






"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block






@dataclass
class  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				torch.FloatTensor






class  _SCREAMING_SNAKE_CASE(       A   ,     A  ):




       @register_to_config
       def __init__(						self		,SCREAMING_SNAKE_CASE__ = 6_55_36		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = 2		,SCREAMING_SNAKE_CASE__ = 2		,SCREAMING_SNAKE_CASE__ = 0		,SCREAMING_SNAKE_CASE__ = "fourier"		,SCREAMING_SNAKE_CASE__ = True		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = 0.0		,SCREAMING_SNAKE_CASE__ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")		,SCREAMING_SNAKE_CASE__ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")		,SCREAMING_SNAKE_CASE__ = "UNetMidBlock1D"		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = (32, 32, 64)		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = 8		,SCREAMING_SNAKE_CASE__ = 1		,SCREAMING_SNAKE_CASE__ = False		,)      ->				Dict:





             """simple docstring"""






             super().__init__()
             __SCREAMING_SNAKE_CASE       :Tuple		    =  sample_size

             # time
             if time_embedding_type == "fourier":
                   __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  GaussianFourierProjection(
                       embedding_size=8		,set_W_to_weight=SCREAMING_SNAKE_CASE__		,log=SCREAMING_SNAKE_CASE__		,flip_sin_to_cos=SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  2 * block_out_channels[0]
             elif time_embedding_type == "positional":
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  Timesteps(
                       block_out_channels[0]		,flip_sin_to_cos=SCREAMING_SNAKE_CASE__		,downscale_freq_shift=SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE       :int		    =  block_out_channels[0]

             if use_timestep_embedding:
                   __SCREAMING_SNAKE_CASE       :List[Any]		    =  block_out_channels[0] * 4
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  TimestepEmbedding(
                       in_channels=SCREAMING_SNAKE_CASE__		,time_embed_dim=SCREAMING_SNAKE_CASE__		,act_fn=SCREAMING_SNAKE_CASE__		,out_dim=block_out_channels[0]		,)

             __SCREAMING_SNAKE_CASE       :List[str]		    =  nn.ModuleList([]							)
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  None
             __SCREAMING_SNAKE_CASE       :Tuple		    =  nn.ModuleList([]							)
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  None

             # down
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  in_channels
             for i, down_block_type in enumerate(SCREAMING_SNAKE_CASE__							):
                   __SCREAMING_SNAKE_CASE       :Dict		    =  output_channel
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  block_out_channels[i]

                   if i == 0:
                         input_channel += extra_in_channels

                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  i == len(SCREAMING_SNAKE_CASE__							) - 1

                   __SCREAMING_SNAKE_CASE       :Dict		    =  get_down_block(
                       SCREAMING_SNAKE_CASE__		,num_layers=SCREAMING_SNAKE_CASE__		,in_channels=SCREAMING_SNAKE_CASE__		,out_channels=SCREAMING_SNAKE_CASE__		,temb_channels=block_out_channels[0]		,add_downsample=not is_final_block or downsample_each_block		,)
                   self.down_blocks.append(SCREAMING_SNAKE_CASE__							)

             # mid
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  get_mid_block(
                 SCREAMING_SNAKE_CASE__		,in_channels=block_out_channels[-1]		,mid_channels=block_out_channels[-1]		,out_channels=block_out_channels[-1]		,embed_dim=block_out_channels[0]		,num_layers=SCREAMING_SNAKE_CASE__		,add_downsample=SCREAMING_SNAKE_CASE__		,)

             # up
             __SCREAMING_SNAKE_CASE       :List[str]		    =  list(reversed(SCREAMING_SNAKE_CASE__							)							)
             __SCREAMING_SNAKE_CASE       :Tuple		    =  reversed_block_out_channels[0]
             if out_block_type is None:
                   __SCREAMING_SNAKE_CASE       :List[Any]		    =  out_channels
             else:
                   __SCREAMING_SNAKE_CASE       :Any		    =  block_out_channels[0]

             for i, up_block_type in enumerate(SCREAMING_SNAKE_CASE__							):
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  output_channel
                   __SCREAMING_SNAKE_CASE       :Dict		    =  (
                       reversed_block_out_channels[i + 1] if i < len(SCREAMING_SNAKE_CASE__							) - 1 else final_upsample_channels
                   )

                   __SCREAMING_SNAKE_CASE       :Any		    =  i == len(SCREAMING_SNAKE_CASE__							) - 1

                   __SCREAMING_SNAKE_CASE       :Any		    =  get_up_block(
                       SCREAMING_SNAKE_CASE__		,num_layers=SCREAMING_SNAKE_CASE__		,in_channels=SCREAMING_SNAKE_CASE__		,out_channels=SCREAMING_SNAKE_CASE__		,temb_channels=block_out_channels[0]		,add_upsample=not is_final_block		,)
                   self.up_blocks.append(SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE       :int		    =  output_channel

             # out
             __SCREAMING_SNAKE_CASE       :int		    =  norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4		,32							)
             __SCREAMING_SNAKE_CASE       :Dict		    =  get_out_block(
                 out_block_type=SCREAMING_SNAKE_CASE__		,num_groups_out=SCREAMING_SNAKE_CASE__		,embed_dim=block_out_channels[0]		,out_channels=SCREAMING_SNAKE_CASE__		,act_fn=SCREAMING_SNAKE_CASE__		,fc_dim=block_out_channels[-1] // 4		,)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = True		,)      ->				Union[UNetaDOutput, Tuple]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Tuple		    =  timestep
             if not torch.is_tensor(SCREAMING_SNAKE_CASE__							):
                   __SCREAMING_SNAKE_CASE       :Any		    =  torch.tensor([timesteps]		,dtype=torch.long		,device=sample.device							)
             elif torch.is_tensor(SCREAMING_SNAKE_CASE__							) and len(timesteps.shape							) == 0:
                   __SCREAMING_SNAKE_CASE       :int		    =  timesteps[None].to(sample.device							)

             __SCREAMING_SNAKE_CASE       :str		    =  self.time_proj(SCREAMING_SNAKE_CASE__							)
             if self.config.use_timestep_embedding:
                   __SCREAMING_SNAKE_CASE       :List[Any]		    =  self.time_mlp(SCREAMING_SNAKE_CASE__							)
             else:
                   __SCREAMING_SNAKE_CASE       :int		    =  timestep_embed[..., None]
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  timestep_embed.repeat([1, 1, sample.shape[2]]							).to(sample.dtype							)
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])							)

             # 2. down
             __SCREAMING_SNAKE_CASE       :Dict		    =  ()
             for downsample_block in self.down_blocks:
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  downsample_block(hidden_states=SCREAMING_SNAKE_CASE__		,temb=SCREAMING_SNAKE_CASE__							)
                   down_block_res_samples += res_samples

             # 3. mid
             if self.mid_block:
                   __SCREAMING_SNAKE_CASE       :Tuple		    =  self.mid_block(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

             # 4. up
             for i, upsample_block in enumerate(self.up_blocks							):
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  down_block_res_samples[-1:]
                   __SCREAMING_SNAKE_CASE       :Any		    =  down_block_res_samples[:-1]
                   __SCREAMING_SNAKE_CASE       :str		    =  upsample_block(SCREAMING_SNAKE_CASE__		,res_hidden_states_tuple=SCREAMING_SNAKE_CASE__		,temb=SCREAMING_SNAKE_CASE__							)

             # 5. post-process
             if self.out_block:
                   __SCREAMING_SNAKE_CASE       :str		    =  self.out_block(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

             if not return_dict:
                   return (sample,)

             return UNetaDOutput(sample=SCREAMING_SNAKE_CASE__							)






"""simple docstring"""
import colorsys

from PIL import Image  # type: ignore



def 					__lowerCamelCase       (							a_					:		float			,       a_					:		float			,       a_					:		int							)      ->				float:
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  x
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  y
      for step in range(a_							):  # noqa: B007
            __SCREAMING_SNAKE_CASE       :Dict		    =  a * a - b * b + x
            __SCREAMING_SNAKE_CASE       :Tuple		    =  2 * a * b + y
            __SCREAMING_SNAKE_CASE       :Dict		    =  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 					__lowerCamelCase       (							a_					:		float							)      ->				tuple:
      if distance == 1:
            return (0, 0, 0)
      else:
            return (2_55, 2_55, 2_55)



def 					__lowerCamelCase       (							a_					:		float							)      ->				tuple:
      if distance == 1:
            return (0, 0, 0)
      else:
            return tuple(round(i * 2_55							) for i in colorsys.hsv_to_rgb(a_			,       1			,       1							)							)



def 					__lowerCamelCase       (							a_					:		int = 8_00			,       a_					:		int = 6_00			,       a_					:		float = -0.6			,       a_					:		float = 0			,       a_					:		float = 3.2			,       a_					:		int = 50			,       a_					:		bool = True			,       )      ->				Image.Image:
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  Image.new('''RGB'''			,       (image_width, image_height)							)
      __SCREAMING_SNAKE_CASE       :Tuple		    =  img.load()

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

                  __SCREAMING_SNAKE_CASE       :List[Any]		    =  get_distance(a_			,       a_			,       a_							)

                  # color the corresponding pixel based on the selected coloring-function
                  if use_distance_color_coding:
                        __SCREAMING_SNAKE_CASE       :Optional[int]		    =  get_color_coded_rgb(a_							)
                  else:
                        __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  get_black_and_white_rgb(a_							)

      return img


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # colored version, full figure
  lowerCamelCase_       =		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()