


'''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


A__						:   Dict				=    logging.get_logger(__name__)





class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_      )							->	List[str]:
         super().__init__()
         __lowerCamelCase  :      str														=						nn.ModuleList(SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = False     ,	SCREAMING_SNAKE_CASE_ = True     ,	)							->	Union[ControlNetOutput, Tuple]:
         for i, (image, scale, controlnet) in enumerate(zip(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	self.nets      )      ):
              __lowerCamelCase							,			__lowerCamelCase  :      str														=						controlnet(
                  SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	)

              # merge samples
              if i == 0:
                   __lowerCamelCase							,			__lowerCamelCase  :      Tuple														=						down_samples, mid_sample
              else:
                   __lowerCamelCase  :      str														=						[
                       samples_prev + samples_curr
                       for samples_prev, samples_curr in zip(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
                   ]
                   mid_block_res_sample += mid_sample

         return down_block_res_samples, mid_block_res_sample







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = True     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = False     ,	SCREAMING_SNAKE_CASE_ = None     ,	)							->	List[Any]:
         __lowerCamelCase  :      Any														=						0
         __lowerCamelCase  :      Union[str, Any]														=						save_directory
         for controlnet in self.nets:
              controlnet.save_pretrained(
                  SCREAMING_SNAKE_CASE_     ,	is_main_process=SCREAMING_SNAKE_CASE_     ,	save_function=SCREAMING_SNAKE_CASE_     ,	safe_serialization=SCREAMING_SNAKE_CASE_     ,	variant=SCREAMING_SNAKE_CASE_     ,	)

              idx += 1
              __lowerCamelCase  :      List[str]														=						model_path_to_save + f'_{idx}'







    @classmethod
    def  lowercase_							(				cls     ,	SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )							->	Optional[Any]:
         __lowerCamelCase  :      str														=						0
         __lowerCamelCase  :      Any														=						[]

         # 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`, ...
         __lowerCamelCase  :      str														=						pretrained_model_path
         while os.path.isdir(SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      Optional[int]														=						ControlNetModel.from_pretrained(SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )
              controlnets.append(SCREAMING_SNAKE_CASE_      )

              idx += 1
              __lowerCamelCase  :      Dict														=						pretrained_model_path + f'_{idx}'

         logger.info(f'{len(SCREAMING_SNAKE_CASE_      )} controlnets loaded from {pretrained_model_path}.'      )

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

         return cls(SCREAMING_SNAKE_CASE_      )










'''simple docstring'''



from __future__ import annotations

A__						:   int				=    10



def      UpperCAmelCase__   (      UpperCAmelCase_       :							list[int]				)	->						list[int]:
     __lowerCamelCase  :      List[Any]														=						1
     __lowerCamelCase  :      Any														=						max(UpperCAmelCase_				)
     while placement <= max_digit:
          # declare and initialize empty buckets
          __lowerCamelCase  :      list[list]														=						[[] for _ in range(UpperCAmelCase_				)]
          # split list_of_ints between the buckets
          for i in list_of_ints:
               __lowerCamelCase  :      List[Any]														=						int((i / placement) % RADIX				)
               buckets[tmp].append(UpperCAmelCase_				)
          # put each buckets' contents into list_of_ints
          __lowerCamelCase  :      Tuple														=						0
          for b in range(UpperCAmelCase_				):
               for i in buckets[b]:
                    __lowerCamelCase  :      List[Any]														=						i
                    a += 1
        # move to next
          placement *= RADIX
     return list_of_ints


if __name__ == "__main__":
      import doctest

      doctest.testmod()






