


import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin



def       lowerCAmelCase			(		lowerCAmelCase_=2			, lowerCAmelCase_=True			, lowerCAmelCase_=False			)->			Optional[Any]:
				lowerCAmelCase_			:   Optional[Any]     					=					Accelerator(step_scheduler_with_optimizer=lowerCAmelCase_			, split_batches=lowerCAmelCase_			)
				lowerCAmelCase_			:   Optional[int]     					=					torch.nn.Linear(2			, 4			)
				lowerCAmelCase_			:   Tuple     					=					torch.optim.AdamW(model.parameters()			, lr=1.0			)
				lowerCAmelCase_			:   Tuple     					=					torch.optim.lr_scheduler.OneCycleLR(lowerCAmelCase_			, max_lr=0.01			, steps_per_epoch=2			, epochs=1			)
				lowerCAmelCase_					,	lowerCAmelCase_					,	lowerCAmelCase_			:   Optional[Any]     					=					accelerator.prepare(lowerCAmelCase_			, lowerCAmelCase_			, lowerCAmelCase_			)

				# Optimizer has stepped
				scheduler.step()
				if step_scheduler_with_optimizer or (num_processes == 1):
								assert (
								    scheduler.scheduler.last_epoch == num_processes
								), f"""Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})"""
				else:
								assert (
								    scheduler.scheduler.last_epoch != num_processes
								), f"""Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})"""



def       lowerCAmelCase			(		lowerCAmelCase_=2			, lowerCAmelCase_=True			, lowerCAmelCase_=False			)->			List[Any]:
				lowerCAmelCase_			:   str     					=					Accelerator(step_scheduler_with_optimizer=lowerCAmelCase_			, split_batches=lowerCAmelCase_			)
				lowerCAmelCase_			:   int     					=					torch.nn.Linear(2			, 4			)
				lowerCAmelCase_			:   Optional[Any]     					=					torch.optim.AdamW(model.parameters()			, lr=1.0			)
				lowerCAmelCase_			:   Tuple     					=					torch.optim.lr_scheduler.LambdaLR(lowerCAmelCase_			, lr_lambda=lambda lowerCAmelCase_			: 1 - n / 10			)
				lowerCAmelCase_					,	lowerCAmelCase_					,	lowerCAmelCase_			:   Tuple     					=					accelerator.prepare(lowerCAmelCase_			, lowerCAmelCase_			, lowerCAmelCase_			)

				# Optimizer has stepped
				lowerCAmelCase_			:   int     					=					False
				scheduler.step()
				lowerCAmelCase_			:   Union[str, Any]     					=					1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
				assert (
				    scheduler.get_last_lr()[0] == expected_lr
				), f"""Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""

				# Optimizer has not stepped
				lowerCAmelCase_			:   int     					=					True
				scheduler.step()
				if not step_scheduler_with_optimizer:
								lowerCAmelCase_			:   Dict     					=					1 - 2 / 10
				assert (
				    scheduler.get_last_lr()[0] == expected_lr
				), f"""Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""



def       lowerCAmelCase			(		lowerCAmelCase_ = 2			)->			str:
				from transformers import get_linear_schedule_with_warmup

				lowerCAmelCase_			:   int     					=					[1, 2, 4]
				for num_steps in steps:
								lowerCAmelCase_			:   Dict     					=					GradientAccumulationPlugin(num_steps=lowerCAmelCase_			, adjust_scheduler=num_steps > 1			)
								lowerCAmelCase_			:   List[Any]     					=					Accelerator(gradient_accumulation_plugin=lowerCAmelCase_			)
								lowerCAmelCase_			:   Any     					=					torch.nn.Linear(2			, 4			)
								lowerCAmelCase_			:   Tuple     					=					torch.optim.AdamW(model.parameters()			, lr=10.0			)
								lowerCAmelCase_			:   Optional[Any]     					=					get_linear_schedule_with_warmup(optimizer=lowerCAmelCase_			, num_warmup_steps=0			, num_training_steps=20			)

								lowerCAmelCase_					,	lowerCAmelCase_					,	lowerCAmelCase_			:   Dict     					=					accelerator.prepare(lowerCAmelCase_			, lowerCAmelCase_			, lowerCAmelCase_			)

								for i in range(10 * num_steps			):
												with accelerator.accumulate(lowerCAmelCase_			):
																optimizer.step()
																scheduler.step()

												if i == (10 * num_steps - 2):
																assert (
																    scheduler.get_last_lr()[0] != 0
																), f"""Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}"""
								assert (
								    scheduler.get_last_lr()[0] == 0
								), f"""Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}"""
								GradientState._reset_state()







@require_cpu
class 					snake_case__(				unittest.TestCase				):







						'''simple docstring'''




						def     lowercase_			(					self				)       ->     List[str]:
										debug_launcher(partial(__lowercase ,    num_processes=1				) ,    num_processes=1				)
										debug_launcher(partial(__lowercase ,    num_processes=1 ,    split_batches=__lowercase				) ,    num_processes=1				)




						def     lowercase_			(					self				)       ->     List[str]:
										debug_launcher(partial(__lowercase ,    num_processes=1				) ,    num_processes=1				)
										debug_launcher(partial(__lowercase ,    num_processes=1 ,    split_batches=__lowercase				) ,    num_processes=1				)




						def     lowercase_			(					self				)       ->     Optional[int]:
										debug_launcher(partial(__lowercase ,    num_processes=1 ,    step_scheduler_with_optimizer=__lowercase				) ,    num_processes=1				)




						def     lowercase_			(					self				)       ->     str:
										debug_launcher(partial(__lowercase ,    num_processes=1 ,    step_scheduler_with_optimizer=__lowercase				) ,    num_processes=1				)




						def     lowercase_			(					self				)       ->     List[str]:
										AcceleratorState._reset_state(__lowercase				)
										debug_launcher(__lowercase				)
										debug_launcher(partial(__lowercase ,    num_processes=1 ,    split_batches=__lowercase				) ,    num_processes=1				)




						def     lowercase_			(					self				)       ->     List[str]:
										AcceleratorState._reset_state(__lowercase				)
										debug_launcher(__lowercase				)
										debug_launcher(partial(__lowercase ,    num_processes=1 ,    split_batches=__lowercase				) ,    num_processes=1				)




						def     lowercase_			(					self				)       ->     List[str]:
										AcceleratorState._reset_state(__lowercase				)
										debug_launcher(partial(__lowercase ,    step_scheduler_with_optimizer=__lowercase				)				)




						def     lowercase_			(					self				)       ->     str:
										AcceleratorState._reset_state(__lowercase				)
										debug_launcher(partial(__lowercase ,    step_scheduler_with_optimizer=__lowercase				)				)




						@require_huggingface_suite
						def     lowercase_			(					self				)       ->     Dict:
										AcceleratorState._reset_state(__lowercase				)
										debug_launcher(partial(__lowercase ,    num_processes=1				)				)
										debug_launcher(__lowercase				)



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       lowerCAmelCase			(		lowerCAmelCase_			)->			list[int]:
				return [int(lowerCAmelCase_			) for c in f"""{ruleset:08}"""[:8]]



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



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


if __name__ == "__main__":
						_UpperCAmelCase      :	str  =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      :	Dict  =generate_image(CELLS)
						# Uncomment to save the image
						# img.save(f"rule_{rule_num}.png")
						img.show()