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__":
   lowerCamelCase							:     str								=			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)
  
   lowerCamelCase		,						lowerCamelCase							:     Union[str, Any]								=			parser.parse_known_args()
  
   # overwrite batch size until we have tf_glue.py
   lowerCamelCase							:     str								=			16
   lowerCamelCase							:     List[Any]								=			16
  
   # Set up logging
   lowerCamelCase							:     Dict								=			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
   lowerCamelCase							:     Tuple								=			TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
   lowerCamelCase							:     Optional[Any]								=			AutoTokenizer.from_pretrained(args.model_name_or_path)
  
   # Load dataset
   lowerCamelCase		,						lowerCamelCase							:     List[Any]								=			load_dataset('imdb', split=['train', 'test'])
   lowerCamelCase							:     Tuple								=			train_dataset.shuffle().select(range(5_000))  # smaller the size for train dataset to 5k
   lowerCamelCase							:     List[str]								=			test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500
  
   # Preprocess train dataset
   lowerCamelCase							:     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'])
  
   lowerCamelCase							:     List[Any]								=			{
       x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
       for x in ['input_ids', 'attention_mask']
   }
   lowerCamelCase							:     Optional[int]								=			tf.data.Dataset.from_tensor_slices((train_features, train_dataset['label'])).batch(
       args.per_device_train_batch_size
   )
  
   # Preprocess test dataset
   lowerCamelCase							:     List[str]								=			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'])
  
   lowerCamelCase							:     Optional[int]								=			{
       x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
       for x in ['input_ids', 'attention_mask']
   }
   lowerCamelCase							:     str								=			tf.data.Dataset.from_tensor_slices((test_features, test_dataset['label'])).batch(
       args.per_device_eval_batch_size
   )
  
   # fine optimizer and loss
   lowerCamelCase							:     List[Any]								=			tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
   lowerCamelCase							:     List[Any]								=			tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
   lowerCamelCase							:     Optional[int]								=			[tf.keras.metrics.SparseCategoricalAccuracy()]
   model.compile(optimizer=optimizer, loss=loss, metrics=metrics)
  
   lowerCamelCase							:     Dict								=			time.time()
   lowerCamelCase							:     Dict								=			model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
   lowerCamelCase							:     Optional[Any]								=			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'''
  
  from __future__ import annotations
  
  from collections import Counter
  
  def _SCREAMING_SNAKE_CASE   (A ) -> list[int]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				[True] * n
         lowercase__       =				False
         lowercase__       =				False
         lowercase__       =				True
  
         for i in range(3						,				int(n**0.5 + 1 )						,				2 ):
                lowercase__       =				i * 2
                while index < n:
                       lowercase__       =				False
                       lowercase__       =				index + i
  
         lowercase__       =				[2]
  
         for i in range(3						,				A						,				2 ):
                if is_prime[i]:
                       primes.append(A )
  
         return primes
  
  def _SCREAMING_SNAKE_CASE   (A ) -> list[list[int]]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				str(A )
         lowercase__       =				[]
         lowercase__       =				['''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''']
  
         for duplicate in Counter(A ) - Counter(set(A ) ):
                lowercase__       =				[int(number_str.replace(A						,				A ) ) for digit in digits]
                replacements.append(A )
  
         return replacements
  
  def _SCREAMING_SNAKE_CASE   (A = 8 ) -> int:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				set()
  
         # Filter primes with less than 3 replaceable digits
         lowercase__       =				{
             x for x in set(prime_sieve(1_000_000 ) ) if len(str(A ) ) - len(set(str(A ) ) ) >= 3
         }
  
         for prime in primes:
                if prime in numbers_checked:
                       continue
  
                lowercase__       =				digit_replacements(A )
  
                for family in replacements:
                       numbers_checked.update(A )
                       lowercase__       =				primes.intersection(A )
  
                       if len(A ) != family_length:
                              continue
  
                       return min(A )
  
         return -1
  
  
  if __name__ == "__main__":
   print(solution())
  
  
  
  
  