from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)



class    lowercase_    (     __snake_case		):



							def __init__(						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				):
													super().__init__()

													self.register_modules(vae=lowercase_      ,				text_encoder=lowercase_      ,				tokenizer=lowercase_      ,				unet=lowercase_      ,				scheduler=lowercase_				)



							def        UpperCamelCase       (						self      ,				lowercase_				):
													with torch.no_grad():
																			_snake_case						:     Optional[Any]				=    self.vae.encode(tfms.ToTensor()(lowercase_				).unsqueeze(0				).to(self.device				) * 2 - 1				)
																			_snake_case						:     Tuple				=    0.18_215 * latent.latent_dist.sample()
													return latent



							def        UpperCamelCase       (						self      ,				lowercase_				):
													_snake_case						:     List[str]				=    (1 / 0.18_215) * latent
													with torch.no_grad():
																			_snake_case						:     Tuple				=    self.vae.decode(lowercase_				).sample
													_snake_case						:     str				=    (img / 2 + 0.5).clamp(0      ,				1				)
													_snake_case						:     Optional[Any]				=    img.detach().cpu().permute(0      ,				2      ,				3      ,				1				).numpy()
													_snake_case						:     Dict				=    (img * 255).round().astype("uint8"				)
													return Image.fromarray(img[0]				)



							def        UpperCamelCase       (						self      ,				lowercase_				):
													_snake_case						:     str				=    self.tokenizer(
													    lowercase_      ,				padding="max_length"      ,				max_length=self.tokenizer.model_max_length      ,				truncation=lowercase_      ,				return_tensors="pt"      ,				)

													_snake_case						:     Optional[Any]				=    self.text_encoder(text_input.input_ids.to(self.device				)				)[0]

													_snake_case						:     Dict				=    self.tokenizer(
													    ""      ,				padding="max_length"      ,				max_length=self.tokenizer.model_max_length      ,				truncation=lowercase_      ,				return_tensors="pt"      ,				)

													_snake_case						:     Tuple				=    self.text_encoder(uncond_input.input_ids.to(self.device				)				)[0]

													return torch.cat([uncond_embedding, text_embedding]				)



							def __call__(						self      ,				lowercase_      ,				lowercase_      ,				lowercase_ = 0.3      ,				lowercase_ = 0.6      ,				lowercase_ = 0.5      ,				lowercase_ = 42      ,				lowercase_ = 50      ,				lowercase_ = 7.5      ,				):
													_snake_case						:     str				=    steps - int(kmin * steps				)
													_snake_case						:     Tuple				=    steps - int(kmax * steps				)

													_snake_case						:     Union[str, Any]				=    self.prep_text(lowercase_				)

													self.scheduler.set_timesteps(lowercase_				)

													_snake_case				,_snake_case						:     List[Any]				=    img.size
													_snake_case						:     Optional[Any]				=    self.encode(lowercase_				)

													torch.manual_seed(lowercase_				)
													_snake_case						:     str				=    torch.randn(
													    (1, self.unet.config.in_channels, height // 8, width // 8)      ,				).to(self.device				)

													_snake_case						:     Any				=    self.scheduler.add_noise(
													    lowercase_      ,				lowercase_      ,				timesteps=self.scheduler.timesteps[tmax]      ,				)

													_snake_case						:     Tuple				=    torch.cat([latents] * 2				)

													_snake_case						:     Optional[int]				=    self.scheduler.scale_model_input(lowercase_      ,				self.scheduler.timesteps[tmax]				)

													with torch.no_grad():
																			_snake_case						:     List[Any]				=    self.unet(
																			    lowercase_      ,				self.scheduler.timesteps[tmax]      ,				encoder_hidden_states=lowercase_      ,				).sample

													_snake_case				,_snake_case						:     Dict				=    pred.chunk(2				)
													_snake_case						:     str				=    pred_uncond + guidance_scale * (pred_text - pred_uncond)

													_snake_case						:     int				=    self.scheduler.step(lowercase_      ,				self.scheduler.timesteps[tmax]      ,				lowercase_				).prev_sample

													for i, t in enumerate(tqdm(self.scheduler.timesteps				)				):
																			if i > tmax:
																									if i < tmin:  # layout generation phase
																															_snake_case						:     Tuple				=    self.scheduler.add_noise(
																															    lowercase_      ,				lowercase_      ,				timesteps=lowercase_      ,				)

																															_snake_case						:     Dict				=    (mix_factor * latents) + (
																															    1 - mix_factor
																															) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
																															_snake_case						:     Dict				=    torch.cat([input] * 2				)

																									else:  # content generation phase
																															_snake_case						:     List[str]				=    torch.cat([latents] * 2				)

																									_snake_case						:     Tuple				=    self.scheduler.scale_model_input(lowercase_      ,				lowercase_				)

																									with torch.no_grad():
																															_snake_case						:     str				=    self.unet(
																															    lowercase_      ,				lowercase_      ,				encoder_hidden_states=lowercase_      ,				).sample

																									_snake_case				,_snake_case						:     List[Any]				=    pred.chunk(2				)
																									_snake_case						:     int				=    pred_uncond + guidance_scale * (pred_text - pred_uncond)

																									_snake_case						:     Optional[Any]				=    self.scheduler.step(lowercase_      ,				lowercase_      ,				lowercase_				).prev_sample

													return self.decode(lowercase_				)
from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
__SCREAMING_SNAKE_CASE						:    Optional[int]     = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
def 					snake_case						(__lowercase						)       ->       list[int]:

						'''simple docstring'''



						return [int(__lowercase						) for c in F"""{ruleset:08}"""[:8]]
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase						)       ->       list[int]:

						'''simple docstring'''



						_snake_case						:     Any				=    len(cells[0]						)  # 31
						_snake_case						:     str				=    []
						for i in range(__lowercase						):
												# Get the neighbors of each cell
												# Handle neighbours outside bounds by using 0 as their value
												_snake_case						:     Any				=    0 if i == 0 else cells[time][i - 1]
												_snake_case						:     List[str]				=    0 if i == population - 1 else cells[time][i + 1]
												# Define a new cell and add it to the new generation
												_snake_case						:     str				=    7 - int(F"""{left_neighbor}{cells[time][i]}{right_neighbor}"""					,			2						)
												next_generation.append(rule[situation]						)
						return next_generation
def 					snake_case						(__lowercase						)       ->       Image.Image:

						'''simple docstring'''



						_snake_case						:     str				=    Image.new("RGB"					,			(len(cells[0]						), len(__lowercase						))						)
						_snake_case						:     int				=    img.load()
						# Generates image
						for w in range(img.width						):
												for h in range(img.height						):
																		_snake_case						:     List[Any]				=    255 - int(255 * cells[h][w]						)
																		_snake_case						:     Union[str, Any]				=    (color, color, color)
						return img


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:    Any     = bin(int(input('Rule:\n').strip()))[2:]
				__SCREAMING_SNAKE_CASE						:    Tuple     = format_ruleset(int(rule_num))
				for time in range(1_6):
								CELLS.append(new_generation(CELLS, rule, time))
				__SCREAMING_SNAKE_CASE						:    List[Any]     = generate_image(CELLS)
				# Uncomment to save the image
				# img.save(f"rule_{rule_num}.png")
				img.show()