code: 
  
  
  '''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			:		Optional[Any] =			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			:		Any =			parser.parse_known_args()
  
  	# overwrite batch size until we have tf_glue.py
  	__lowercase			:		Optional[Any] =			16
  	__lowercase			:		int =			16
  
  	# Set up logging
  	__lowercase			:		Optional[Any] =			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			:		List[Any] =			TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
  	__lowercase			:		Dict =			AutoTokenizer.from_pretrained(args.model_name_or_path)
  
  	# Load dataset
  	__lowercase ,   __lowercase			:		List[str] =			load_dataset('imdb', split=['train', 'test'])
  	__lowercase			:		int =			train_dataset.shuffle().select(range(50_00))  # smaller the size for train dataset to 5k
  	__lowercase			:		Union[str, Any] =			test_dataset.shuffle().select(range(5_00))  # smaller the size for test dataset to 500
  
  	# Preprocess train dataset
  	__lowercase			:		Any =			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			:		List[str] =			{
  	    x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
  	    for x in ['input_ids', 'attention_mask']
  	}
  	__lowercase			:		Any =			tf.data.Dataset.from_tensor_slices((train_features, train_dataset['label'])).batch(
  	    args.per_device_train_batch_size
  	)
  
  	# Preprocess test dataset
  	__lowercase			:		Optional[Any] =			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			:		Any =			{
  	    x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
  	    for x in ['input_ids', 'attention_mask']
  	}
  	__lowercase			:		str =			tf.data.Dataset.from_tensor_slices((test_features, test_dataset['label'])).batch(
  	    args.per_device_eval_batch_size
  	)
  
  	# fine optimizer and loss
  	__lowercase			:		str =			tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
  	__lowercase			:		str =			tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
  	__lowercase			:		str =			[tf.keras.metrics.SparseCategoricalAccuracy()]
  	model.compile(optimizer=optimizer, loss=loss, metrics=metrics)
  
  	__lowercase			:		Union[str, Any] =			time.time()
  	__lowercase			:		Any =			model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
  	__lowercase			:		str =			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}''')
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     set		,   _SCREAMING_SNAKE_CASE   :     set     ):
  					assert isinstance(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     ), F"""The input value of [set_a={set_a}] is not a set"""
  					assert isinstance(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     ), F"""The input value of [set_b={set_b}] is not a set"""
  
  					return {a + b for a in set_a for b in set_b}
  
  
  if __name__ == "__main__":
  	from doctest import testmod
  
  	testmod()
  
  
  
  