






from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
A_			:List[str]	    =		[[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	(			a_   )	->		list[int]:
					return [int(a_   ) for c in F'{ruleset:08}'[:8]]






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






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


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



import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


_UpperCAmelCase   :					str    =			100_000_000_000
_UpperCAmelCase   :					Optional[int]    =			10_000

_UpperCAmelCase  ,_UpperCAmelCase   :					Optional[int]    =			os.path.split(__file__)
_UpperCAmelCase   :					Optional[int]    =			os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))


def  A			(   lowercase       ,			lowercase					)  ->    datasets.Dataset:
		'''simple docstring'''

		UpperCamelCase            =	pa.Table.from_pydict({'col': [0] * chunk_size}					)
		UpperCamelCase            =	pa.concat_tables([table] * (num_examples // chunk_size)					)
		return datasets.Dataset(lowercase       ,			fingerprint='table_100B'					)


@dataclass
class 							lowercase   :
		__lowercase			:			int
		__lowercase			:			int
		__lowercase			:			int
		__lowercase			:			int



		def __UpperCamelCase					(					self			)    ->   Optional[int]:
				"""simple docstring"""
				UpperCamelCase            =	np.random.default_rng(self.seed			)
				UpperCamelCase            =	rng.integers(low=self.low							, high=self.high							, size=self.size			).tolist()



		def __iter__(					self			)    ->   List[str]:
				"""simple docstring"""
				return iter(self._sampled_values			)



		def __len__(					self			)    ->   int:
				"""simple docstring"""
				return self.size






@get_duration
def  A			(   lowercase					)  ->    List[Any]:
		'''simple docstring'''

		UpperCamelCase            =	dataset[0]


@get_duration
def  A			(   lowercase					)  ->    Any:
		'''simple docstring'''

		UpperCamelCase            =	dataset[-1]


@get_duration
def  A			(   lowercase					)  ->    List[Any]:
		'''simple docstring'''

		UpperCamelCase            =	dataset[range(len(lowercase					) // 2       ,			len(lowercase					) // 2 + 1_024					)]


@get_duration
def  A			(   lowercase					)  ->    Any:
		'''simple docstring'''

		UpperCamelCase            =	dataset[RandIter(0       ,			len(lowercase					)       ,			1_024       ,			seed=42					)]


def  A			(   )  ->    List[Any]:
		'''simple docstring'''

		UpperCamelCase            =	{'num examples': SPEED_TEST_N_EXAMPLES}
		UpperCamelCase            =	(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
		print('generating dataset'					)
		UpperCamelCase            =	generate_100B_dataset(num_examples=lowercase       ,			chunk_size=lowercase					)
		print('Functions'					)
		for func in functions:
				print(func.__name__					)
				UpperCamelCase            =	func(lowercase					)

		with open(lowercase       ,			'wb'					) as f:
				f.write(json.dumps(lowercase					).encode('utf-8'					)					)


if __name__ == "__main__":  # useful to run the profiler
					benchmark_table_100B()
