

"""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


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



class 					a						(       _UpperCAmelCase				):



    """simple docstring"""



    def __init__(     self: List[Any]    ,						UpperCamelCase: List[Any]       ):






         """simple docstring"""


         super().__init__()
         A__										=  nn.ModuleList(lowercase__       )

    def 				UpperCamelCase	(     self: Union[str, Any]    ,						UpperCamelCase: Optional[Any]    ,						UpperCamelCase: Dict    ,						UpperCamelCase: str    ,						UpperCamelCase: Dict    ,						UpperCamelCase: Any    ,						UpperCamelCase: Any = None    ,						UpperCamelCase: Optional[int] = None    ,						UpperCamelCase: str = None    ,						UpperCamelCase: str = None    ,						UpperCamelCase: Optional[int] = False    ,						UpperCamelCase: List[Any] = True    ,						):






         """simple docstring"""


         for i, (image, scale, controlnet) in enumerate(zip(lowercase__    ,						lowercase__    ,						self.nets       )       ):
              A__										=  controlnet(
                  lowercase__    ,						lowercase__    ,						lowercase__    ,						lowercase__    ,						lowercase__    ,						lowercase__    ,						lowercase__    ,						lowercase__    ,						lowercase__    ,						lowercase__    ,						lowercase__    ,						)

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

         return down_block_res_samples, mid_block_res_sample

    def 				UpperCamelCase	(     self: Tuple    ,						UpperCamelCase: List[str]    ,						UpperCamelCase: Any = True    ,						UpperCamelCase: int = None    ,						UpperCamelCase: Optional[Any] = False    ,						UpperCamelCase: List[Any] = None    ,						):






         """simple docstring"""


         A__										=  0
         A__										=  save_directory
         for controlnet in self.nets:
              controlnet.save_pretrained(
                  lowercase__    ,						is_main_process=lowercase__    ,						save_function=lowercase__    ,						safe_serialization=lowercase__    ,						variant=lowercase__    ,						)

              idx += 1
              A__										=  model_path_to_save + f"""_{idx}"""





    @classmethod
    def 				UpperCamelCase	(     cls: Union[str, Any]    ,						UpperCamelCase: str    ,						**UpperCamelCase: Optional[int]       ):






         """simple docstring"""


         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(lowercase__       ):
              A__										=  ControlNetModel.from_pretrained(lowercase__    ,						**lowercase__       )
              controlnets.append(lowercase__       )

              idx += 1
              A__										=  pretrained_model_path + f"""_{idx}"""

         logger.info(f"""{len(lowercase__       )} controlnets loaded from {pretrained_model_path}."""       )

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

         return cls(lowercase__       )









"""simple docstring"""



def  _snake_case     (    UpperCAmelCase_  :					Optional[Any]					,   UpperCAmelCase_  :					Optional[Any]			):
     A__										=  [0 for i in range(r + 1			)]
     # nc0 = 1
     A__										=  1
     for i in range(1					,   n + 1			):
          # to compute current row from previous row.
          A__										=  min(UpperCAmelCase_					,   UpperCAmelCase_			)
          while j > 0:
               c[j] += c[j - 1]
               j -= 1
     return c[r]

print(binomial_coefficient(n=1_0, r=5))






