"""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__":
				__lowercase    =				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)

				__lowercase   ,			__lowercase    =				parser.parse_known_args()

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

				# Set up logging
				__lowercase    =				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
				__lowercase    =				TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
				__lowercase    =				AutoTokenizer.from_pretrained(args.model_name_or_path)

				# Load dataset
				__lowercase   ,			__lowercase    =				load_dataset('''imdb''', split=['''train''', '''test'''])
				__lowercase    =				train_dataset.shuffle().select(range(5_000))  # smaller the size for train dataset to 5k
				__lowercase    =				test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

				# Preprocess train dataset
				__lowercase    =				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'''])

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

				# Preprocess test dataset
				__lowercase    =				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'''])

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

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

				__lowercase    =				time.time()
				__lowercase    =				model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
				__lowercase    =				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}''')




"""simple docstring"""

from collections import defaultdict


def    lowerCAmelCase			(__UpperCamelCase							:    int = 5			):
							"""simple docstring"""


							__UpperCamelCase							=defaultdict(__UpperCamelCase			)
							__UpperCamelCase							=0

							while True:
														__UpperCamelCase							=get_digits(__UpperCamelCase			)
														freqs[digits].append(__UpperCamelCase			)

														if len(freqs[digits]			) == max_base:
																					__UpperCamelCase							=freqs[digits][0] ** 3
																					return base

														num += 1


def    lowerCAmelCase			(__UpperCamelCase							:    int			):
							"""simple docstring"""


							return "".join(sorted(str(num**3			)			)			)


if __name__ == "__main__":
				print(f'''{solution() = }''')



