)					->     str:
     super().__init__()
     a 		=nn.ModuleList(__A						)


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A = None ,    __A = None ,    __A = None ,    __A = None ,    __A = False ,    __A = True ,    )					->     Union[ControlNetOutput, Tuple]:
     for i, (image, scale, controlnet) in enumerate(zip(__A ,    __A ,    self.nets						)						):
         a		,      a 		=controlnet(
             __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    )

         # merge samples
         if i == 0:
             a		,      a 		=down_samples, mid_sample
         else:
             a 		=[
                 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       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A = True ,    __A = None ,    __A = False ,    __A = None ,    )					->     List[Any]:
     a 		=0
     a 		=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
         a 		=model_path_to_save + f'''_{idx}'''





 @classmethod
 def       SCREAMING_SNAKE_CASE							(		cls ,    __A ,    **__A						)					->     int:
     a 		=0
     a 		=[]

     # 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 		=pretrained_model_path
     while os.path.isdir(__A						):
         a 		=ControlNetModel.from_pretrained(__A ,    **__A						)
         controlnets.append(__A						)

         idx += 1
         a 		=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   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						return [ord(SCREAMING_SNAKE_CASE__       ) - 96 for elem in plain]


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						return "".join(chr(elem + 96       ) for elem in encoded       )


def   __SCREAMING_SNAKE_CASE					():
						snake_case_   =	encode(input('''-> '''       ).strip().lower()       )
						print('''Encoded: '''  ,		SCREAMING_SNAKE_CASE__       )
						print('''Decoded:'''  ,		decode(SCREAMING_SNAKE_CASE__       )       )


if __name__ == "__main__":
					main()