




from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


UpperCamelCase__						=		logging.get_logger(__name__)  # pylint: disable=invalid-name


UpperCamelCase__						=		'\n    Examples:\n        ```py\n        >>> import torch\n\n        >>> from diffusers import ConsistencyModelPipeline\n\n        >>> device = "cuda"\n        >>> # Load the cd_imagenet64_l2 checkpoint.\n        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"\n        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n        >>> pipe.to(device)\n\n        >>> # Onestep Sampling\n        >>> image = pipe(num_inference_steps=1).images[0]\n        >>> image.save("cd_imagenet64_l2_onestep_sample.png")\n\n        >>> # Onestep sampling, class-conditional image generation\n        >>> # ImageNet-64 class label 145 corresponds to king penguins\n        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]\n        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")\n\n        >>> # Multistep sampling, class-conditional image generation\n        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:\n        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77\n        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]\n        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")\n        ```\n'


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):

					def __init__(							self					,			__lowercase					,			__lowercase   ):







						'''simple docstring'''




						super().__init__()

						self.register_modules(
						    unet=__lowercase					,			scheduler=__lowercase					,			)

						lowerCamelCase_				:						Union[str, Any]				  =				None

					def       lowercase_				(							self					,			__lowercase=0   ):







						'''simple docstring'''




						if is_accelerate_available() and is_accelerate_version('>='					,			'0.14.0'   ):
							from accelerate import cpu_offload
						else:
							raise ImportError('`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher'   )

						lowerCamelCase_				:						Tuple				  =				torch.device(f"cuda:{gpu_id}"   )

						if self.device.type != "cpu":
							self.to('cpu'					,			silence_dtype_warnings=__lowercase   )
							torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

						for cpu_offloaded_model in [self.unet]:
							cpu_offload(__lowercase					,			__lowercase   )

						if self.safety_checker is not None:
							cpu_offload(self.safety_checker					,			execution_device=__lowercase					,			offload_buffers=__lowercase   )

					def       lowercase_				(							self					,			__lowercase=0   ):







						'''simple docstring'''




						if is_accelerate_available() and is_accelerate_version('>='					,			'0.17.0.dev0'   ):
							from accelerate import cpu_offload_with_hook
						else:
							raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'   )

						lowerCamelCase_				:						Tuple				  =				torch.device(f"cuda:{gpu_id}"   )

						if self.device.type != "cpu":
							self.to('cpu'					,			silence_dtype_warnings=__lowercase   )
							torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

						lowerCamelCase_				:						List[str]				  =				None
						for cpu_offloaded_model in [self.unet]:
							lowerCamelCase_  ,     lowerCamelCase_				:						List[str]				  =				cpu_offload_with_hook(__lowercase					,			__lowercase					,			prev_module_hook=__lowercase   )

						if self.safety_checker is not None:
							lowerCamelCase_  ,     lowerCamelCase_				:						int				  =				cpu_offload_with_hook(self.safety_checker					,			__lowercase					,			prev_module_hook=__lowercase   )

						# We'll offload the last model manually.
						lowerCamelCase_				:						str				  =				hook

					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def       lowercase_				(							self   ):







						'''simple docstring'''




						if not hasattr(self.unet					,			'_hf_hook'   ):
							return self.device
						for module in self.unet.modules():
							if (
							    hasattr(__lowercase					,			'_hf_hook'   )
							    and hasattr(module._hf_hook					,			'execution_device'   )
							    and module._hf_hook.execution_device is not None
							):
								return torch.device(module._hf_hook.execution_device   )
						return self.device

					def       lowercase_				(							self					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase=None   ):







						'''simple docstring'''




						lowerCamelCase_				:						Optional[int]				  =				(batch_size, num_channels, height, width)
						if isinstance(__lowercase					,			__lowercase   ) and len(__lowercase   ) != batch_size:
							raise ValueError(
							    f"You have passed a list of generators of length {len(__lowercase   )}, but requested an effective batch"
							    f" size of {batch_size}. Make sure the batch size matches the length of the generators."   )

						if latents is None:
							lowerCamelCase_				:						str				  =				randn_tensor(__lowercase					,			generator=__lowercase					,			device=__lowercase					,			dtype=__lowercase   )
						else:
							lowerCamelCase_				:						Optional[int]				  =				latents.to(device=__lowercase					,			dtype=__lowercase   )

						# scale the initial noise by the standard deviation required by the scheduler
						lowerCamelCase_				:						Optional[int]				  =				latents * self.scheduler.init_noise_sigma
						return latents

					def       lowercase_				(							self					,			__lowercase					,			__lowercase = "pil"   ):







						'''simple docstring'''




						if output_type not in ["pt", "np", "pil"]:
							raise ValueError(
							    f"output_type={output_type} is not supported. Make sure to choose one of ['pt', 'np', or 'pil']"   )

						# Equivalent to diffusers.VaeImageProcessor.denormalize
						lowerCamelCase_				:						Any				  =				(sample / 2 + 0.5).clamp(0					,			1   )
						if output_type == "pt":
							return sample

						# Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
						lowerCamelCase_				:						int				  =				sample.cpu().permute(0					,			2					,			3					,			1   ).numpy()
						if output_type == "np":
							return sample

						# Output_type must be 'pil'
						lowerCamelCase_				:						int				  =				self.numpy_to_pil(__lowercase   )
						return sample

					def       lowercase_				(							self					,			__lowercase					,			__lowercase					,			__lowercase=None   ):







						'''simple docstring'''




						if self.unet.config.num_class_embeds is not None:
							if isinstance(__lowercase					,			__lowercase   ):
								lowerCamelCase_				:						Union[str, Any]				  =				torch.tensor(__lowercase					,			dtype=torch.int   )
							elif isinstance(__lowercase					,			__lowercase   ):
								assert batch_size == 1, "Batch size must be 1 if classes is an int"
								lowerCamelCase_				:						Dict				  =				torch.tensor([class_labels]					,			dtype=torch.int   )
							elif class_labels is None:
								# Randomly generate batch_size class labels
								# TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
								lowerCamelCase_				:						List[str]				  =				torch.randint(0					,			self.unet.config.num_class_embeds					,			size=(batch_size,)   )
							lowerCamelCase_				:						Dict				  =				class_labels.to(__lowercase   )
						else:
							lowerCamelCase_				:						Any				  =				None
						return class_labels

					def       lowercase_				(							self					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase   ):







						'''simple docstring'''




						if num_inference_steps is None and timesteps is None:
							raise ValueError('Exactly one of `num_inference_steps` or `timesteps` must be supplied.'   )

						if num_inference_steps is not None and timesteps is not None:
							logger.warning(
							    f"Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;"
							    ' `timesteps` will be used over `num_inference_steps`.'   )

						if latents is not None:
							lowerCamelCase_				:						int				  =				(batch_size, 3, img_size, img_size)
							if latents.shape != expected_shape:
								raise ValueError(f"The shape of latents is {latents.shape} but is expected to be {expected_shape}."   )

						if (callback_steps is None) or (
						    callback_steps is not None and (not isinstance(__lowercase					,			__lowercase   ) or callback_steps <= 0)
						):
							raise ValueError(
							    f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
							    f" {type(__lowercase   )}."   )





					@torch.no_grad()
					@replace_example_docstring(__lowercase   )
					def __call__(							self					,			__lowercase = 1					,			__lowercase = None					,			__lowercase = 1					,			__lowercase = None					,			__lowercase = None					,			__lowercase = None					,			__lowercase = "pil"					,			__lowercase = True					,			__lowercase = None					,			__lowercase = 1					,			):







						'''simple docstring'''




						lowerCamelCase_				:						Tuple				  =				self.unet.config.sample_size
						lowerCamelCase_				:						Optional[int]				  =				self._execution_device

						# 1. Check inputs
						self.check_inputs(__lowercase					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase   )

						# 2. Prepare image latents
						# Sample image latents x_0 ~ N(0, sigma_0^2 * I)
						lowerCamelCase_				:						Tuple				  =				self.prepare_latents(
						    batch_size=__lowercase					,			num_channels=self.unet.config.in_channels					,			height=__lowercase					,			width=__lowercase					,			dtype=self.unet.dtype					,			device=__lowercase					,			generator=__lowercase					,			latents=__lowercase					,			)

						# 3. Handle class_labels for class-conditional models
						lowerCamelCase_				:						str				  =				self.prepare_class_labels(__lowercase					,			__lowercase					,			class_labels=__lowercase   )

						# 4. Prepare timesteps
						if timesteps is not None:
							self.scheduler.set_timesteps(timesteps=__lowercase					,			device=__lowercase   )
							lowerCamelCase_				:						Optional[int]				  =				self.scheduler.timesteps
							lowerCamelCase_				:						Optional[int]				  =				len(__lowercase   )
						else:
							self.scheduler.set_timesteps(__lowercase   )
							lowerCamelCase_				:						Tuple				  =				self.scheduler.timesteps

						# 5. Denoising loop
						# Multistep sampling: implements Algorithm 1 in the paper
						with self.progress_bar(total=__lowercase   ) as progress_bar:
							for i, t in enumerate(__lowercase   ):
								lowerCamelCase_				:						Any				  =				self.scheduler.scale_model_input(__lowercase					,			__lowercase   )
								lowerCamelCase_				:						List[str]				  =				self.unet(__lowercase					,			__lowercase					,			class_labels=__lowercase					,			return_dict=__lowercase   )[0]

								lowerCamelCase_				:						Optional[Any]				  =				self.scheduler.step(__lowercase					,			__lowercase					,			__lowercase					,			generator=__lowercase   )[0]

								# call the callback, if provided
								progress_bar.update()
								if callback is not None and i % callback_steps == 0:
									callback(__lowercase					,			__lowercase					,			__lowercase   )

        # 6. Post-process image sample
						lowerCamelCase_				:						List[Any]				  =				self.postprocess_image(__lowercase					,			output_type=__lowercase   )

						# Offload last model to CPU
						if hasattr(self					,			'final_offload_hook'   ) and self.final_offload_hook is not None:
							self.final_offload_hook.offload()

						if not return_dict:
							return (image,)

						return ImagePipelineOutput(images=__lowercase   )








"""simple docstring"""





from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
UpperCamelCase_						:					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      __lowercase       (     a			:					int			)       ->       list[int]:
      return [int(a			) for c in f'''{ruleset:08}'''[:8]]



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



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


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


