





from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
_SCREAMING_SNAKE_CASE   						=		[[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 				SCREAMING_SNAKE_CASE__    (     __a ):
	return [int(__a ) for c in f"""{ruleset:08}"""[:8]]


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ,			__a ):
	snake_case_		:      Any           =						len(cells[0] )  # 31
	snake_case_		:      Any           =						[]
	for i in range(__a ):
		# Get the neighbors of each cell
		# Handle neighbours outside bounds by using 0 as their value
		snake_case_		:      int           =						0 if i == 0 else cells[time][i - 1]
		snake_case_		:      str           =						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 				SCREAMING_SNAKE_CASE__    (     __a ):
	snake_case_		:      Tuple           =						Image.new('RGB' ,			(len(cells[0] ), len(__a )) )
	snake_case_		:      List[str]           =						img.load()
	# Generates image
	for w in range(img.width ):
		for h in range(img.height ):
			snake_case_		:      Optional[int]           =						2_55 - int(2_55 * cells[h][w] )
			snake_case_		:      int           =						(color, color, color)
	return img


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







import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
  import torch_xla.core.xla_model as xm



def     lowerCAmelCase		(		_lowerCAmelCase				: int    ,				_lowerCAmelCase				: bool = False						):




 """simple docstring"""







 if device_specific:
  seed += AcceleratorState().process_index
 random.seed(_lowerCAmelCase						)
 np.random.seed(_lowerCAmelCase						)
 torch.manual_seed(_lowerCAmelCase						)
 if is_xpu_available():
  torch.xpu.manual_seed_all(_lowerCAmelCase						)
 elif is_npu_available():
  torch.npu.manual_seed_all(_lowerCAmelCase						)
 else:
  torch.cuda.manual_seed_all(_lowerCAmelCase						)
 # ^^ safe to call this function even if cuda is not available
 if is_tpu_available():
  xm.set_rng_state(_lowerCAmelCase						)



def     lowerCAmelCase		(		_lowerCAmelCase				: Optional[RNGType] = None    ,				_lowerCAmelCase				: Optional[torch.Generator] = None						):




 """simple docstring"""







 if rng_type == RNGType.TORCH:
  UpperCAmelCase__							=    torch.get_rng_state()
 elif rng_type == RNGType.CUDA:
  UpperCAmelCase__							=    torch.cuda.get_rng_state()
 elif rng_type == RNGType.XLA:
  assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
  UpperCAmelCase__							=    torch.tensor(xm.get_rng_state()						)
 elif rng_type == RNGType.NPU:
  assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
  UpperCAmelCase__							=    torch.npu.get_rng_state()
 elif rng_type == RNGType.XPU:
  assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
  UpperCAmelCase__							=    torch.xpu.get_rng_state()
 elif rng_type == RNGType.GENERATOR:
  assert generator is not None, "Need a generator to synchronize its seed."
  UpperCAmelCase__							=    generator.get_state()

 # Broadcast the rng state from device 0 to other devices
 UpperCAmelCase__							=    AcceleratorState()
 if state.distributed_type == DistributedType.TPU:
  UpperCAmelCase__							=    rng_state.to(xm.xla_device()						)
  xm.collective_broadcast([rng_state]						)
  xm.mark_step()
  UpperCAmelCase__							=    rng_state.cpu()
 elif (
     state.distributed_type in CUDA_DISTRIBUTED_TYPES
     or state.distributed_type == DistributedType.MULTI_NPU
     or state.distributed_type == DistributedType.MULTI_XPU
 ):
  UpperCAmelCase__							=    rng_state.to(state.device						)
  torch.distributed.broadcast(_lowerCAmelCase    ,				0						)
  UpperCAmelCase__							=    rng_state.cpu()
 elif state.distributed_type == DistributedType.MULTI_CPU:
  torch.distributed.broadcast(_lowerCAmelCase    ,				0						)

 # Set the broadcast rng state
 if rng_type == RNGType.TORCH:
  torch.set_rng_state(_lowerCAmelCase						)
 elif rng_type == RNGType.CUDA:
  torch.cuda.set_rng_state(_lowerCAmelCase						)
 elif rng_type == RNGType.NPU:
  torch.npu.set_rng_state(_lowerCAmelCase						)
 elif rng_type == RNGType.XPU:
  torch.xpu.set_rng_state(_lowerCAmelCase						)
 elif rng_type == RNGType.XLA:
  xm.set_rng_state(rng_state.item()						)
 elif rng_type == RNGType.GENERATOR:
  generator.set_state(_lowerCAmelCase						)



def     lowerCAmelCase		(		_lowerCAmelCase				: List[Union[str, RNGType]]    ,				_lowerCAmelCase				: Optional[torch.Generator] = None						):




 """simple docstring"""







 for rng_type in rng_types:
  synchronize_rng_state(RNGType(_lowerCAmelCase						)    ,				generator=_lowerCAmelCase						)





