


'''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        lowerCAmelCase_  (     UpperCAmelCase	):
       __UpperCAmelCase					=42






class        lowerCAmelCase_  (     UpperCAmelCase      ,							UpperCAmelCase	):






       @register_to_config
       def __init__( self			,     _UpperCamelCase = 6_5536			,     _UpperCamelCase = None			,     _UpperCamelCase = 2			,     _UpperCamelCase = 2			,     _UpperCamelCase = 0			,     _UpperCamelCase = "fourier"			,     _UpperCamelCase = True			,     _UpperCamelCase = False			,     _UpperCamelCase = 0.0			,     _UpperCamelCase = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")			,     _UpperCamelCase = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")			,     _UpperCamelCase = "UNetMidBlock1D"			,     _UpperCamelCase = None			,     _UpperCamelCase = (32, 32, 64)			,     _UpperCamelCase = None			,     _UpperCamelCase = 8			,     _UpperCamelCase = 1			,     _UpperCamelCase = False			,     )->      Dict:
            super().__init__()
            _A				=							sample_size

            # time
            if time_embedding_type == "fourier":
                 _A				=							GaussianFourierProjection(
                     embedding_size=8			,     set_W_to_weight=_UpperCamelCase			,     log=_UpperCamelCase			,     flip_sin_to_cos=_UpperCamelCase   )
                 _A				=							2 * block_out_channels[0]
            elif time_embedding_type == "positional":
                 _A				=							Timesteps(
                     block_out_channels[0]			,     flip_sin_to_cos=_UpperCamelCase			,     downscale_freq_shift=_UpperCamelCase   )
                 _A				=							block_out_channels[0]

            if use_timestep_embedding:
                 _A				=							block_out_channels[0] * 4
                 _A				=							TimestepEmbedding(
                     in_channels=_UpperCamelCase			,     time_embed_dim=_UpperCamelCase			,     act_fn=_UpperCamelCase			,     out_dim=block_out_channels[0]			,     )

            _A				=							nn.ModuleList([]   )
            _A				=							None
            _A				=							nn.ModuleList([]   )
            _A				=							None

            # down
            _A				=							in_channels
            for i, down_block_type in enumerate(_UpperCamelCase   ):
                 _A				=							output_channel
                 _A				=							block_out_channels[i]

                 if i == 0:
                      input_channel += extra_in_channels

                 _A				=							i == len(_UpperCamelCase   ) - 1

                 _A				=							get_down_block(
                     _UpperCamelCase			,     num_layers=_UpperCamelCase			,     in_channels=_UpperCamelCase			,     out_channels=_UpperCamelCase			,     temb_channels=block_out_channels[0]			,     add_downsample=not is_final_block or downsample_each_block			,     )
                 self.down_blocks.append(_UpperCamelCase   )

            # mid
            _A				=							get_mid_block(
                _UpperCamelCase			,     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=_UpperCamelCase			,     add_downsample=_UpperCamelCase			,     )

            # up
            _A				=							list(reversed(_UpperCamelCase   )   )
            _A				=							reversed_block_out_channels[0]
            if out_block_type is None:
                 _A				=							out_channels
            else:
                 _A				=							block_out_channels[0]

            for i, up_block_type in enumerate(_UpperCamelCase   ):
                 _A				=							output_channel
                 _A				=							(
                     reversed_block_out_channels[i + 1] if i < len(_UpperCamelCase   ) - 1 else final_upsample_channels
                 )

                 _A				=							i == len(_UpperCamelCase   ) - 1

                 _A				=							get_up_block(
                     _UpperCamelCase			,     num_layers=_UpperCamelCase			,     in_channels=_UpperCamelCase			,     out_channels=_UpperCamelCase			,     temb_channels=block_out_channels[0]			,     add_upsample=not is_final_block			,     )
                 self.up_blocks.append(_UpperCamelCase   )
                 _A				=							output_channel

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






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase = True			,     )->      Union[UNetaDOutput, Tuple]:
            _A				=							timestep
            if not torch.is_tensor(_UpperCamelCase   ):
                 _A				=							torch.tensor([timesteps]			,     dtype=torch.long			,     device=sample.device   )
            elif torch.is_tensor(_UpperCamelCase   ) and len(timesteps.shape   ) == 0:
                 _A				=							timesteps[None].to(sample.device   )

            _A				=							self.time_proj(_UpperCamelCase   )
            if self.config.use_timestep_embedding:
                 _A				=							self.time_mlp(_UpperCamelCase   )
            else:
                 _A				=							timestep_embed[..., None]
                 _A				=							timestep_embed.repeat([1, 1, sample.shape[2]]   ).to(sample.dtype   )
                 _A				=							timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])   )

            # 2. down
            _A				=							()
            for downsample_block in self.down_blocks:
                 _A		,				_A				=							downsample_block(hidden_states=_UpperCamelCase			,     temb=_UpperCamelCase   )
                 down_block_res_samples += res_samples

            # 3. mid
            if self.mid_block:
                 _A				=							self.mid_block(_UpperCamelCase			,     _UpperCamelCase   )

            # 4. up
            for i, upsample_block in enumerate(self.up_blocks   ):
                 _A				=							down_block_res_samples[-1:]
                 _A				=							down_block_res_samples[:-1]
                 _A				=							upsample_block(_UpperCamelCase			,     res_hidden_states_tuple=_UpperCamelCase			,     temb=_UpperCamelCase   )

            # 5. post-process
            if self.out_block:
                 _A				=							self.out_block(_UpperCamelCase			,     _UpperCamelCase   )

            if not return_dict:
                 return (sample,)

            return UNetaDOutput(sample=_UpperCamelCase   )









'''simple docstring'''
import colorsys

from PIL import Image  # type: ignore

def 					lowerCamelCase_ ( __UpperCamelCase					:       float							,    __UpperCamelCase					:       float							,    __UpperCamelCase					:       int		)					->	float:







     """simple docstring"""
     _A				=							x
     _A				=							y
     for step in range(__UpperCamelCase		):  # noqa: B007
          _A				=							a * a - b * b + x
          _A				=							2 * a * b + y
          _A				=							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_ ( __UpperCamelCase					:       float		)					->	tuple:







     """simple docstring"""
     if distance == 1:
          return (0, 0, 0)
     else:
          return (2_5_5, 2_5_5, 2_5_5)

def 					lowerCamelCase_ ( __UpperCamelCase					:       float		)					->	tuple:







     """simple docstring"""
     if distance == 1:
          return (0, 0, 0)
     else:
          return tuple(round(i * 2_5_5		) for i in colorsys.hsv_to_rgb(__UpperCamelCase							,    1							,    1		)		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       int = 8_0_0							,    __UpperCamelCase					:       int = 6_0_0							,    __UpperCamelCase					:       float = -0.6							,    __UpperCamelCase					:       float = 0							,    __UpperCamelCase					:       float = 3.2							,    __UpperCamelCase					:       int = 5_0							,    __UpperCamelCase					:       bool = True							,    )					->	Image.Image:







     """simple docstring"""
     _A				=							Image.new('RGB'							,    (image_width, image_height)		)
     _A				=							img.load()

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

               _A				=							get_distance(__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase		)

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

     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()





