






"""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


UpperCAmelCase_   : Tuple   			= logging.get_logger(__name__)




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      def __init__(						self     :  Any						,	lowercase_     :  Optional[int]):




          '''simple docstring'''


          super().__init__()
          SCREAMING_SNAKE_CASE_      :  Dict    						=		nn.ModuleList(lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]						,	lowercase_     :  Optional[int]						,	lowercase_     :  int						,	lowercase_     :  Optional[int]						,	lowercase_     :  Optional[Any]						,	lowercase_     :  Optional[Any]						,	lowercase_     :  int = None						,	lowercase_     :  Optional[int] = None						,	lowercase_     :  Optional[int] = None						,	lowercase_     :  int = None						,	lowercase_     :  Optional[Any] = False						,	lowercase_     :  int = True						,	):




          '''simple docstring'''


          for i, (image, scale, controlnet) in enumerate(zip(lowercase_						,	lowercase_						,	self.nets)):
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		controlnet(
                  lowercase_						,	lowercase_						,	lowercase_						,	lowercase_						,	lowercase_						,	lowercase_						,	lowercase_						,	lowercase_						,	lowercase_						,	lowercase_						,	lowercase_						,	)

              # merge samples
              if i == 0:
                  SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		down_samples, mid_sample
              else:
                  SCREAMING_SNAKE_CASE_      :  Any    						=		[
                      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 _SCREAMING_SNAKE_CASE (						self     :  Any						,	lowercase_     :  Any						,	lowercase_     :  Dict = True						,	lowercase_     :  int = None						,	lowercase_     :  Optional[Any] = False						,	lowercase_     :  Tuple = None						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		0
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		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
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		model_path_to_save + F'_{idx}'



      @classmethod
      def _SCREAMING_SNAKE_CASE (						cls     :  Tuple						,	lowercase_     :  Dict						,	**lowercase_     :  int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		0
          SCREAMING_SNAKE_CASE_      :  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`, ...
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		pretrained_model_path
          while os.path.isdir(lowercase_):
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		ControlNetModel.from_pretrained(lowercase_						,	**lowercase_)
              controlnets.append(lowercase_)

              idx += 1
              SCREAMING_SNAKE_CASE_      :  List[str]    						=		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"""
from __future__ import annotations

import math





def  lowerCamelCase_						(UpperCamelCase__		:		int     ,       UpperCamelCase__		:		int     ,       UpperCamelCase__		:		bool     ,       UpperCamelCase__		:		list[int]     ,       UpperCamelCase__		:		float							):

    if depth < 0:
        raise ValueError('''Depth cannot be less than 0'''							)

    if len(UpperCamelCase__							) == 0:
        raise ValueError('''Scores cannot be empty'''							)

    if depth == height:
        return scores[node_index]

    if is_max:
        return max(
            minimax(depth + 1     ,       node_index * 2     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)     ,       minimax(depth + 1     ,       node_index * 2 + 1     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)     ,       )

    return min(
        minimax(depth + 1     ,       node_index * 2     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)     ,       minimax(depth + 1     ,       node_index * 2 + 1     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)     ,       )





def  lowerCamelCase_						():
    _UpperCAmelCase							:  Any											=    [90, 23, 6, 33, 21, 65, 123, 3_4423]
    _UpperCAmelCase							:  Any											=    math.log(len(UpperCamelCase__							)     ,       2							)
    print('''Optimal value : '''     ,       end=''''''							)
    print(minimax(0     ,       0     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()


