




'''simple docstring'''




from __future__ import annotations

from PIL import Image

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




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




def 						snake_case__    ( lowerCamelCase__				:   list[list[int]]			)   ->   Image.Image:
					A_ :     Dict =							Image.new('''RGB'''			,		(len(cells[0]			), len(lowerCamelCase__			))			)
					A_ :     Tuple =							img.load()
					# Generates image
					for w in range(img.width			):
										for h in range(img.height			):
															A_ :     List[Any] =							2_5_5 - int(2_5_5 * cells[h][w]			)
															A_ :     Optional[Any] =							(color, color, color)
					return img


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









'''simple docstring'''




import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


if __name__ == "__main__":
							snake_case__          =						argparse.ArgumentParser()

							# Hyperparameters sent by the client are passed as command-line arguments to the script.
							parser.add_argument("""--epochs""", type=int, default=1)
							parser.add_argument("""--per_device_train_batch_size""", type=int, default=16)
							parser.add_argument("""--per_device_eval_batch_size""", type=int, default=8)
							parser.add_argument("""--model_name_or_path""", type=str)
							parser.add_argument("""--learning_rate""", type=str, default=5e-5)
							parser.add_argument("""--do_train""", type=bool, default=True)
							parser.add_argument("""--do_eval""", type=bool, default=True)
							parser.add_argument("""--output_dir""", type=str)

							snake_case__       ,						snake_case__          =						parser.parse_known_args()

							# overwrite batch size until we have tf_glue.py
							snake_case__          =						16
							snake_case__          =						16

							# Set up logging
							snake_case__          =						logging.getLogger(__name__)

							logging.basicConfig(
							    level=logging.getLevelName("""INFO"""),
							    handlers=[logging.StreamHandler(sys.stdout)],
							    format="""%(asctime)s - %(name)s - %(levelname)s - %(message)s""",
							)

							# Load model and tokenizer
							snake_case__          =						TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
							snake_case__          =						AutoTokenizer.from_pretrained(args.model_name_or_path)

							# Load dataset
							snake_case__       ,						snake_case__          =						load_dataset("""imdb""", split=["""train""", """test"""])
							snake_case__          =						train_dataset.shuffle().select(range(50_00))  # smaller the size for train dataset to 5k
							snake_case__          =						test_dataset.shuffle().select(range(5_00))  # smaller the size for test dataset to 500

							# Preprocess train dataset
							snake_case__          =						train_dataset.map(
							    lambda e: tokenizer(e["""text"""], truncation=True, padding="""max_length"""), batched=True
							)
							train_dataset.set_format(type="""tensorflow""", columns=["""input_ids""", """attention_mask""", """label"""])

							snake_case__          =						{
							    x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
							    for x in ["""input_ids""", """attention_mask"""]
							}
							snake_case__          =						tf.data.Dataset.from_tensor_slices((train_features, train_dataset["""label"""])).batch(
							    args.per_device_train_batch_size
							)

							# Preprocess test dataset
							snake_case__          =						test_dataset.map(
							    lambda e: tokenizer(e["""text"""], truncation=True, padding="""max_length"""), batched=True
							)
							test_dataset.set_format(type="""tensorflow""", columns=["""input_ids""", """attention_mask""", """label"""])

							snake_case__          =						{
							    x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
							    for x in ["""input_ids""", """attention_mask"""]
							}
							snake_case__          =						tf.data.Dataset.from_tensor_slices((test_features, test_dataset["""label"""])).batch(
							    args.per_device_eval_batch_size
							)

							# fine optimizer and loss
							snake_case__          =						tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
							snake_case__          =						tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
							snake_case__          =						[tf.keras.metrics.SparseCategoricalAccuracy()]
							model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

							snake_case__          =						time.time()
							snake_case__          =						model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
							snake_case__          =						time.time() - start_train_time

							logger.info("""*** Train ***""")
							logger.info(F'train_runtime = {end_train_time}')
							for key, value in train_results.history.items():
														logger.info(F'  {key} = {value}')



