


'''simple docstring'''





from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
__lowerCamelCase       				=	[[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 					a__    (				UpperCamelCase_	:			List[str]	):
   return [int(__A	) for c in F'''{ruleset:08}'''[:8]]


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


def 					a__    (				UpperCamelCase_	:			Union[str, Any]	):
   UpperCAmelCase__  :str            =			Image.new('''RGB''',      (len(cells[0]	), len(__A	))	)
   UpperCAmelCase__  :int            =			img.load()
   # Generates image
   for w in range(img.width	):
      for h in range(img.height	):
         UpperCAmelCase__  :Any            =			255 - int(255 * cells[h][w]	)
         UpperCAmelCase__  :Tuple            =			(color, color, color)
   return img


if __name__ == "__main__":
  __lowerCamelCase       				=	bin(int(input('''Rule:\n''').strip()))[2:]
  __lowerCamelCase       				=	format_ruleset(int(rule_num))
  for time in range(16):
    CELLS.append(new_generation(CELLS, rule, time))
  __lowerCamelCase       				=	generate_image(CELLS)
  # Uncomment to save the image
  # img.save(f"rule_{rule_num}.png")
  img.show()







'''simple docstring'''





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMParallelScheduler,
    DDPMParallelScheduler,
    StableDiffusionParadigmsPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    require_torch_gpu,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 							UpperCAmelCase ( _snake_case	,   _snake_case	,   unittest.TestCase	):
 UpperCAmelCase				=				StableDiffusionParadigmsPipeline
 UpperCAmelCase				=				TEXT_TO_IMAGE_PARAMS
 UpperCAmelCase				=				TEXT_TO_IMAGE_BATCH_PARAMS
 UpperCAmelCase				=				TEXT_TO_IMAGE_IMAGE_PARAMS
 UpperCAmelCase				=				TEXT_TO_IMAGE_IMAGE_PARAMS


 def 					__SCREAMING_SNAKE_CASE				(			self       :  int   ):
    torch.manual_seed(0   )
    UpperCAmelCase__  :Optional[int]            =			UNetaDConditionModel(
        block_out_channels=(3_2, 6_4) ,					layers_per_block=2 ,					sample_size=3_2 ,					in_channels=4 ,					out_channels=4 ,					down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') ,					up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') ,					cross_attention_dim=3_2 ,					attention_head_dim=(2, 4) ,					use_linear_projection=__lowerCamelCase ,					)
    UpperCAmelCase__  :Union[str, Any]            =			DDIMParallelScheduler(
        beta_start=0.0_00_85 ,					beta_end=0.0_12 ,					beta_schedule='''scaled_linear''' ,					clip_sample=__lowerCamelCase ,					set_alpha_to_one=__lowerCamelCase ,					)
    torch.manual_seed(0   )
    UpperCAmelCase__  :Tuple            =			AutoencoderKL(
        block_out_channels=[3_2, 6_4] ,					in_channels=3 ,					out_channels=3 ,					down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] ,					up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] ,					latent_channels=4 ,					sample_size=1_2_8 ,					)
    torch.manual_seed(0   )
    UpperCAmelCase__  :Tuple            =			CLIPTextConfig(
        bos_token_id=0 ,					eos_token_id=2 ,					hidden_size=3_2 ,					intermediate_size=3_7 ,					layer_norm_eps=1e-05 ,					num_attention_heads=4 ,					num_hidden_layers=5 ,					pad_token_id=1 ,					vocab_size=1_0_0_0 ,					hidden_act='''gelu''' ,					projection_dim=5_1_2 ,					)
    UpperCAmelCase__  :List[Any]            =			CLIPTextModel(__lowerCamelCase   )
    UpperCAmelCase__  :Tuple            =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

    UpperCAmelCase__  :int            =			{
        '''unet''': unet,
        '''scheduler''': scheduler,
        '''vae''': vae,
        '''text_encoder''': text_encoder,
        '''tokenizer''': tokenizer,
        '''safety_checker''': None,
        '''feature_extractor''': None,
    }
    return components


 def 					__SCREAMING_SNAKE_CASE				(			self       :  str ,					__lowerCamelCase       :  List[Any] ,					__lowerCamelCase       :  Any=0   ):
    if str(__lowerCamelCase   ).startswith('''mps'''   ):
       UpperCAmelCase__  :str            =			torch.manual_seed(__lowerCamelCase   )
    else:
       UpperCAmelCase__  :Union[str, Any]            =			torch.Generator(device=__lowerCamelCase   ).manual_seed(__lowerCamelCase   )
    UpperCAmelCase__  :Union[str, Any]            =			{
        '''prompt''': '''a photograph of an astronaut riding a horse''',
        '''generator''': generator,
        '''num_inference_steps''': 1_0,
        '''guidance_scale''': 6.0,
        '''output_type''': '''numpy''',
        '''parallel''': 3,
        '''debug''': True,
    }
    return inputs


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    UpperCAmelCase__  :Dict            =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
    UpperCAmelCase__  :Dict            =			self.get_dummy_components()
    UpperCAmelCase__  :str            =			StableDiffusionParadigmsPipeline(**__lowerCamelCase   )
    UpperCAmelCase__  :List[Any]            =			sd_pipe.to(__lowerCamelCase   )
    sd_pipe.set_progress_bar_config(disable=__lowerCamelCase   )

    UpperCAmelCase__  :Optional[int]            =			self.get_dummy_inputs(__lowerCamelCase   )
    UpperCAmelCase__  :Any            =			sd_pipe(**__lowerCamelCase   ).images
    UpperCAmelCase__  :Union[str, Any]            =			image[0, -3:, -3:, -1]
    assert image.shape == (1, 6_4, 6_4, 3)

    UpperCAmelCase__  :List[Any]            =			np.array([0.47_73, 0.54_17, 0.47_23, 0.49_25, 0.56_31, 0.47_52, 0.52_40, 0.49_35, 0.50_23]   )

    assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    UpperCAmelCase__  :Any            =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
    UpperCAmelCase__  :Dict            =			self.get_dummy_components()
    torch.manual_seed(0   )
    UpperCAmelCase__  :Tuple            =			DDPMParallelScheduler()
    torch.manual_seed(0   )
    UpperCAmelCase__  :List[str]            =			StableDiffusionParadigmsPipeline(**__lowerCamelCase   )
    UpperCAmelCase__  :Optional[int]            =			sd_pipe.to(__lowerCamelCase   )
    sd_pipe.set_progress_bar_config(disable=__lowerCamelCase   )

    UpperCAmelCase__  :Optional[int]            =			self.get_dummy_inputs(__lowerCamelCase   )
    UpperCAmelCase__  :Optional[Any]            =			sd_pipe(**__lowerCamelCase   ).images
    UpperCAmelCase__  :Dict            =			image[0, -3:, -3:, -1]
    assert image.shape == (1, 6_4, 6_4, 3)

    UpperCAmelCase__  :List[str]            =			np.array([0.35_73, 0.44_20, 0.49_60, 0.47_99, 0.37_96, 0.38_79, 0.48_19, 0.43_65, 0.44_68]   )

    assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    super().test_inference_batch_consistent(batch_sizes=[1, 2]   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  List[str]   ):
    super().test_inference_batch_single_identical(batch_size=2 ,					expected_max_diff=3e-3   )





 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    UpperCAmelCase__  :int            =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
    UpperCAmelCase__  :str            =			self.get_dummy_components()
    UpperCAmelCase__  :Any            =			StableDiffusionParadigmsPipeline(**__lowerCamelCase   )
    UpperCAmelCase__  :List[str]            =			sd_pipe.to(__lowerCamelCase   )
    sd_pipe.set_progress_bar_config(disable=__lowerCamelCase   )

    UpperCAmelCase__  :Tuple            =			self.get_dummy_inputs(__lowerCamelCase   )
    UpperCAmelCase__  :Optional[int]            =			'''french fries'''
    UpperCAmelCase__  :Any            =			sd_pipe(**__lowerCamelCase ,					negative_prompt=__lowerCamelCase   )
    UpperCAmelCase__  :int            =			output.images
    UpperCAmelCase__  :Tuple            =			image[0, -3:, -3:, -1]

    assert image.shape == (1, 6_4, 6_4, 3)

    UpperCAmelCase__  :Tuple            =			np.array([0.47_71, 0.54_20, 0.46_83, 0.49_18, 0.56_36, 0.47_25, 0.52_30, 0.49_23, 0.50_15]   )

    assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2




@slow
@require_torch_gpu
class 							UpperCAmelCase ( unittest.TestCase	):


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[Any] ,					__lowerCamelCase       :  Tuple=0   ):
    UpperCAmelCase__  :List[Any]            =			torch.Generator(device=__lowerCamelCase   ).manual_seed(__lowerCamelCase   )
    UpperCAmelCase__  :List[str]            =			{
        '''prompt''': '''a photograph of an astronaut riding a horse''',
        '''generator''': generator,
        '''num_inference_steps''': 1_0,
        '''guidance_scale''': 7.5,
        '''output_type''': '''numpy''',
        '''parallel''': 3,
        '''debug''': True,
    }
    return inputs





 def 					__SCREAMING_SNAKE_CASE				(			self       :  int   ):
    UpperCAmelCase__  :Dict            =			'''stabilityai/stable-diffusion-2-base'''
    UpperCAmelCase__  :Union[str, Any]            =			DDIMParallelScheduler.from_pretrained(__lowerCamelCase ,					subfolder='''scheduler'''   )
    UpperCAmelCase__  :Dict            =			StableDiffusionParadigmsPipeline.from_pretrained(__lowerCamelCase ,					scheduler=__lowerCamelCase ,					safety_checker=__lowerCamelCase   )
    pipe.to(__lowerCamelCase   )
    pipe.set_progress_bar_config(disable=__lowerCamelCase   )
    pipe.enable_attention_slicing()

    UpperCAmelCase__  :Dict            =			self.get_inputs()
    UpperCAmelCase__  :int            =			pipe(**__lowerCamelCase   ).images
    UpperCAmelCase__  :List[str]            =			image[0, -3:, -3:, -1].flatten()

    assert image.shape == (1, 5_1_2, 5_1_2, 3)

    UpperCAmelCase__  :Optional[int]            =			np.array([0.96_22, 0.96_02, 0.97_48, 0.95_91, 0.96_30, 0.96_91, 0.96_61, 0.96_31, 0.97_41]   )

    assert np.abs(expected_slice - image_slice   ).max() < 1e-2



