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__":
  					__magic_name__	:   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=1_6)
  					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)
  
  					__magic_name__	:   Union[str, Any]          =							parser.parse_known_args()
  
  					# overwrite batch size until we have tf_glue.py
  					__magic_name__	:   str          =							1_6
  					__magic_name__	:   List[Any]          =							1_6
  
  					# Set up logging
  					__magic_name__	:   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
  					__magic_name__	:   Tuple          =							TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
  					__magic_name__	:   Optional[Any]          =							AutoTokenizer.from_pretrained(args.model_name_or_path)
  
  					# Load dataset
  					__magic_name__	:   List[Any]          =							load_dataset('imdb', split=['train', 'test'])
  					__magic_name__	:   Tuple          =							train_dataset.shuffle().select(range(5_0_0_0))  # smaller the size for train dataset to 5k
  					__magic_name__	:   List[str]          =							test_dataset.shuffle().select(range(5_0_0))  # smaller the size for test dataset to 500
  
  					# Preprocess train dataset
  					__magic_name__	:   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'])
  
  					__magic_name__	:   List[Any]          =							{
  					    x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
  					    for x in ['input_ids', 'attention_mask']
  					}
  					__magic_name__	:   Optional[int]          =							tf.data.Dataset.from_tensor_slices((train_features, train_dataset['label'])).batch(
  					    args.per_device_train_batch_size
  					)
  
  					# Preprocess test dataset
  					__magic_name__	:   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'])
  
  					__magic_name__	:   Optional[int]          =							{
  					    x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
  					    for x in ['input_ids', 'attention_mask']
  					}
  					__magic_name__	:   str          =							tf.data.Dataset.from_tensor_slices((test_features, test_dataset['label'])).batch(
  					    args.per_device_eval_batch_size
  					)
  
  					# fine optimizer and loss
  					__magic_name__	:   List[Any]          =							tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
  					__magic_name__	:   List[Any]          =							tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
  					__magic_name__	:   Optional[int]          =							[tf.keras.metrics.SparseCategoricalAccuracy()]
  					model.compile(optimizer=optimizer, loss=loss, metrics=metrics)
  
  					__magic_name__	:   Dict          =							time.time()
  					__magic_name__	:   Dict          =							model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
  					__magic_name__	:   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
  
  import re
  
  
  
  
  
  def 			a_   (  lowercase__   :list[str]    ):
  
  			def alphanum_key(lowercase__   :int    ):
  						return [int(lowercase__    ) if s.isdigit() else s.lower() for s in re.split("""([0-9]+)""", lowercase__    )]
  
  			return sorted(lowercase__, key=lowercase__    )
  
  
  if __name__ == "__main__":
  					import doctest
  
  					doctest.testmod()
  
  
  
  
  