'''simple docstring'''




from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
_UpperCamelCase				:		Optional[Any]       			=		[[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 (       lowerCAmelCase       :     Optional[Any]			):
	return [int(__A			) for c in F"""{ruleset:08}"""[:8]]



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



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


if __name__ == "__main__":
					_UpperCamelCase				:		Union[str, 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				:		Optional[int]       			=		generate_image(CELLS)
					# Uncomment to save the image
					# img.save(f"rule_{rule_num}.png")
					img.show()




'''simple docstring'''




import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
     from .testing_utils import (
         floats_tensor,
         load_hf_numpy,
         load_image,
         load_numpy,
         load_pt,
         nightly,
         parse_flag_from_env,
         print_tensor_test,
         require_torch_a,
         require_torch_gpu,
         skip_mps,
         slow,
         torch_all_close,
         torch_device,
     )
     from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


_UpperCamelCase				:		List[Any]       			=		get_logger(__name__)



def 						__snake_case (       lowerCAmelCase       :     List[str]			):
 if version.parse(lowerCAmelCase			) < version.parse(lowerCAmelCase			):
  if "dev" in min_version:
   __UpperCAmelCase         =					(
       'This example requires a source install from HuggingFace diffusers (see '
       '`https://huggingface.co/docs/diffusers/installation#install-from-source`),'
   )
  else:
   __UpperCAmelCase         =					F"""This example requires a minimum version of {min_version},"""
  error_message += F""" but the version found is {__version__}.\n"""
  raise ImportError(lowerCAmelCase			)



