'''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_     :						Union[str, Any]    							=logging.get_logger(__name__)


class     __UpperCAmelCase       ( __a		):





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





  def 						UpperCAmelCase_	(	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 ) ):
        lowerCAmelCase_	,lowerCAmelCase_			=      controlnet(
            _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     )

        # merge samples
        if i == 0:
           lowerCAmelCase_	,lowerCAmelCase_			=      down_samples, mid_sample
        else:
           lowerCAmelCase_			=      [
               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 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase = True    ,     _lowerCamelCase = None    ,     _lowerCamelCase = False    ,     _lowerCamelCase = None    ,     ):
     lowerCAmelCase_			=      0
     lowerCAmelCase_			=      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
        lowerCAmelCase_			=      model_path_to_save + F'''_{idx}'''







  @classmethod
  def 						UpperCAmelCase_	(	cls    ,     _lowerCamelCase    ,     **_lowerCamelCase ):
     lowerCAmelCase_			=      0
     lowerCAmelCase_			=      []

     # 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_			=      pretrained_model_path
     while os.path.isdir(_lowerCamelCase ):
        lowerCAmelCase_			=      ControlNetModel.from_pretrained(_lowerCamelCase    ,     **_lowerCamelCase )
        controlnets.append(_lowerCamelCase )

        idx += 1
        lowerCAmelCase_			=      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 )






'''simple docstring'''

import colorsys

from PIL import Image  # type: ignore



def snake_case_      (					__snake_case  :							float	,   __snake_case  :							float	,   __snake_case  :							int)			->					float:
   lowerCAmelCase_			=      x
   lowerCAmelCase_			=      y
   for step in range(__snake_case):  # noqa: B007
      lowerCAmelCase_			=      a * a - b * b + x
      lowerCAmelCase_			=      2 * a * b + y
      lowerCAmelCase_			=      a_new

      # divergence happens for all complex number with an absolute value
      # greater than 4
      if a * a + b * b > 4:
         break
   return step / (max_step - 1)



def snake_case_      (					__snake_case  :							float)			->					tuple:
   if distance == 1:
      return (0, 0, 0)
   else:
      return (255, 255, 255)



def snake_case_      (					__snake_case  :							float)			->					tuple:
   if distance == 1:
      return (0, 0, 0)
   else:
      return tuple(round(i * 255) for i in colorsys.hsv_to_rgb(__snake_case	,   1	,   1))



def snake_case_      (					__snake_case  :							int = 800	,   __snake_case  :							int = 600	,   __snake_case  :							float = -0.6	,   __snake_case  :							float = 0	,   __snake_case  :							float = 3.2	,   __snake_case  :							int = 50	,   __snake_case  :							bool = True	,   )			->					Image.Image:
   lowerCAmelCase_			=      Image.new('''RGB'''	,   (image_width, image_height))
   lowerCAmelCase_			=      img.load()

   # loop through the image-coordinates
   for image_x in range(__snake_case):
      for image_y in range(__snake_case):
         # determine the figure-coordinates based on the image-coordinates
         lowerCAmelCase_			=      figure_width / image_width * image_height
         lowerCAmelCase_			=      figure_center_x + (image_x / image_width - 0.5) * figure_width
         lowerCAmelCase_			=      figure_center_y + (image_y / image_height - 0.5) * figure_height

         lowerCAmelCase_			=      get_distance(__snake_case	,   __snake_case	,   __snake_case)

         # color the corresponding pixel based on the selected coloring-function
         if use_distance_color_coding:
            lowerCAmelCase_			=      get_color_coded_rgb(__snake_case)
         else:
            lowerCAmelCase_			=      get_black_and_white_rgb(__snake_case)

   return img


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # colored version, full figure
 A_     :						List[str]    							=get_image()

 # uncomment for colored version, different section, zoomed in
 # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
 # figure_width = 0.8)

 # uncomment for black and white version, full figure
 # img = get_image(use_distance_color_coding = False)

 # uncomment to save the image
 # img.save("mandelbrot.png")

 img.show()





