code: import argparse
  import logging
  import os
  import sys
  import time
  
  import tensorflow as tf
  from datasets import load_dataset
  from tqdm import tqdm
  
  from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
  from transformers.utils import is_sagemaker_dp_enabled
  
  
  if os.environ.get('SDP_ENABLED') or is_sagemaker_dp_enabled():
  				A_:      int        =			True
  				A_:      Dict        =			'p3dn.24xlarge'
  				import smdistributed.dataparallel.tensorflow as sdp
  else:
  				A_:      Union[str, Any]        =			False
  
  
  def 					__lowerCAmelCase			(  _A ,_A ,_A ,_A ,_A ,_A ,_A=None					):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	tqdm(_A					)
  							for i, batch in enumerate(_A					):
  														with tf.GradientTape() as tape:
  																					_lowercase				,       _lowercase				=	batch
  																					_lowercase				=	model(_A					)
  																					_lowercase				=	loss(_A ,outputs.logits					)
  
  														if SDP_ENABLED:
  																					_lowercase				=	sdp.DistributedGradientTape(_A ,sparse_as_dense=_A					)
  
  														_lowercase				=	tape.gradient(_A ,model.trainable_variables					)
  														opt.apply_gradients(zip(_A ,model.trainable_variables					)					)
  
  														pbar.set_description(f'''Loss: {loss_value:.4f}'''					)
  
  														if SDP_ENABLED and i == 0:
  																					sdp.broadcast_variables(model.variables ,root_rank=0					)
  																					sdp.broadcast_variables(opt.variables() ,root_rank=0					)
  
  														if max_steps and i >= max_steps:
  																					break
  
  							_lowercase				=	{"""loss""": loss_value.numpy()}
  							return train_results
  
  
  def 					__lowerCAmelCase			(  _A ,_A ,_A					):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				,       _lowercase				=	load_dataset("""imdb""" ,split=["""train""", """test"""]					)
  
  							# Preprocess train dataset
  							_lowercase				=	train_dataset.map(
  							    lambda _A					: tokenizer(e["""text"""] ,truncation=_A ,padding="""max_length"""					) ,batched=_A					)
  							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"""])					)
  
  							# Preprocess test dataset
  							_lowercase				=	test_dataset.map(
  							    lambda _A					: tokenizer(e["""text"""] ,truncation=_A ,padding="""max_length"""					) ,batched=_A					)
  							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"""])					)
  
  							if SDP_ENABLED:
  														_lowercase				=	tf_train_dataset.shard(sdp.size() ,sdp.rank()					)
  														_lowercase				=	tf_test_dataset.shard(sdp.size() ,sdp.rank()					)
  							_lowercase				=	tf_train_dataset.batch(_A ,drop_remainder=_A					)
  							_lowercase				=	tf_test_dataset.batch(_A ,drop_remainder=_A					)
  
  							return tf_train_dataset, tf_test_dataset
  
  
  if __name__ == "__main__":
  				A_:      Any        =			argparse.ArgumentParser()
  
  				# Hyperparameters sent by the client are passed as command-line arguments to the script.
  				parser.add_argument('--epochs', type=int, default=3)
  				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)
  				parser.add_argument('--max_steps', type=int, default=None)
  
  				# Data, model, and output directories
  				parser.add_argument('--output_data_dir', type=str, default=os.environ['SM_OUTPUT_DATA_DIR'])
  				parser.add_argument('--model_dir', type=str, default=os.environ['SM_MODEL_DIR'])
  				parser.add_argument('--n_gpus', type=str, default=os.environ['SM_NUM_GPUS'])
  
  				A_   ,							A_:      Tuple        =			parser.parse_known_args()
  
  				# Set up logging
  				A_:      Tuple        =			logging.getLogger(__name__)
  
  				logging.basicConfig(
  				    level=logging.getLevelName('INFO'),
  				    handlers=[logging.StreamHandler(sys.stdout)],
  				    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
  				)
  
  				if SDP_ENABLED:
  								sdp.init()
  
  								A_:      Union[str, Any]        =			tf.config.experimental.list_physical_devices('GPU')
  								for gpu in gpus:
  												tf.config.experimental.set_memory_growth(gpu, True)
  								if gpus:
  												tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], 'GPU')
  
      # Load model and tokenizer
  				A_:      Optional[Any]        =			TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
  				A_:      List[Any]        =			AutoTokenizer.from_pretrained(args.model_name_or_path)
  
  				# get datasets
  				A_   ,							A_:      List[str]        =			get_datasets(
  				    tokenizer=tokenizer,
  				    train_batch_size=args.per_device_train_batch_size,
  				    eval_batch_size=args.per_device_eval_batch_size,
  				)
  
  				# fine optimizer and loss
  				A_:      Optional[Any]        =			tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
  				A_:      int        =			tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
  				A_:      str        =			[tf.keras.metrics.SparseCategoricalAccuracy()]
  				model.compile(optimizer=optimizer, loss=loss, metrics=metrics)
  
  				# Training
  				if args.do_train:
  								# train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size)
  								A_:      List[str]        =			time.time()
  								A_:      Union[str, Any]        =			fit(
  								    model,
  								    loss,
  								    optimizer,
  								    tf_train_dataset,
  								    args.epochs,
  								    args.per_device_train_batch_size,
  								    max_steps=args.max_steps,
  								)
  								A_:      Union[str, Any]        =			time.time() - start_train_time
  								logger.info('*** Train ***')
  								logger.info(F'''train_runtime = {end_train_time}''')
  
  								A_:      List[Any]        =			os.path.join(args.output_dir, 'train_results.txt')
  
  								if not SDP_ENABLED or sdp.rank() == 0:
  												with open(output_eval_file, 'w') as writer:
  																logger.info('***** Train results *****')
  																logger.info(train_results)
  																for key, value in train_results.items():
  																				logger.info(F'''  {key} = {value}''')
  																				writer.write(F'''{key} = {value}\n''')
  
      # Evaluation
  				if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0):
  								A_:      Dict        =			model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
  								logger.info('*** Evaluate ***')
  
  								A_:      List[str]        =			os.path.join(args.output_dir, 'eval_results.txt')
  
  								with open(output_eval_file, 'w') as writer:
  												logger.info('***** Eval results *****')
  												logger.info(result)
  												for key, value in result.items():
  																logger.info(F'''  {key} = {value}''')
  																writer.write(F'''{key} = {value}\n''')
  
      # Save result
  				if SDP_ENABLED:
  								if sdp.rank() == 0:
  												model.save_pretrained(args.output_dir)
  												tokenizer.save_pretrained(args.output_dir)
  				else:
  								model.save_pretrained(args.output_dir)
  								tokenizer.save_pretrained(args.output_dir)
  
  
  
style_context: def 					__lowerCAmelCase			(  _A = 998_001					):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	0
  							for i in range(999 ,99 ,-1					):  # 3 digit numbers range from 999 down to 100
  														for j in range(999 ,99 ,-1					):
  																					_lowercase				=	str(i * j					)
  																					if product_string == product_string[::-1] and i * j < n:
  																												_lowercase				=	max(_A ,i * j					)
  							return answer
  
  
  if __name__ == "__main__":
  				print(F'''{solution() = }''')
  
  
  